IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2020-02-20T06:16:21ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2015-03-02T09:41:51Z2015-03-02T09:41:51Zhttp://eprints.imtlucca.it/id/eprint/2624This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/26242015-03-02T09:41:51ZA convex feasibility approach to anytime model predictive controlThis paper proposes to decouple performance optimization and enforcement of asymptotic convergence in Model Predictive Control (MPC) so that convergence to a given terminal set is achieved independently of how much performance is optimized at each sampling step. By embedding an explicit decreasing condition in the MPC constraints and thanks to a novel and very easy-to-implement convex feasibility solver proposed in the paper, it is possible to run an outer performance optimization algorithm on top of the feasibility solver and optimize for an amount of time that depends on the available CPU resources within the current sampling step (possibly going open-loop at a given sampling step in the extreme case no resources are available) and still guarantee convergence to the terminal set. While the MPC setup and the solver proposed in the paper can deal with quite general classes of functions, we highlight the synthesis method and show numerical results in case of linear MPC and ellipsoidal and polyhedral terminal sets. Alberto Bemporadalberto.bemporad@imtlucca.itDaniele Bernardinidaniele.bernardini@imtlucca.itPanagiotis Patrinospanagiotis.patrinos@imtlucca.it2014-10-22T09:53:27Z2014-10-22T10:00:58Zhttp://eprints.imtlucca.it/id/eprint/2331This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/23312014-10-22T09:53:27ZStabilizing linear model predictive control under inexact numerical optimizationThis note describes a model predictive control (MPC) formulation for discrete-time linear systems with hard constraints on control and state variables, under the assumption that the solution of the associated quadratic program is neither optimal nor satisfies the inequality constraints. This is common in embedded control applications, for which real-time constraints and limited computing resources dictate restrictions on the possible number of on-line iterations that can be performed within a sampling period. The proposed approach is rather general, in that it does not refer to a particular optimization algorithm, and is based on the definition of an alternative MPC problem that we assume can only be solved within bounded levels of suboptimality, and violation of the inequality constraints. By showing that the inexact solution is a feasible suboptimal one for the original problem, asymptotic or exponential stability is guaranteed for the closed-loop system. Based on the above general results, we focus on a specific dual accelerated gradient-projection method to obtain a stabilizing MPC law that only requires a predetermined maximum number of on-line iterations.Matteo RubagottiPanagiotis Patrinospanagiotis.patrinos@imtlucca.itAlberto Bemporadalberto.bemporad@imtlucca.it2014-10-22T09:15:26Z2014-10-22T09:15:26Zhttp://eprints.imtlucca.it/id/eprint/2330This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/23302014-10-22T09:15:26ZCabin heat thermal management in hybrid vehicles using model predictive controlThis paper describes a Model Predictive Control (MPC) design for the thermal management of cabin heat in Hybrid Electric Vehicles (HEVs). Due to the augmented complexity of the energy flow in recent energy-efficient vehicles in comparison to conventional vehicles, control degrees of freedom are increased, as many components can achieve the same functionality of heating up the cabin temperature. This paper proposes an MPC strategy to distribute the workload between available components in the vehicle, while achieving multiple objectives, such as fuel efficiency and heat-power reference tracking, and enforcing various constraints. First, a simplified linear dynamical model subject to linear time-varying (LTV) constraints is identified, based on high-fidelity simulations on a full nonlinear model. Then an MPC controller is designed to achieve multiple control objectives by manipulating different inputs. Simulation results indicate that the proposed approach is suitable for such multi-objective automotive control problems.Hasan EsenTsutomu TashiroDaniele Bernardinidaniele.bernardini@imtlucca.itAlberto Bemporadalberto.bemporad@imtlucca.it2014-10-22T08:30:45Z2014-10-22T08:30:45Zhttp://eprints.imtlucca.it/id/eprint/2329This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/23292014-10-22T08:30:45ZMPC for power systems dispatch based on stochastic optimizationIn this paper we investigate the problem of optimal real-time power dispatch of an interconnection of conventional power generation plants, renewable resources and energy storage systems. The objective of the problem is to minimize imbalance costs and maximize the profit of the company managing the system whilst satisfying user demand. The managing company is able to trade energy on an electricity market. Energy prices on the market, user demand and intermittent generation from the renewable plants are considered stochastic processes. We show that under certain assumptions, the stochastic power dispatch problem over a finite horizon can be recast, under a proper choice for the feedback policies and for the disturbance set, into a stochastic optimization formulation but with deterministic constraints. We carry out a systematic study of stochastic optimization methods to solve this problem, in particular we analyze the stochastic gradient method. We also show that this problem can be approximated by a proper deterministic optimization problem using the sample average approximation method, which can then be solved by standard means.Ion NecoaraDragos Nicolae ClipiciPanagiotis Patrinospanagiotis.patrinos@imtlucca.itAlberto Bemporadalberto.bemporad@imtlucca.it2014-10-22T07:55:49Z2015-04-07T14:04:19Zhttp://eprints.imtlucca.it/id/eprint/2328This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/23282014-10-22T07:55:49ZFixed-point implementation of a proximal Newton method for embedded model predictive control (I)Extending the success of model predictive control (MPC) technologies in embedded applications heavily depends on the capability of improving quadratic programming (QP) solvers. Improvements can be done in two directions: better algorithms that reduce the number of arithmetic operations required to compute a solution, and more efficient architectures in terms of speed, power consumption, memory occupancy and cost. This paper proposes a fixed point implementation of a proximal Newton method to solve optimization problems arising in input-constrained MPC. The main advantages of the algorithm are its fast asymptotic convergence rate and its relatively low computational cost per iteration since it the solution of a small linear system is required. A detailed analysis on the effects of quantization errors is presented, showing the robustness of the algorithm with respect to finite-precision computations. A hardware implementation with specific optimizations to minimize computation times and memory footprint is also described, demonstrating the viability of low-cost, low-power controllers for high-bandwidth MPC applications. The algorithm is shown to be very effective for embedded MPC applications through a number of simulation experiments. Alberto Guiggianialberto.guiggiani@imtlucca.itPanagiotis Patrinospanagiotis.patrinos@imtlucca.itAlberto Bemporadalberto.bemporad@imtlucca.it2014-09-19T07:24:07Z2014-11-17T13:01:55Zhttp://eprints.imtlucca.it/id/eprint/2283This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/22832014-09-19T07:24:07ZStochastic model predictive control for constrained discrete-time Markovian switching systems In this paper we study constrained stochastic optimal control problems for Markovian switching systems, an extension of Markovian jump linear systems (MJLS), where the subsystems are allowed to be nonlinear. We develop appropriate notions of invariance and stability for such systems and provide terminal conditions for stochastic model predictive control (SMPC) that guarantee mean-square stability and robust constraint fulfillment of the Markovian switching system in closed-loop with the {SMPC} law under very weak assumptions. In the special but important case of constrained {MJLS} we present an algorithm for computing explicitly the {SMPC} control law off-line, that combines dynamic programming with parametric piecewise quadratic optimization. Panagiotis Patrinospanagiotis.patrinos@imtlucca.itPantelis Sopasakispantelis.sopasakis@imtlucca.itHaralambos SarimveisAlberto Bemporadalberto.bemporad@imtlucca.it2014-07-16T12:09:17Z2014-12-03T13:05:43Zhttp://eprints.imtlucca.it/id/eprint/2260This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/22602014-07-16T12:09:17ZStabilizing dynamic controllers for hybrid systems: a hybrid control Lyapunov function approach
This paper proposes a dynamic controller structure and a systematic design procedure for stabilizing discrete-time hybrid systems. The proposed approach is based on the concept of control Lyapunov functions (CLFs), which, when available, can be used to design a stabilizing state-feedback control law. In general, the construction of a CLF for hybrid dynamical systems involving both continuous and discrete states is extremely complicated, especially in the presence of non-trivial discrete dynamics. Therefore, we introduce the novel concept of a hybrid control Lyapunov function, which allows the compositional design of a discrete and a continuous part of the CLF, and we formally prove that the existence of a hybrid CLF guarantees the existence of a classical CLF. A constructive procedure is provided to synthesize a hybrid CLF, by expanding the dynamics of the hybrid system with a specific controller dynamics. We show that this synthesis procedure leads to a dynamic controller that can be implemented by a receding horizon control strategy, and that the associated optimization problem is numerically tractable for a fairly general class of hybrid systems, useful in real world applications. Compared to classical hybrid receding horizon control algorithms, the proposed approach typically requires a shorter prediction horizon to guarantee asymptotic stability of the closed-loop system, which yields a reduction of the computational burden, as illustrated through two examples.Stefano Di CairanoW.P.M.H. HeemelsMircea LazarAlberto Bemporadalberto.bemporad@imtlucca.it2014-07-09T14:51:32Z2014-07-09T14:51:32Zhttp://eprints.imtlucca.it/id/eprint/2259This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/22592014-07-09T14:51:32ZModel-varying predictive control of a nonlinear systemModel Predictive Control (MPC) can be used for nonlinear systems if they are working around an operating point. If the operating point is moved away from the nominal working point the controller is less effective due to model mismatch. This situation can be tackled by using a Model-Varying Predictive Controller (MVPC), which changes its internal model, switching among a set of liner models, according to the working point.C. PedretK. StadlerA. TollerA.H. GlattfelderAlberto Bemporadalberto.bemporad@imtlucca.itDomenico Mignone2014-07-09T14:35:09Z2014-07-09T14:47:41Zhttp://eprints.imtlucca.it/id/eprint/2257This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/22572014-07-09T14:35:09ZHYSDEL 2.0. 5- User manualThis is the HYSDEL user guide. HYSDEL allows to model a class of hybrid systems described by interconnections of linear dynamic systems, automata, if-then-else and propositional logic rules. For this class of systems we present general techniques for transforming an abstract
representation into a set of constrained linear difference equations involving integer and continuous variables. The resulting model can be immediately used for optimization, to
solve, e.g., optimal control problems or as an intermediate step to obtain other popular representations such as piecewise a±ne systems.
The developer's manual [39] completes the present document with the details on the implementation of HYSDEL and is included in the source distribution.Fabio Danilo TorrisiAlberto Bemporadalberto.bemporad@imtlucca.itGioele BertiniPeter HertachDominic JostDomenico Mignone2014-07-09T14:20:39Z2014-07-09T14:20:39Zhttp://eprints.imtlucca.it/id/eprint/2256This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/22562014-07-09T14:20:39ZOptimal control of discrete time linear hybrid systemsIn this paper we study the solution to optimal control problems for constrained discrete-time linear hybrid systems based on quadratic or linear performance criteria. The aim of the paper it twofold. First we give basic theoretical results on the structure of the optimal state feedback solution and of the value function. Second we describe how the state feedback optimal control law can be efficiently constructed by combining multiparametric programming and dynamic programming. Alberto Bemporadalberto.bemporad@imtlucca.itFrancesco BorrelliManfred Morari2014-07-09T14:05:30Z2014-07-09T14:05:30Zhttp://eprints.imtlucca.it/id/eprint/2254This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/22542014-07-09T14:05:30ZUsing MPC as master controller for integrated gasification combined cycle processesThe aim of this work is the design of a master controller for IGCC (Integrated Gasification Combine Cycle) plant, based on an MPC (Model Predictive Control) approach, which is able to coordinate the main process variables interacting with the basic structure of standard controllers at the unit level. Generally, a master controller is obtained by conventional loops based on a “pressure driven” configuration. In the following , the MPC library for MATLAB, by Bemporad, Morari and Ricker (2000) [1] has been applied to a detailed IGCC plant stimulation tool in order to understand the performance of a reliable multivariable linear MPC when adopted for such a nonlinear complex process with crucial targets. A detailed first principle model has been used as a “real plant” when performing the step tests for the identification of the simplified linear model and when checking the reliability of the control tool. Moreover, the effectiveness of the designed controller has been proved through the comparison between the linear MPC approach and an ideal solution (“direct” approach) obtained by the direct inversion of the DAE model, where perfect setpoint tracking is imposed by additional constraint equations and using the corresponding manipulated variables as closing variables. Moreover the performance of the derived MPC controller, when compared with a more conventional control configuration , shows a significant reduction of the overshoots and settling time when the plant is subject to load variations. The paper clearly shows how the MPS approach for a master controller is reliable, easy to design and of real value for practical purposes.Alberto Bemporadalberto.bemporad@imtlucca.itF. RusconiManfred MorariM. Rovaglio2014-07-08T14:21:55Z2014-07-08T14:21:55Zhttp://eprints.imtlucca.it/id/eprint/2253This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/22532014-07-08T14:21:55ZStability of hybrid model predictive controlIn this paper we investigate the stability of hybrid systems in closed-loop with Model Predictive
Controllers (MPC) and we derive a priori sufficient conditions for Lyapunov asymptotic stability and
exponential stability. A general theory is presented which proves that Lyapunov stability is achieved for
both terminal cost and constraint set and terminal equality constraint hybrid MPC, even though the
considered Lyapunov function and the system dynamics may be discontinuous. For particular choices
of MPC criteria and constrained Piecewise Affine (PWA) systems as the prediction models we develop
novel algorithms for computing the terminal cost and the terminal constraint set. For a quadratic MPC
cost, the stabilization conditions translate into a linear matrix inequality while, for an 1-norm based
MPC cost, they are obtained as 1-norm inequalities. It is shown that by using 1-norms, the terminal
constraint set is automatically obtained as a polyhedron or a finite union of polyhedra by taking a
sublevel set of the calculated terminal cost function. New algorithms are developed for calculating
polyhedral or piecewise polyhedral positively invariant sets for PWA systems. In this manner, the on-line
optimization problem leads to a mixed integer quadratic programming problem or to a mixed integer
linear programming problem, which can be solved by standard optimization tools. Several examples
illustrate the effectiveness of the developed methodology.Mircea LazarW.P.M.H. HeemelsSiep WeilandAlberto Bemporadalberto.bemporad@imtlucca.it2014-07-08T13:48:09Z2014-07-08T13:48:09Zhttp://eprints.imtlucca.it/id/eprint/2252This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/22522014-07-08T13:48:09ZNon-smooth model predictive control: stability and applications to hybrid systems In this report we investigate the stability of hybrid systems in closed-loop with Model Predictive Controllers (MPC) and we derive a priori sufficient conditions for Lyapunov asymptotic stability and exponential stability. A general theory is presented which proves that Lyapunov stability is achieved for both terminal cost and constraint set and terminal equality constraint hybrid MPC, even though the considered Lyapunov function and the system dynamics may be discontinuous. For particular choices of MPC criteria and constrained Piecewise Affine (PWA) systems as the prediction models we develop novel algorithms for computing the terminal cost and the terminal constraint set. For a quadratic MPC cost, the stabilization conditions translate into a linear matrix inequality while, for an ∞-norm based MPC cost, they are obtained as ∞-norm inequalities. It is shown that by using ∞-norms, the terminal constraint set is automatically obtained as a polyhedron or a finite union of polyhedra by taking a sublevel set of the calculated terminal cost function. New algorithms are developed for calculating polyhedral or piecewise polyhedral positively invariant sets for PWA systems. In this manner, the on-line optimization problem leads to a mixed integer quadratic programming problem or to a mixed integer linear programming problem, which can be solved by standard optimization tools. Several examples illustrate the effectiveness of the developed methodology.Mircea LazarW.P.M.H. HeemelsSiep WeilandAlberto Bemporadalberto.bemporad@imtlucca.it2014-07-01T12:18:48Z2014-07-01T12:18:48Zhttp://eprints.imtlucca.it/id/eprint/2227This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/22272014-07-01T12:18:48ZModel predictive traction and steering control of planetary roversResults of the ESA project RobMPC (Robust Model Predictive Control for Space Constraint Systems) could successfully demonstrate that model predictive control (MPC) is definitively applicable for space systems with high dynamics like wheeled vehicles exploring a planetary surface. In the context of RobMPC a rover control hierarchy for guidance, trajectory control as well as traction and steering control was implemented. Controller verifications and robustness tests were performed using a functional engineering simulator (FES) including a multi-body dynamics model of ESA’s EGP rover and the vehicle-terrain contact physics. The latest validation step is the MPC implementation on a real-time computer system controlling the ExoMars breadboard rover at DLR’s planetary exploration lab.Rainer KrennAndreas GibbeschGiovanni BinetAlberto Bemporadalberto.bemporad@imtlucca.it2014-06-16T12:58:57Z2016-04-06T10:33:26Zhttp://eprints.imtlucca.it/id/eprint/2206This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/22062014-06-16T12:58:57ZModel predictive control application to spacecraft rendezvous in mars sample return scenarioModel Predictive Control (MPC) is an optimization-based control strategy that is considered extremely attractive in the autonomous space rendezvous scenarios. The Online Recon¦guration Control System and Avionics Architecture (ORCSAT) study addresses its applicability in Mars Sample Return (MSR) mission, including the implementation of the developed solution in a space representative avionic architecture system. With respect to a classical control solution High-integrity Autonomous RendezVous and Docking control system (HARVD), MPC allows a signi¦cant performance improvement both in trajectory and in propellant save. Furthermore, thanks to the online optimization, it allows to identify improvements in other areas (i. e., at mission de¦nition level) that could not be known a prioriM. SaponaraV. BarrenaAlberto Bemporadalberto.bemporad@imtlucca.itE. N. HartleyJan M. MaciejowskiA. RichardsA. TramutolaP. Trodden2013-11-08T10:18:45Z2013-11-08T10:18:45Zhttp://eprints.imtlucca.it/id/eprint/1895This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/18952013-11-08T10:18:45ZIntegrating the electric grid and the commuter network through a 'Veichle to Grid' concept: a Complex Networks Theory approachAlfonso DamianoGuido Caldarelliguido.caldarelli@imtlucca.itAlessandro Chessaalessandro.chessa@imtlucca.itAntonio Scala2013-08-05T08:53:53Z2014-07-01T12:39:39Zhttp://eprints.imtlucca.it/id/eprint/1653This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/16532013-08-05T08:53:53ZStochastic MPC with learning for driver-predictive vehicle control and its application to HEV energy managementThis paper develops an approach for driver-aware vehicle control based on stochastic model predictive control with learning (SMPCL). The framework combines the on-board learning of a Markov chain that represents the driver behavior, a scenario-based approach for stochastic optimization, and quadratic programming. By using quadratic programming, SMPCL can handle, in general, larger state dimension models than stochastic dynamic programming, and can reconfigure in real-time for accommodating changes in driver behavior. The SMPCL approach is demonstrated in the energy management of a series hybrid electrical vehicle, aimed at improving fuel efficiency while enforcing constraints on battery state of charge and power. The SMPCL controller allocates the power from the battery and the engine to meet the driver power request. A Markov chain that models the power request dynamics is learned in real-time to improve the prediction capabilities of model predictive control (MPC). Because of exploiting the learned pattern of the driver behavior, the proposed approach outperforms conventional model predictive control and shows performance close to MPC with full knowledge of future driver power request in standard and real-world driving cycles.Stefano Di CairanoDaniele Bernardinidaniele.bernardini@imtlucca.itAlberto Bemporadalberto.bemporad@imtlucca.itIlya Kolmanovsky2012-07-03T09:04:30Z2016-04-06T10:26:52Zhttp://eprints.imtlucca.it/id/eprint/1307This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/13072012-07-03T09:04:30ZVehicle yaw stability control by coordinated active front steering and differential braking in the tire sideslip angles domain Vehicle active safety receives ever increasing attention in the attempt to achieve zero accidents on the road. In this paper, we investigate a control architecture that has the potential of improving yaw stability control by achieving faster convergence and reduced impact on the longitudinal dynamics. We consider a system where active front steering and differential braking are available and propose a model predictive control (MPC) strategy to coordinate the actuators. We formulate the vehicle dynamics with respect to the tire slip angles and use a piecewise affine (PWA) approximation of the tire force characteristics. The resulting PWA system is used as prediction model in a hybrid MPC strategy. After assessing the benefits of the proposed approach, we synthesize the controller by using a switched MPC strategy, where the tire conditions (linear/saturated) are assumed not to change during the prediction horizon. The assessment of the controller computational load and memory requirements indicates that it is capable of real-time execution in automotive-grade electronic control units. Experimental tests in different maneuvers executed on low-friction surfaces demonstrate the high performance of the controller.Stefano Di CairanoH. E. TsengDaniele Bernardinidaniele.bernardini@imtlucca.itAlberto Bemporadalberto.bemporad@imtlucca.it2012-04-04T08:41:10Z2012-04-04T08:41:10Zhttp://eprints.imtlucca.it/id/eprint/1254This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/12542012-04-04T08:41:10ZDecentralized linear time-varying model predictive control of a formation of unmanned aerial vehiclesThis paper proposes a hierarchical MPC approach to stabilization and autonomous navigation of a formation of unmanned aerial vehicles (UAVs), under constraints on motor thrusts, angles and positions, and under collision avoidance constraints. Each vehicle is of quadcopter type and is stabilized by a local linear time-invariant (LTI) MPC controller at the lower level of the control hierarchy around commanded desired set-points. These are generated at the higher level and at a slower sampling rate by a linear time-varying (LTV) MPC controller per vehicle, based on an a simplified dynamical model of the stabilized UAV and a novel algorithm for convex under-approximation of the feasible space. Formation flying is obtained by running the above decentralized scheme in accordance with a leader-follower approach. The performance of the hierarchical control scheme is assessed through simulations, and compared to previous work in which a hybrid MPC scheme is used for planning paths on-line.Alberto Bemporadalberto.bemporad@imtlucca.itClaudio Rocchi2012-03-06T12:06:46Z2013-09-30T12:27:36Zhttp://eprints.imtlucca.it/id/eprint/1216This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/12162012-03-06T12:06:46ZA nonlinear model predictive control scheme with multirate integral sliding modeIn this paper, a hierarchical multirate control scheme for nonlinear discrete-time systems is proposed, composed of a robust model predictive controller (MPC) and a multirate integral sliding mode (MISM) controller. In particular, the MISM controller acts at a faster sampling time than the MPC controller, and reduces the effect of model uncertainties and external disturbances, in order to obtain, at the next sampling instant of the MPC controller, a value of the system state that is as close as possible to the nominal one. To obtain this result, the control variable is composed of two parts: one generated by the MPC controller, and the other by the MISM controller. The a-priori reduction of the disturbance terms turns out to be very useful in order to improve the convergence properties of the MPC controller.Matteo RubagottiDavide Martino RaimondoColin Neil JonesLalo MagniAntonella FerraraManfred Morari2012-03-06T11:57:29Z2013-09-30T12:29:22Zhttp://eprints.imtlucca.it/id/eprint/1214This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/12142012-03-06T11:57:29ZA smart embedded control unit for electro-hydraulic aircraft actuatorsThe design and experimental characterization of a novel electronic control unit for fly-by-wire aircraft actuators are presented. Thanks to a carefully-engineered compact volume, it is embedded in the actuator body, providing local closed-loop control. Various digital control laws can be implemented in the easily reconfigurable digital platform. After system modeling and tuning, a variable-gain controller has been selected. Beyond reduction of weight (900g), power dissipation (7W) and cabling connections (required only for power supply and digital communication bus), the advantages provided by this smart unit are both in terms of performance and safety improvement. In fact, fast (1Hz) and precise (1mm resolution, 2mm accuracy, no overshoot) closed-loop linear displacement regulation is here demonstrated. Furthermore, this robust and rugged unit is endowed with self-diagnostic capabilities: fault detection of all critical electronic and electro/mechanical sections is implemented by dedicated analog circuits. It is suitable for fully-electrical and hybrid electro-hydraulic actuators. Extensive thermal characterization has confirmed the fulfillment of specifications over the whole -40 C° ÷ +70 C° operating range.Marco CarminatiMatteo RubagottiRiccardo GrassettiGiorgio FerrariMarco Sampietro2012-03-02T15:49:32Z2013-09-30T12:32:05Zhttp://eprints.imtlucca.it/id/eprint/1210This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/12102012-03-02T15:49:32ZSecond-order sliding-mode control of a mobile robot based on a harmonic potential fieldThe problem of controlling an autonomous wheeled vehicle which must move in its operative space and reach a prescribed goal point avoiding the collision with the obstacles is dealt with. To comply with the non- holonomic nature of the system, a gradient-tracking approach is followed, so that a reference velocity and orientation are suitably generated during the vehicle motion. To track such references, two control laws are designed by suitably transforming the system model into a couple of auxiliary second-order uncertain systems, relying on which second-order sliding modes can be enforced. As a result, the control objective is attained by means of a continuous control law, so that the problems due to the so-called chattering effect, such as the possible actuators wear or the induction of vibrations, typically associated with the use of sliding-mode control, are circumvented.Antonella FerraraMatteo Rubagotti2012-03-02T14:37:36Z2015-05-12T13:23:25Zhttp://eprints.imtlucca.it/id/eprint/1203This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/12032012-03-02T14:37:36ZModeling and control of an airbrake electro-hydraulic smart actuatorIn this paper, an accurate model of an airbrake electro-hydraulic smart actuator is obtained by physical considerations, and then different control strategies (variable-gain proportional control, PT1 control with switching integrator, and second order sub-optimal sliding mode control) are proposed and analyzed. This application is innovative in the avionic field, and is one of the first attempts to realize a fly-by-wire system for airbrakes, oriented to its immediate employment and installation on current aircraft. The project was carried on with the participation of the Italian Ministry of Defense, and was commissioned to MAG, a leading provider of integrated systems and aviation services for aerospace.Matteo RubagottiMarco CarminatiGiampiero ClementeRiccardo GrassettiAntonella Ferrara2011-08-03T10:52:17Z2011-08-04T07:30:21Zhttp://eprints.imtlucca.it/id/eprint/765This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7652011-08-03T10:52:17ZKriging metamodels in design optimization: an automotive engineering applicationGabriella Dellinogabriella.dellino@imtlucca.itPaolo LinoCarlo MeloniAlessandro Rizzo2011-08-02T10:17:52Z2011-08-04T07:30:21Zhttp://eprints.imtlucca.it/id/eprint/763This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7632011-08-02T10:17:52ZModels for the design and optimization of CNG injection systemsGabriella Dellinogabriella.dellino@imtlucca.itPaolo LinoCarlo MeloniAlessandro Rizzo2011-08-02T09:22:36Z2011-08-04T07:30:21Zhttp://eprints.imtlucca.it/id/eprint/760This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7602011-08-02T09:22:36ZMultidisciplinary design optimization of a pressure controller for CNG injection systemsIn this work, the multidisciplinary design optimization (MDO) methodology is applied to a case arising in the automotive engineering in which the design optimization of mechanical and control features of a system are simultaneously carried out with an evolutionary algorithm based method. The system under study is the regulator of the injection pressure of an innovative Common Rail system for Compressed Natural Gas (CNG) automotive engines, whose engineering design includes several practical and numerical difficulties. To tackle such a situation, this paper proposes a constrained multi-objective optimization method, that pursues the Pareto-optimality on the basis of fitness functions that capture domain specific design aspects as well as static and dynamic objectives. The proposed scheme provides ways to incorporate the designers specific knowledge, from interactive actions to simulation based analysis or surrogate-assisted evolution. The computational experiments show the ability of the method for finding a relevant and satisfactory set of efficient solutions.Gabriella Dellinogabriella.dellino@imtlucca.itPaolo LinoCarlo MeloniAlessandro Rizzo2011-07-29T10:53:29Z2012-07-09T09:25:27Zhttp://eprints.imtlucca.it/id/eprint/743This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7432011-07-29T10:53:29ZDecentralized hybrid model predictive control of a formation of unmanned aerial vehiclesThis paper proposes a hierarchical MPC strategy for autonomous navigation of a formation of unmanned aerial vehicles (UAVs) of quadcopter type under obstacle and collision avoidance constraints. Each vehicle is stabilized by a lower-level local linear MPC controller around a desired position, that is generated, at a slower sampling rate, by a hybrid MPC controller per vehicle. Such an upper control layer is based on a hybrid dynamical model of the UAV in closed-loop with its linear MPC controller and of its surrounding environment (i.e., the other UAVs and obstacles). The resulting decentralized scheme controls the formation based on a leader-follower approach. The performance of the hierarchical control scheme is assessed through simulations and comparisons with other path planning strategies, showing the ability of linear MPC to handle the strong couplings among the dynamical variables of each quadcopter under motor voltage and angle/position constraints, and the flexibility of the decentralized hybrid MPC scheme in planning the desired paths on-line.Alberto Bemporadalberto.bemporad@imtlucca.itClaudio Rocchi2011-07-29T10:53:06Z2012-07-09T09:32:53Zhttp://eprints.imtlucca.it/id/eprint/741This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7412011-07-29T10:53:06ZHybrid dynamic optimization for cruise speeed controlThe cruise control problem of transferring the speed of a vehicle between two values in a fixed interval of time using a predefined sequence of gears is solved in this paper. This is a hybrid dynamic optimization problem since the control variables include both a continuous variable (fuel flow) and a discrete variable (the gear to apply at each instant). The solution is given in the form of a hybrid optimal control algorithm that computes the optimal switching times between gears using Dynamic Programming and the optimal fuel profile between successive gear boundaries using a gradient algorithm to approximate the optimum conditions. In order to reduce the search of the optimal switching times to a search in a finite dimension graph, a procedure based on a changing grid is used. The algorithm is illustrated by a simulation using a diesel one-dimensional car model.Tiago JorgeJoao M. LemosMiguel BarãoAlberto Bemporadalberto.bemporad@imtlucca.it2011-07-29T10:52:40Z2011-11-17T11:01:57Zhttp://eprints.imtlucca.it/id/eprint/738This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7382011-07-29T10:52:40ZStochastic model predictive control with driver behavior learning for improved powertrain controlIn this paper we advocate the use of stochastic model predictive control (SMPC) for improving the performance of powertrain control algorithms, by optimally controlling the complex system composed of driver and vehicle. While the powertrain is modeled as the deterministic component of the dynamics, the driver behavior is represented as a
stochastic system which affects the vehicle dynamics. Since stochastic MPC is based on online numerical optimization, the driver model can be learned online, hence allowing the control algorithm to adapt to different drivers and drivers' behaviors. The proposed technique is evaluated in two applications: adaptive cruise control, where the driver behavioral model is used to predict the leading vehicle dynamics, and series hybrid electric vehicle (SHEV) energy management, where the driver model is used to predict the future power requests.M. BichiGiulio RipaccioliStefano Di CairanoDaniele Bernardinidaniele.bernardini@imtlucca.itAlberto Bemporadalberto.bemporad@imtlucca.itIlya Kolmanovsky2011-07-27T09:20:37Z2014-07-16T13:05:32Zhttp://eprints.imtlucca.it/id/eprint/596This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5962011-07-27T09:20:37ZLocal incremental planning for a car-like robot navigating among obstaclesWe present a local approach for planning the motion of a car-like robot navigating among obstacles, suitable for sensor-based implementation. The nonholonomic nature of the robot kinematics is explicitly taken into account. The strategy is to modify the output of a generic local holonomic planner, so as to provide commands that realize the desired motion in a least-squares sense. A feedback action tends to align the vehicle with the local force field. In order to avoid the motion stops away from the desired goal, various force fields are considered and compared by simulationAlberto Bemporadalberto.bemporad@imtlucca.itAlessandro De LucaGiuseppe Oriolo2011-07-27T09:20:29Z2014-07-16T13:01:51Zhttp://eprints.imtlucca.it/id/eprint/593This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5932011-07-27T09:20:29ZAnalog fuzzy implementation of a vehicle traction sliding-mode controlRoad adherence is an imprecise function of many parameters strongly affected by road conditions. In this paper, we propose a very robust control with a static nonlinear feedback law which can consider adherence and other model uncertainties, regulating the wheel slip at any desired value with good precision properties. A sliding-mode control has been designed to provide stability and reliability. Once designed, the control surface has been fuzzified and implemented with a programmable analog fuzzy circuit which uses a 0.7 mu m CMOS technology provided by SGS-Thomson Microelectronics. This implementation is carried out with a semi-automatic design flow and features high computational efficiency at it very low cost, especially when compared to a digital one. Moreover, the controller response time is less than 1 mu s. A flexible control of the slip coefficient has been performed. Results show that the desired slip coefficient is reached and kept with good approximation in compliance with theoretical results.A. BelliniAlberto Bemporadalberto.bemporad@imtlucca.itEleonora FranchiNicolò ManaresiRiccardo RovattiG. Torrini2011-07-27T09:11:10Z2014-07-17T12:48:08Zhttp://eprints.imtlucca.it/id/eprint/519This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5192011-07-27T09:11:10ZA hybrid approach to traction controlIn this paper we describe a hybrid model and an optimization-based control strategy for solving a traction control prob- lem currently under investigation at Ford Research Laboratories. We show through simulations on a model and a realistic set of parameters that good and robust performance is achieved. Furthermore, the result- ing optimal controller is a piecewise linear function of the measurements that can be implemented on low cost control hardware. Francesco BorrelliAlberto Bemporadalberto.bemporad@imtlucca.itMichael FodorDavor Hrovat2011-07-27T09:11:08Z2014-07-17T12:49:10Zhttp://eprints.imtlucca.it/id/eprint/582This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5822011-07-27T09:11:08ZDiscrete-time hybrid modeling and verificationFor hybrid systems described by interconnections of linear dynamical systems and logic devices, we recently (A. Bemporad et al., 2000, 2001) proposed mixed logical-dynamical (MLD) systems and the language HYSDEL (HYbrid System DEscription Language) as a modeling tool. For MLD models, we developed a reachability analysis algorithm which combines forward reach-set computation and feasibility analysis of trajectories by linear and mixed-integer linear programming. In this paper, the versatility of the overall analysis tool is illustrated in the verification of an automotive cruise control system for a car with a robotized manual gear shiftFabio Danilo TorrisiAlberto Bemporadalberto.bemporad@imtlucca.it2011-07-27T09:08:51Z2014-07-17T12:40:36Zhttp://eprints.imtlucca.it/id/eprint/576This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5762011-07-27T09:08:51ZOptimal piecewise-linear control of dry clutch engagementBased on a discrete-time second order state-space dynamic model of the powertrain system, a piecewise feedback control for the dry clutch engagement process is proposed. The engine speed and the clutch disk speed are assumed to be measurable and the control input is the normal engaging force applied to the disks. The controller is designed by minimizing a quadratic performance index subject to constraints on the normal force, normal force derivative, and engine speed. The resulting Model Predictive Controller (MPC) is shown to consist of a piecewise linear feedback control: the state space can be divided into several regions, such that in each region an off-line computed linear controller must be implemented. The explicit piecewise linear form of the MPC law is obtained by using a multiparametric programming solver and can be tuned so that fast engagement, small friction losses and smooth lock-up are achieved. The paper reports numerical results, carried out by a Simulink/MPC Toolbox simulation scheme and a realistic set of parameters, showing the good performance of the closed-loop system.Alberto Bemporadalberto.bemporad@imtlucca.itFrancesco BorrelliLuigi GlielmoFrancesco Vasca2011-07-27T09:08:49Z2014-07-17T12:40:08Zhttp://eprints.imtlucca.it/id/eprint/579This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5792011-07-27T09:08:49ZHybrid control of dry clutch engagementThis paper proposes a novel piecewise linear feedback control strategy for the automotive dry clutch engagement process. Based on a dynamic model of the powertrain system, the controller is designed by minimizing a quadratic performance index subject to constraints on the inputs and on the states. The resulting model predictive controller is shown to consist of a piecewise linear feedback control and can be tuned so that fast engagement, small friction losses and smooth lock-up are achieved. Numerical results show the good performance of the closed-loop system.Alberto Bemporadalberto.bemporad@imtlucca.itFrancesco BorrelliLuigi GlielmoFrancesco Vasca2011-07-27T09:07:57Z2011-08-04T07:29:09Zhttp://eprints.imtlucca.it/id/eprint/548This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5482011-07-27T09:07:57ZA hybrid system approach to modeling and optimal control of DISC enginesThis paper illustrates the application of hybrid modeling and optimal control to the problem of air-to-fuel ratio and torque control in advanced technology gasoline direct injection stratified charge (DISC) engines. DISC engines have two discrete modes of operation, stratified and homogeneous, and their dynamic behavior can be easily captured by a hybrid model. We show that the design flow (hybrid modeling and controller synthesis) is simple in terms of problem setup and tuning, provides good closed-loop performance, and leads to a control law that can be implemented on automotive hardware as a piecewise affine function of the measured and estimated quantities.Alberto Bemporadalberto.bemporad@imtlucca.itNicolò GiorgettiIlya KolmanovskyDavor Hrovat2011-07-27T09:07:53Z2011-08-04T07:29:09Zhttp://eprints.imtlucca.it/id/eprint/546This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5462011-07-27T09:07:53ZHybrid modeling and control of a direct injection stratified charge engineThis paper illustrates the application of hybrid modeling and optimal control to the problem of air-to-fuel ratio and torque control in advanced technology gasoline direct injection stratified charge (DISC) engines. DISC engines have two discrete modes of operation, stratified and homogeneous, and their dynamic behavior can be easily captured by a hybrid model. We show that the design flow (hybrid modeling and controller synthesis) is simple in terms of problem setup and tuning, provides good closed-loop performance, and leads to a control law that can be implemented on automotive hardware as a piecewise affine function of the measured and estimated quantities. Alberto Bemporadalberto.bemporad@imtlucca.itNicolò GiorgettiIlya KolmanovskyDavor Hrovat2011-07-27T09:03:57Z2011-08-04T07:29:08Zhttp://eprints.imtlucca.it/id/eprint/501This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5012011-07-27T09:03:57ZHybrid control of an automotive robotized gearbox for reduction of consumptions and emissionsThis paper describes the application of hybrid modeling and receding horizon optimal control techniques for supervising an automotive robotized gearbox, with the goal of reducing consumptions and emissions, a problem that is currently under investigation at Fiat Research Center (CRF). We show that the dynamic behavior of the vehicle can be easily approximated and captured by the hybrid model, and through simulations on standard speed patterns that a good closed loop performance can be achieved. The synthesized control law can be implemented on automotive hardware as a piecewise affine function of the measured and estimated quantities. Alberto Bemporadalberto.bemporad@imtlucca.itPandeli BorodaniMassimo Mannelli2011-07-27T08:47:30Z2011-08-04T07:29:08Zhttp://eprints.imtlucca.it/id/eprint/522This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5222011-07-27T08:47:30ZExplicit hybrid optimal control of direct injection stratified charge enginesThis paper illustrates the application of hybrid
modeling and receding horizon optimal control techniques
to the problem of air-to-fuel ratio and torque management
in advanced technology gasoline direct injection stratified
charge (DISC) engines. A DISC engine represents an example
of a constrained hybrid system, because it can operate
in two discrete modes (stratified and homogeneous) and
because the mode-dependent constraints on the air-to-fuel
ratio and on the spark timing need to be enforced. The paper
extends the prior work by the authors [1] and reports the
development of an explicit controller which implements the
optimal solution using piecewise affine functions of the state, thereby avoiding the need for on-line optimization. Strategies to simplify the explicit controller by reducing the number of regions in its characterization are discussed.Nicolò GiorgettiAlberto Bemporadalberto.bemporad@imtlucca.itIlya KolmanovskyDavor Hrovat2011-07-27T08:45:13Z2016-04-06T10:27:19Zhttp://eprints.imtlucca.it/id/eprint/523This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5232011-07-27T08:45:13ZHybrid model predictive control application towards optimal semi-active suspensionThe optimal control problem of a quartercar semi-active suspension has been studied in the past.
Considering that a quarter-car semi-active suspension can
either be modeled as a linear system with state dependent
constraint on control (of actuator force) input, or a bilinear system with a control (of variable damping coefficient) saturation, the seemingly simple problem poses several interesting questions and challenges. Does the optimal control law derived from the corresponding un-constrained system, i.e. “clipped-optimal”, remain optimal for the constrained case? If the optimal control law of the constrained system does deviate from its un-constrained counter-part, how different are they? What is the structure of the optimal control law? In this paper, we attempt to answer some of the above questions by utilizing the recent development in model predictive control (MPC) of hybrid dynamical systems. The constrained quarter-car semi-active suspension is modeled as a switching affine system, where the switching is determined
by the activation of passivity constraints, force saturation, and maximum power dissipation limits. Theoretically, over an infinite prediction horizon the MPC controller corresponds to the exact optimal controller. The performance of different finite-horizon hybrid MPC controllers is tested in simulation using mixed-integer quadratic programming.
Then, for short-horizon MPC controllers, we derive the
explicit optimal control law and show that the optimal
control is piecewise affine in state. In particular, we show
that for horizon equal to one the explicit MPC control law
corresponds to clipped LQR. We will compare the derived
optimal control law to various semi-active control laws in
the literature including the well-known “clipped-optimal”.
We will evaluate their corresponding performances for both
a deterministic shock input case and a stochastic random
disturbances case through simulations.Nicolò GiorgettiAlberto Bemporadalberto.bemporad@imtlucca.itH. E. TsengDavor Hrovat2011-07-27T08:43:58Z2016-04-06T10:27:04Zhttp://eprints.imtlucca.it/id/eprint/488This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/4882011-07-27T08:43:58ZHybrid model predictive control application towards optimal semi-active suspensionThe optimal control problem of a quarter-car semi-active suspension has been studied in the past. Considering that a quarter-car semi-active suspension can either be modelled as a linear system with state dependent constraint on control (of actuator force) input, or a bi-linear system with a control (of variable damping coefficient) saturation, the seemingly simple problem poses several interesting questions and challenges. Does the saturated version of the optimal control law derived from the corresponding un-constrained system, i.e. “clipped-optimal”, remain optimal for the constrained case as suggested in some previous publications? Or should the optimal deviate from the “clipped-optimal” as suggested in other publications? If the optimal control law of the constrained system does deviate from its unconstrained counter-part, how different are they? What is the structure of the optimal control law? Does it retain the linear state feedback form (as the unconstrained case)? In this paper, we attempt to answer some of the above questions by utilizing the recent development in model predictive control (MPC) of hybrid dynamical systems.
The constrained quarter-car semi-active suspension is modelled as a switching affine system, where the switching is determined by the activation of passivity constraints, force saturation, and maximum power dissipation limits. Theoretically, over an infinite prediction horizon the MPC controller corresponds to the exact optimal controller. The performance of different finite-horizon hybrid MPC controllers is tested in simulation using mixed-integer quadratic programming. Then, for short-horizon MPC controllers, we derive the explicit optimal control law and show that the optimal control is piecewise affine in state. In the process, we show that for horizon equal to one the explicit MPC control law corresponds to clipped LQR as expected. We also compare the derived optimal control law to various semi-active control laws in the literature including the well-known “clipped-optimal”. We evaluate their corresponding performances for both a deterministic shock input case and a stochastic random disturbances case through simulations.Nicolò GiorgettiAlberto Bemporadalberto.bemporad@imtlucca.itH. E. TsengDavor Hrovat2011-07-27T08:43:56Z2011-08-05T13:43:06Zhttp://eprints.imtlucca.it/id/eprint/491This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/4912011-07-27T08:43:56ZHybrid model predictive control of direct injection stratified charge enginesThis paper illustrates the application of hybrid modeling and model predictive control techniques to the management of air-to-fuel ratio and torque in advanced technology gasoline direct-injection stratified-charge (DISC) engines. A DISC engine is an example of a constrained hybrid dynamical system, because it can operate in two distinct modes (stratified and homogeneous) and because the mode-dependent constraints on the air-to-fuel ratio and on the spark timing need to be enforced during its operation to avoid misfire, knock, and high combustion variability. In this paper, we approximate the DISC engine dynamics as a two-mode discrete-time switched affine system. Using this approximation, we tune a hybrid model predictive controller with integral action based on online mixed-integer quadratic optimization, and show the effectiveness of the approach through simulations. Then, using an offline multiparametric optimization procedure, we convert the controller into an equivalent explicit piecewise affine form that is easily implementable in an automotive microcontroller through a lookup table of linear gainsNicolò GiorgettiGiulio RipaccioliAlberto Bemporadalberto.bemporad@imtlucca.itIlya KolmanovskyDavor Hrovat2011-07-27T08:43:52Z2011-08-05T13:42:05Zhttp://eprints.imtlucca.it/id/eprint/539This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5392011-07-27T08:43:52ZModel predictive control of nonlinear mechatronic systems: an application to a magnetically actuated mass spring damperMechatronic systems in the automotive applications are characterized by significant nonlinearities and tight performance specifications further exacerbated by state and input constraints. Model Predictive Control (MPC) in conjunction with hybrid modeling can be an attractive and systematic methodology to handle these challenging control problems. In this paper, we focus on a mass spring damper system actuated by an electromagnet, which is one of the most common elements in the automotive actuators, with fuel injectors representing a concrete example. We present two designs which are based, respectively, on a linear MPC approach in cascade with a nonlinear state-dependent saturation, and on a hybrid MPC approach. The performance and the complexity of the two MPC controllers are compared.Stefano Di CairanoAlberto Bemporadalberto.bemporad@imtlucca.itIlya KolmanovskyDavor Hrovat2011-07-27T08:40:33Z2011-08-05T13:19:18Zhttp://eprints.imtlucca.it/id/eprint/493This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/4932011-07-27T08:40:33ZStabilizing model predictive control of hybrid systemsIn this note, we investigate the stability of hybrid systems in closed-loop with model predictive controllers (MPC). A priori sufficient conditions for Lyapunov asymptotic stability and exponential stability are derived in the terminal cost and constraint set fashion, while allowing for discontinuous system dynamics and discontinuous MPC value functions. For constrained piecewise affine (PWA) systems as prediction models, we present novel techniques for computing a terminal cost and a terminal constraint set that satisfy the developed stabilization conditions. For quadratic MPC costs, these conditions translate into a linear matrix inequality while, for MPC costs based on 1, infin-norms, they are obtained as norm inequalities. New ways for calculating low complexity piecewise polyhedral positively invariant sets for PWA systems are also presented. An example illustrates the developed theoryMircea LazarW.P.M.H. HeemelsSiep WeilandAlberto Bemporadalberto.bemporad@imtlucca.it2011-07-27T08:40:30Z2011-08-05T13:15:12Zhttp://eprints.imtlucca.it/id/eprint/505This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5052011-07-27T08:40:30ZModel predictive control of magnetic automotive actuatorsMagnetically actuated mass-spring-damper systems are common in automotive systems as components of various actuation mechanisms. They are characterized by nonlinear dynamics, tight performance specifications and physical constraints. Due to these reasons, model predictive control (MPC) is an appealing control framework for such systems. In this paper we describe different MPC approaches to control the magnetically actuated mass-spring-damper system. The MPC controller based on the complete system model achieves very good performance, yet it may be too complex to be implemented in standard automotive microcontrollers. Hence, we consider the possibility of decoupling the electromagnetic subsystem from the mechanical subsystem, assuming that the electromagnetic dynamics, controlled by an inner-loop controller, are much faster than the mechanical dynamics. Based on a previous feasibility study, we implement a control architecture in which the MPC optimizes only the dynamics of the mechanical subsystem, and we test it in closed-loop simulations with the nonlinear system. The resulting control system achieves lower performance, but it is simple enough to be implemented in an automotive microcontroller.Stefano Di CairanoAlberto Bemporadalberto.bemporad@imtlucca.itIlya KolmanovskyDavor Hrovat2011-07-27T08:39:08Z2011-08-04T07:29:07Zhttp://eprints.imtlucca.it/id/eprint/545This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5452011-07-27T08:39:08ZA wireless magneto-resistive sensor network for real-time vehicle detectionThis works describes a prototype wireless sensor network for vehicle detection developed at the University of Siena in collaboration with the Italian highways society Autostrade S.p.A. Each wireless sensor node is composed by an in-house designed electronic board driving a 2-axis Honeywell HMC1002 magneto-resistive sensor interfaced to a Telos rev.b (Moteiv Corporation) mote, and by a Matlab/Simulink interface for collecting and processing sensor data in (soft) real-time.Alberto Bemporadalberto.bemporad@imtlucca.itF. GentileA. MecocciFrancesco MolendiF. Rossi2011-07-27T08:39:05Z2011-08-05T13:15:39Zhttp://eprints.imtlucca.it/id/eprint/484This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/4842011-07-27T08:39:05ZModel predictive control of magnetically actuated mass spring dampers for automotive applicationsMechatronic systems such as those arising in automotive applications are characterized by significant non-linearities, tight performance specifications as well as by state and input constraints which need to be enforced during system operation. This paper takes a view that model predictive control (MPC) and hybrid models can be an attractive and systematic methodology to handle these challenging control problems, even when the underlying process is not hybrid. In addition, the piecewise affine (PWA) explicit form of MPC solutions avoids on-line optimization and can make this approach computationally viable even in situations with rather constrained computational resources. To illustrate the MPC design procedure and the underlying issues, we focus on a specific non-linear process example of a mass spring damper system actuated by an electromagnet. Such a system is one of the most common elements of mechatronic systems in automotive systems, with fuel injectors representing a concrete example. We first consider a linear MPC design for the mechanical part of the system. The approach accounts for all the constraints in the system but one, which is subsequently enforced via a state-dependent saturation element. Second, a hybrid MPC approach for the mechanical subsystem is analysed that can handle all the constraints by design and achieves better performance, at the price of a higher complexity of the controller. Finally, a hybrid MPC design that also takes into account the electrical dynamics of the system is considered.Stefano Di CairanoAlberto Bemporadalberto.bemporad@imtlucca.itIlya KolmanovskyDavor Hrovat2011-06-21T10:13:44Z2011-08-05T12:57:37Zhttp://eprints.imtlucca.it/id/eprint/615This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/6152011-06-21T10:13:44ZAn MPC design flow for automotive control and applications to idle speed regulationThis paper describes the steps of a model predictive control (MPC) design procedure developed for a broad class of control problems in automotive engineering. The design flow starts by deriving a linearized discrete-time prediction model from an existing simulation model, augmenting it with integral action or output disturbance models to ensure offset-free steady-state properties, and tuning the resulting MPC controller in simulation. Explicit MPC tools are employed to synthesize the controller to quickly assess controller complexity, local stability of the closed-loop dynamics, and for rapid prototype testing. Then, the controller is fine-tuned by refining the linear prediction model through identification from experimental data, and by adjusting from observed experimental performance the values of weights and noise covariances for filter design. The idle speed control (ISC) problem is used in this paper to exemplify the design flow and our vehicle implementation results are reported.Stefano Di CairanoDiana YanakievAlberto Bemporadalberto.bemporad@imtlucca.itIlya KolmanovskyDavor Hrovat