IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-28T13:03:55ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2011-07-29T10:53:35Z2012-07-09T09:26:08Zhttp://eprints.imtlucca.it/id/eprint/744This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7442011-07-29T10:53:35ZDecentralized hierarchical multi-rate control of constrained linear systemsThis paper proposes a decentralized hierarchical multi-rate control scheme for large-scale dynamically-coupled linear systems subject to linear constraints on input and state variables. At the lower level, a set of decentralized and independent linear controllers stabilizes the process, without taking care of the constraints. Each controller receives reference signals from its own upper-level controller, that runs at a lower sampling frequency. By optimally constraining the magnitude and rate of variation of the reference signals to each lower-level controller, quantitative criteria are provided for selecting the ratio between the sampling rates of the upper and lower layers of control at each location, in a way that closed-loop stability is preserved and the fulfillment of the prescribed constraints is guaranteed.Alberto Bemporadalberto.bemporad@imtlucca.itDavide BarcelliGiulio Ripaccioli2011-07-29T10:52:40Z2011-11-17T11:01:57Zhttp://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 Kolmanovsky2011-07-29T10:52:30Z2011-08-05T12:20:39Zhttp://eprints.imtlucca.it/id/eprint/736This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7362011-07-29T10:52:30ZHierarchical multi-rate control design for constrained linear systemsThis paper proposes a hierarchical multi-rate control design approach to linear systems subject to linear constraints on input and output variables. At the lower level, a linear controller stabilizes the open-loop process without considering the constraints. A higher-level controller commands reference signals at a lower sampling frequency so as to enforce linear constraints on the variables of the process. By optimally constraining the magnitude and the rate of variation of the reference signals applied to the lower control layer, we provide quantitative criteria for selecting the ratio between the sampling rates of the upper and lower layers to preserve closed-loop stability without violating the prescribed constraints.Davide BarcelliAlberto Bemporadalberto.bemporad@imtlucca.itGiulio Ripaccioli2011-07-27T08:43:56Z2011-08-05T13:43:06Zhttp://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 Hrovat2011-07-27T08:34:12Z2011-08-05T12:43:59Zhttp://eprints.imtlucca.it/id/eprint/513This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5132011-07-27T08:34:12ZHybrid modeling, identification, and predictive control: an application to hybrid electric vehicle energy managementRising fuel prices and tightening emission regulations have resulted in an increasing need for advanced powertrain systems and systematic model-based control approaches. Along these lines, this paper illustrates the use of hybrid modeling and model predictive control for a vehicle equipped with an advanced hybrid powertrain. Starting from an existing high fidelity nonlinear simulation model based on experimental data, the hybrid dynamical model is developed through the use of linear and piecewise affine identification methods. Based on the resulting hybrid dynamical model, a hybrid MPC controller is tuned and its effectiveness is demonstrated through closed-loop simulations with the high-fidelity nonlinear model. Giulio RipaccioliAlberto Bemporadalberto.bemporad@imtlucca.itF. AssadianC. DextreitStefano Di CairanoIlya Kolmanovsky2011-07-27T08:29:39Z2011-11-17T11:26:40Zhttp://eprints.imtlucca.it/id/eprint/427This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/4272011-07-27T08:29:39ZA stochastic model predictive control approach for series hybrid electric vehicle power managementThis paper illustrates the use of stochastic model predictive control (SMPC) for power management in vehicles equipped with advanced hybrid powertrains. Hybrid vehicles use two or more distinct power sources for propulsion, and their complex powertrain architecture requires the coordination of all the subsystems to achieve target performances in terms of fuel consumption, driveability, component life-time, exhaust emissions. Many control strategies have been presented and successfully applied, mainly based on heuristics or rules and tuned on certain reference drive cycles. To take into account that cycles are not exactly known a priori in driving routine, this paper proposes a stochastic approach for the power management problem. We focus on a series hybrid electric vehicle (HEV), which combines an internal combustion engine and an electric motor. The power demand from the driver is modeled as a Markov chain estimated on several driving cycles and used to generate scenarios in the SMPC law. Simulation results over a standard driving cycle are presented to demonstrate the effectiveness of the proposed stochastic approach and compared with other deterministic approaches.Giulio RipaccioliDaniele Bernardinidaniele.bernardini@imtlucca.itStefano Di CairanoAlberto Bemporadalberto.bemporad@imtlucca.itIlya Kolmanovsky