IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-05-18T03:26:28ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2015-03-26T11:45:05Z2015-03-26T11:45:05Zhttp://eprints.imtlucca.it/id/eprint/2447This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24472015-03-26T11:45:05ZRobust pole placement for plants with semialgebraic parametric uncertaintyIn this paper we address the problem of robust pole placement for linear-time-invariant systems whose uncertain parameters are assumed to belong to a semialgebraic region. A dynamic controller is designed in order to constrain the coefficients of the closed-loop characteristic polynomial within prescribed intervals. Two main topics arising from the problem of robust pole placement are tackled by means of polynomial optimization. First, necessary conditions on the plant parameters for the existence of a robust controller are given. Then, the set of all admissible robust controllers is sought. Convex relaxation techniques based on sum-of-square decomposition of positive polynomials are used to efficiently solve the formulated optimization problems through semidefinite programming techniques.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-03-26T11:36:44Z2015-03-26T11:36:44Zhttp://eprints.imtlucca.it/id/eprint/2439This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24392015-03-26T11:36:44ZSet-membership EIV identification through LMI relaxation techniquesIn this paper the Set-membership Error-In-Variables (EIV) identification problem is considered, that is the identification of linear dynamic systems when both the output and the input measurements are corrupted by bounded noise. A new approach for the computation of the Parameters Uncertainty Intervals (PUIs) is discussed. First the problem is formulated in terms of non-convex semi-algebraic optimization. Then, a Linear-Matrix-Inequalities relaxation technique is presented to compute parameters bounds by means of convex optimization. Finally, convergence properties and computational complexity of the given algorithms are discussed. Advantages of the proposed technique with respect to previously published ones are discussed both theoretically and by means of a simulated example.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-13T14:42:02Z2015-01-13T14:42:02Zhttp://eprints.imtlucca.it/id/eprint/2478This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24782015-01-13T14:42:02ZA unified framework for solving a general class of conditional and robust set-membership estimation problemsIn this paper, we present a unified framework for solving a general class of problems arising in the context of set-membership estimation/identification theory. More precisely, the paper aims at providing an original approach for the computation of optimal conditional and robust projection estimates in a nonlinear estimation setting, where the operator relating the data and the parameter to be estimated is assumed to be a generic multivariate polynomial function, and the uncertainties affecting the data are assumed to belong to semialgebraic sets. By noticing that the computation of both the conditional and the robust projection optimal estimators requires the solution to min-max optimization problems that share the same structure, we propose a unified two-stage approach based on semidefinite-relaxation techniques for solving such estimation problems. The key idea of the proposed procedure is to recognize that the optimal functional of the inner optimization problems can be approximated to any desired precision by a multivariate polynomial function by suitably exploiting recently proposed results in the field of parametric optimization. Two simulation examples are reported to show the effectiveness of the proposed approach.Vito CeroneJean-Bernard LasserreDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-13T14:34:09Z2015-11-02T09:57:27Zhttp://eprints.imtlucca.it/id/eprint/2477This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24772015-01-13T14:34:09ZCharacteristic polynomial assignment for plants with semialgebraic uncertainty: a robust diophantine equation approachIn this paper, we address the problem of robust characteristic polynomial assignment for LTI systems whose parameters are assumed to belong to a semialgebraic uncertainty region. The objective is to design a dynamic fixed-order controller in order to constrain the coefficients of the closed-loop characteristic polynomial within prescribed intervals. First, necessary conditions on the plant parameters for the existence of a robust controller are reviewed, and it is shown that such conditions are satisfied if and only if a suitable Sylvester matrix is nonsingular for all possible values of the uncertain plant parameters. The problem of checking such a robust nonsingularity condition is formulated in terms of a nonconvex optimization problem. Then, the set of all feasible robust controllers is sought through the solution to a suitable robust diophantine equation. Convex relaxation techniques based on sum-of-square decomposition of positive polynomials are used to efficiently solve the formulated optimization problems by means of semidefinite programming. The presented approach provides a generalization of the results previously proposed in the literature on the problem of assigning the characteristic polynomial in the presence of plant parametric uncertainty.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-13T14:22:23Z2015-01-13T14:22:23Zhttp://eprints.imtlucca.it/id/eprint/2475This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24752015-01-13T14:22:23ZApproximation of model predictive control laws for polynomial systemsA fast implementation of a given predictive controller for polynomial systems is introduced by approximating the optimal control law with a piecewise constant function defined over a hyper-cube partition of the system state space. Such a state-space partition is computed in order to guarantee stability, an a priori fixed trajectory error as well as input and state constraints fulfilment. The presented approximation procedure is achieved by solving a set of nonconvex polynomial optimization problems, whose approximate solutions are computed by means of semidefinite relaxation techniques for semialgebraic problems.Massimo CanaleVito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-13T14:12:42Z2015-01-13T14:12:42Zhttp://eprints.imtlucca.it/id/eprint/2473This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24732015-01-13T14:12:42ZA convex relaxation approach to set-membership identification of LPV systems Abstract Identification of linear parameter varying models is considered in this paper, under the assumption that both the output and the scheduling parameter measurements are affected by bounded noise. First, the problem of computing parameter uncertainty intervals is formulated in terms of nonconvex optimization. Then, on the basis of the analysis of the regressor structure, we present an ad hoc convex relaxation scheme for computing parameter bounds by means of semidefinite optimization. Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-13T14:08:50Z2015-01-13T14:08:50Zhttp://eprints.imtlucca.it/id/eprint/2472This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24722015-01-13T14:08:50ZFixed-order FIR approximation of linear systems from quantized input and output data Abstract The problem of identifying a fixed-order {FIR} approximation of linear systems with unknown structure, assuming that both input and output measurements are subjected to quantization, is dealt with in this paper. A fixed-order {FIR} model providing the best approximation of the input–output relationship is sought by minimizing the worst-case distance between the output of the true system and the modeled output, for all possible values of the input and output data consistent with their quantized measurements. The considered problem is firstly formulated in terms of robust optimization. Then, two different algorithms to compute the optimum of the formulated problem by means of linear programming techniques are presented. The effectiveness of the proposed approach is illustrated by means of a simulation example. Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-13T14:01:48Z2015-01-13T14:01:48Zhttp://eprints.imtlucca.it/id/eprint/2471This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24712015-01-13T14:01:48ZComputational load reduction in bounded error identification of Hammerstein systemsIn this technical note we present a procedure for the identification of Hammerstein systems from measurements affected by bounded noise. First, we show that computation of tight parameter bounds requires the solution to nonconvex optimization problems where the number of decision variables increases with the length of the experimental data sequence. Then, in order to reduce the computational burden of the identification problem, we propose a procedure to relax the formulated problem into a collection of polynomial optimization problems where the number of variables does not depend on the number of measurements. Advantages of the presented approach with respect to previously published results are discussed and highlighted by means of a simulation example.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-13T13:57:44Z2015-01-13T14:35:23Zhttp://eprints.imtlucca.it/id/eprint/2470This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24702015-01-13T13:57:44ZBounding the parameters of block-structured nonlinear feedback systemsIn this paper, a procedure for set-membership identification of block-structured nonlinear feedback systems is presented. Nonlinear block parameter bounds are first computed by exploiting steady-state measurements. Then, given the uncertain description of the nonlinear block, bounds on the unmeasurable inner signal are computed. Finally, linear block parameter bounds are evaluated on the basis of output measurements and computed inner-signal bounds. The computation of both the nonlinear block parameters and the inner-signal bounds is formulated in terms of semialgebraic optimization and solved by means of suitable convex LMI relaxation techniques. The problem of linear block parameter evaluation is formulated in terms of a bounded errors-in-variables identification problem. Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-13T13:40:47Z2015-01-13T13:40:47Zhttp://eprints.imtlucca.it/id/eprint/2468This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24682015-01-13T13:40:47ZBounded error identification of Hammerstein systems through sparse polynomial optimization In this paper we present a procedure for the evaluation of bounds on the parameters of Hammerstein systems, from output measurements affected by bounded errors. The identification problem is formulated in terms of polynomial optimization, and relaxation techniques, based on linear matrix inequalities, are proposed to evaluate parameter bounds by means of convex optimization. The structured sparsity of the formulated identification problem is exploited to reduce the computational complexity of the convex relaxed problem. Analysis of convergence properties and computational complexity is reported. Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-13T13:28:37Z2015-01-13T13:28:37Zhttp://eprints.imtlucca.it/id/eprint/2467This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24672015-01-13T13:28:37ZSet-Membership Error-in-variables identification through convex relaxation techniques In this technical note, the set membership error-in-variables identification problem is considered, that is the identification of linear dynamic systems when both output and input measurements are corrupted by bounded noise. A new approach for the computation of parameter uncertainty intervals is presented. First, the identification problem is formulated in terms of nonconvex optimization. Then, relaxation techniques based on linear matrix inequalities are employed to evaluate parameter bounds by means of convex optimization. The inherent structured sparsity of the original identification problems is exploited to reduce the computational complexity of the relaxed problems. Finally, convergence properties and complexity of the proposed procedure are discussed. Advantages of the presented technique with respect to previously published results are discussed and shown by means of two simulated examples.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-12T14:46:07Z2015-01-12T14:46:07Zhttp://eprints.imtlucca.it/id/eprint/2466This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24662015-01-12T14:46:07ZEnforcing stability constraints in set-membership identification of linear dynamic systems In this paper, we consider the identification of linear systems, a priori known to be stable, from input–output data corrupted by bounded noise. By taking explicitly into account a priori information on system stability, a formal definition of the feasible parameter set for a stable linear system is provided. On the basis of a detailed analysis of the geometrical structure of the feasible set, convex relaxation techniques are presented to solve nonconvex optimization problems arising in the computation of parameter uncertainty intervals. Properties of the computed relaxed bounds are discussed. A simulated example is presented to show the effectiveness of the proposed technique. Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-12T14:39:42Z2015-01-13T14:49:53Zhttp://eprints.imtlucca.it/id/eprint/2465This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24652015-01-12T14:39:42ZSet-membership LPV model identification of vehicle lateral dynamics Set-membership identification of a Linear Parameter Varying (LPV) model describing the vehicle lateral dynamics is addressed in the paper. The model structure, chosen as much as possible on the ground of physical insights into the vehicle lateral behavior, consists of two single-input single-output {LPV} models relating the steering angle to the yaw rate and to the sideslip angle. A set of experimental data obtained by performing a large number of maneuvers is used to identify the vehicle lateral dynamics model. Prior information on the error bounds on the output and the time-varying parameter measurements are taken into account. Comparison with other vehicle lateral dynamics models is discussed. Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-12T14:32:40Z2015-01-12T14:32:40Zhttp://eprints.imtlucca.it/id/eprint/2464This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24642015-01-12T14:32:40ZImproved parameter bounds for set-membership EIV problemsIn this paper, we consider the set-membership error-in-variables identification problem, that is the identification of linear dynamic systems when output and input measurements are corrupted by bounded noise. A new approach for the computation of parameters uncertainty intervals is presented. First, the problem is formulated in terms of nonconvex optimization. Then, a relaxation procedure is proposed to compute parameter bounds by means of semidefinite programming techniques. Finally, accuracy of the estimate and computational complexity of the proposed algorithm are discussed. Advantages of the proposed technique with respect to previously published ones are discussed both theoretically and by means of a simulated exampleVito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-12T12:06:11Z2015-01-12T12:06:11Zhttp://eprints.imtlucca.it/id/eprint/2458This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24582015-01-12T12:06:11ZSM identification of input-output LPV models with uncertain time-varying parametersIn this chapter, we consider the identification of single-input single-output linear-parameter-varying models when both the output and the time-varying parameter measurements are affected by bounded noise. First, the problem of computing exact parameter uncertainty intervals is formulated in terms of semialgebraic optimization. Then, a suitable relaxation tecnique is presented to compute parameter bounds by means of convex optimization. Advantages of the presented approach with respect to previously published results are discussed.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-12T11:47:05Z2015-01-12T11:47:05Zhttp://eprints.imtlucca.it/id/eprint/2457This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24572015-01-12T11:47:05ZBounded error identification of Hammerstein Systems with backlashActuators and sensors commonly used in control systems may exhibit a variety of nonlinear behaviours that may be responsible for undesirable phenomena such as delays and oscillations, which may severely limit both the static and the dynamic performance of the system under control (see, e.g., [22]). In particular, one of the most relevant nonlinearities affecting the performance of industrial machines is the backlash (see Figure 22.1), which commonly occurs in mechanical, hydraulic and magnetic components like bearings, gears and impact dampers (see, e.g., [17]). This nonlinearity, which can be classified as dynamic (i.e., with memory) and hard (i.e. non-differentiable), may arise from unavoidable manufacturing tolerances or sometimes may be deliberately incorporated into the system in order to describe lubrication and thermal expansion effects [3]. The interested reader is referred to [22] for real-life examples of systems with either input or output backlash nonlinearities.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-09T13:37:33Z2015-01-09T13:37:33Zhttp://eprints.imtlucca.it/id/eprint/2453This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24532015-01-09T13:37:33ZPolytopic outer approximations of semialgebraic setsThis paper deals with the problem of finding a polytopic outer approximation P* of a compact semialgebraic set S ⊆ Rn. The computed polytope turns out to be an approximation of the linear hull of the set S. The evaluation of P* is reduced to the solution of a sequence of robust optimization problems with nonconvex functional, which are efficiently solved by means of convex relaxation techniques. Properties of the presented algorithm and its possible applications in the analysis, identification and control of uncertain systems are discussed.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-09T13:32:04Z2015-01-09T13:32:04Zhttp://eprints.imtlucca.it/id/eprint/2452This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24522015-01-09T13:32:04ZFixed order LPV controller design for LPV models in input-output formIn this work, a new synthesis approach is proposed to design fixed-order H∞ controllers for linear parameter-varying (LPV) systems described by input-output (I/O) models with polynomial dependence on the scheduling variables. First, by exploiting a suitable technique for polytopic outer approximation of semi-algebraic sets, the closed loop system is equivalently rewritten as an LPV I/O model depending affinely on an augmented scheduling parameter vector constrained inside a polytope. Then, the problem is reformulated in terms of bilinear matrix inequalities (BMI) and solved by means of a suitable semidefinite relaxation technique.Vito CeroneDario Pigadario.piga@imtlucca.itDiego RegrutoRoland Tóth2015-01-09T12:49:50Z2015-01-09T12:49:50Zhttp://eprints.imtlucca.it/id/eprint/2451This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24512015-01-09T12:49:50ZBounded-error identification of linear systems with input and output backlashIn this paper we present a single-stage procedure for computing bounds on the parameters of linear systems with input and output backlash from output data corrupted by bounded measurement noise. By properly selecting a sequence of input/output measurements, the problem of evaluating parameter bounds is formulated as a collection of sparse nonconvex optimization problems. Convex-relation techniques are exploited to efficiently compute guaranteed bounds on system parameters by means of semidefinite programming.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-09T12:25:17Z2015-01-09T12:25:17Zhttp://eprints.imtlucca.it/id/eprint/2450This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24502015-01-09T12:25:17ZFIR approximation of linear systems from quantized recordsIn this paper we consider the problem of identifying a fixed-order FIR approximation of linear systems with unknown structure, assuming that both input and output measurements are subjected to quantization. In particular, a FIR model of given order which provides the best approximation of the input-output relationship is sought by minimizing the worst-case distance between the output of the true system and the modeled output, for all possible values of the input and output data consistent with their quantized measurements. First we show that the considered problem can be formulated in terms of robust optimization. Then, we present two different algorithms to compute the optimum of the formulated problem by means of linear programming techniques. The effectiveness of the proposed approach is illustrated by means of a simulation example.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-09T12:12:01Z2015-01-09T12:12:01Zhttp://eprints.imtlucca.it/id/eprint/2449This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24492015-01-09T12:12:01ZLPV identification of the glucose-insulin dynamics in Type I DiabetesIn this paper we address the problem of identifying a linear parameter varying (LPV) model of the glucose-insulin dynamics in Type I diabetic patients. First, the identification problem is formulated in the framework of bounded-error identification, then an algorithm for parameter bounds computation, based on semidefinite programming, is presented. The effectiveness of the proposed approach is tested in simulation by means of the widely adopted nonlinear Sorensen patient model.Vito CeroneDario Pigadario.piga@imtlucca.itDiego RegrutoSintayehu Berehanu2015-01-09T11:59:20Z2015-01-09T11:59:20Zhttp://eprints.imtlucca.it/id/eprint/2448This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24482015-01-09T11:59:20ZInput-Output LPV Model identification with guaranteed quadratic stabilityThe problem of identifying linear parameter-varying (LPV) systems, a-priori known to be quadratically stable, is considered in the paper using an input-output model structure. To solve this problem, a novel constrained optimization-based algorithm is proposed which guarantees quadratic stability of the identified model. It is shown that this estimation objective corresponds to a nonconvex optimization problem, defined by a set of polynomial matrix inequalities (PMI), whose optimal solution can be approximated by means of suitable convex semidefinite relaxations. Applicability of such relaxation-based estimation approach in the presence of either stochastic or deterministic bounded noise is discussed. A simulation example is also given to demonstrate the effectiveness of the resulting identification method.Vito CeroneDario Pigadario.piga@imtlucca.itDiego RegrutoRoland Tóth2015-01-09T11:36:20Z2015-01-09T11:52:42Zhttp://eprints.imtlucca.it/id/eprint/2446This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24462015-01-09T11:36:20ZMinimal LPV state-space realization driven set-membership identificationSet-membership identification algorithms have been recently proposed to derive linear parameter-varying (LPV) models in input-output form, under the assumption that both measurements of the output and the scheduling signals are affected by bounded noise. In order to use the identified models for controller synthesis, linear time-invariant (LTI) realization theory is usually applied to derive a statespace model whose matrices depend statically on the scheduling signals, as required by most of the LPV control synthesis techniques. Unfortunately, application of the LTI realization theory leads to an approximate state-space description of the original LPV input-output model. In order to limit the effect of the realization error, a new set-membership algorithm for identification of input/output LPV models is proposed in the paper. A suitable nonconvex optimization problem is formulated to select the model in the feasible set which minimizes a suitable measure of the state-space realization error. The solution of the identification problem is then derived by means of convex relaxation techniques.Vito CeroneDario Pigadario.piga@imtlucca.itDiego RegrutoRoland Tóth2015-01-09T11:31:37Z2015-01-09T11:31:37Zhttp://eprints.imtlucca.it/id/eprint/2445This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24452015-01-09T11:31:37ZSet-membership identification of Hammerstein-Wiener systemsSet-membership identification of Hammerstein-Wiener models is addressed in the paper. First, it is shown that computation of tight parameter bounds requires the solutions to a number of nonconvex constrained polynomial optimization problems where the number of decision variables increases with the length of the experimental data sequence. Then, a suitable convex relaxation procedure is presented to significantly reduce the computational burden of the identification problem. A detailed discussion of the identification algorithm properties is reported. Finally, a simulated example is used to show the effectiveness and the computational tractability of the proposed approach.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-09T11:26:33Z2015-01-09T11:26:33Zhttp://eprints.imtlucca.it/id/eprint/2444This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24442015-01-09T11:26:33ZFast implementation of model predictive control with guaranteed performanceA fast implementation of a given predictive controller for nonlinear systems is introduced through a piecewise constant approximate function defined over an hyper-cube partition of the system state space. Such a state partition is obtained by maximizing the hyper-cube volumes in order to guarantee, besides stability, an a priori fixed trajectory error as well as input and state constraints satisfaction. The presented approximation procedure is achieved by solving a set of nonconvex polynomial optimization problems, whose approximate solutions are computed by means of semidefinite relaxation techniques for semialgebraic problems.Massimo CanaleVito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-09T11:12:20Z2015-01-09T11:12:20Zhttp://eprints.imtlucca.it/id/eprint/2443This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24432015-01-09T11:12:20ZComputational burden reduction in set-membership Hammerstein system identificationHammerstein system identification from measurements affected by bounded noise is considered in the paper. First, we show that computation of tight parameter bounds requires the solution to nonconvex optimization problems where the number of decision variables increases with the length of the experimental data sequence. Then, in order to reduce the computational burden of the identification problem, we propose a procedure to relax the previously formulated problem to a set of polynomial optimization problems where the number of variables does not depend on the size of the measurements sequence. Advantages of the presented approach with respect to previously published results are discussed.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-09T10:28:51Z2015-01-09T10:28:51Zhttp://eprints.imtlucca.it/id/eprint/2442This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24422015-01-09T10:28:51ZConvex relaxation techniques for set-membership identification of LPV systemsSet-membership identification of single-input single-output linear parameter varying models is considered in the paper under the assumption that both the output and the scheduling parameter measurements are affected by bounded noise. First, we show that the problem of computing the parameter uncertainty intervals requires the solutions to a number of nonconvex optimization problems. Then, on the basis of the analysis of the regressor structure, we present some ad hoc convex relaxation schemes to compute parameter bounds by means of semidefinite optimization. Advantages of the new techniques with respect to previously published results are discussed both theoretically and by means of simulations.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-09T10:25:09Z2015-01-09T10:25:09Zhttp://eprints.imtlucca.it/id/eprint/2441This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24412015-01-09T10:25:09ZHammerstein systems parameters bounding through sparse polynomial optimizationA single-stage procedure for the evaluation of tight bounds on the parameters of Hammerstein systems from output measurements affected by bounded errors is presented. The identification problem is formulated in terms of polynomial optimization, and relaxation techniques based on linear matrix inequalities are proposed to evaluate parameters bounds by means of convex optimization. The structured sparsity of the identification problem is exploited to reduce the computational complexity of the convex relaxed problem. Convergence proper ties, complexity analysis and advantages of the proposed technique with respect to previously published ones are discussed.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-09T10:00:06Z2015-01-09T10:00:06Zhttp://eprints.imtlucca.it/id/eprint/2440This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24402015-01-09T10:00:06ZBounding the parameters of linear systems with stability constraintsIdentification of linear systems, a priori known to be stable, from input output measurements corrupted by bounded noise is considered in the paper. A formal definition of the feasible parameter set is provided, taking explicitly into account prior information on system stability. On the basis of a detailed analysis of the geometrical structure of the feasible set, convex relaxation techniques are presented to solve nonconvex optimization problems arising in the computation of parameters uncertainty intervals. Properties of the computed relaxed bounds are discussed. A simulated example is presented to show the effectiveness of the proposed technique.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-08T13:24:07Z2015-01-08T13:24:07Zhttp://eprints.imtlucca.it/id/eprint/2436This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24362015-01-08T13:24:07ZSet-membership identification of block-structured nonlinear feedback systemsIn this paper a three-stage procedure for set-membership identification of block-structured nonlinear feedback systems is proposed. Nonlinear block parameters bounds are computed in the first stage exploiting steady-state measurements. Then, given the uncertain description of the nonlinear block, bounds on the unmeasurable inner-signal are computed in the second stage. Finally, linear block parameters bounds are computed in the third stage on the basis of output measurements and computed inner signal bounds. Computation of both the nonlinear block parameters and the inner-signal bounds is formulated in terms of semialgebraic optimization and solved by means of suitable convex LMI relaxation techniques. Linear block parameters are bounded solving a number of linear programming problems.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto2015-01-08T11:51:23Z2015-01-08T11:51:23Zhttp://eprints.imtlucca.it/id/eprint/2434This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24342015-01-08T11:51:23ZParameter bounds evaluation for linear systems with output backlashIn this paper a procedure is presented for deriving parameters bounds of linear systems with output backlash when the output measurement errors are bounded. First, using steady-state input/output data, parameters of the backlash are bounded. Then, given the estimated uncertain backlash and the output measurements collected exciting the system with a PRBS, bounds on the unmeasurable inner signal are computed. Finally, such bounds, together with the input sequence, are used for bounding the parameters of the linear block.Vito CeroneDario Pigadario.piga@imtlucca.itDiego Regruto