Gilberto Pin

Networked Predictive Control of Uncertain Constrained Nonlinear Systems: Recursive Feasibility and Input-to-State Stability Analysis

In this paper, the robust state feedback stabilization of uncertain discrete-time constrained nonlinear systems in which the loop is closed through a packet-based communication network is addressed. In order to cope with model uncertainty, time-varying transmission delays, and packet dropouts (typically affecting the performances of networked control systems), a robust control scheme combining model predictive control with a network delay compensation strategy is proposed in the context of non-acknowledged UDP-like networks. The contribution of the paper is twofold. First, the issue of guaranteeing the recursive feasibility of the optimization problem associated to the receding horizon control law has been addressed, such that the invariance of the feasible region under the networked closed-loop dynamics can be guaranteed. Secondly, by exploiting a novel characterization of regional Input-to-State Stability in terms of time-varying Lyapunov functions, the networked closed-loop system has been proven to be Input-to-State Stable with respect to bounded perturbations.

Robust Model Predictive Control of Nonlinear Systems With Bounded and State-Dependent Uncertainties

In this note, a robust model predictive control scheme for constrained discrete-time nonlinear systems affected by bounded disturbances and state-dependent uncertainties is presented. In order to guarantee the robust satisfaction of the state constraints, restricted constraint sets are introduced in the optimization problem, by exploiting the state-dependent nature of the considered class of uncertainties. Moreover, unlike the nominal model predictive control algorithm, a stabilizing state constraint is imposed at the end of the control horizon in place of the usual terminal constraint posed at the end of the prediction horizon. The regional input-to-state stability of the closed-loop system is analyzed. A simulation example shows the effectiveness of the proposed approach.

Stabilization of Networked Control Systems by Nonlinear Model Predictive Control: A Set Invariance Approach

The present paper is concerned with the robust state feedback stabilization of uncertain discrete-time constrained nonlinear systems in which the loop is closed through a packet-based communication network. In order to cope with model uncertainty, time-varying transmission delays and and packet dropouts which typically affect networked control systems, a robust control policy, which combines model predictive control with a network delay compensation strategy, is proposed.

Robust Sinusoid Identification With Structured and Unstructured Measurement Uncertainties

In this note a globally stable methodology is proposed to estimate the frequency, phase, and amplitude of a sinusoidal signal affected by additive structured and bounded unstructured disturbances. The structured disturbances belong to the class of time-polynomial signals incorporating both bias and drift phenomena. Stability and robustness results are given by resorting to Input-to-State stability arguments. Simulation comparative results show the effectiveness of the proposed technique.

Networked predictive control of constrained nonlinear systems: Recursive feasibility and Input-to-State Stability analysis

The present paper is concerned with the robust state feedback stabilization of uncertain discrete-time constrained nonlinear systems in which the loop is closed through a packet-based communication network. In order to cope with model uncertainty, time-varying transmission delays and packet dropouts which typically affect networked control systems, a robust control policy, which combines model predictive control with a network delay compensation strategy, is proposed. The contribution of the paper is twofold. First, the issue of guaranteeing the recursive feasibility of the optimization problem associated to the receding horizon control law has been addressed, such that the invariance of the feasible region under the networked closed-loop dynamics can be guaranteed. Secondly, the Input-to-Stability property of the networked closed-loop system with respect to bounded perturbations has been analyzed.

Robust parametric identification of sinusoidal signals: An Input-to-State Stability approach

In this work, a robust method to estimate sinusoidal signals of unknown frequency, amplitude and phase is described. The stability properties of the devised estimation method under perturbed condition are studied by Input-to-State Stability (ISS) analysis. Compared to averaging approaches, the ISS-Lyapunov theory allows to study the stability for any value of adaptation parameters.

Robust receding - horizon control of nonlinear systems with state dependent uncertainties: An input-to-state stability approach

This paper is concerned with the robust receding horizon control of constrained discrete-time nonlinear systems affected by model uncertainty. A class of uncertainties entailing norm-bounded additive state dependent and non-state-dependent uncertainties is considered. In order to robustly enforce the constraints, a technique based on constraints tightening is formulated. Moreover, it is shown that the closed-loop system, obtained with the developed RH controller, is regionally input-to-state stable with respect to the considered class of uncertainties. A simulation example shows the effectiveness of the proposed approach.

Non-Asymptotic Kernel-Based Parametric Estimation of Continuous-Time Linear Systems

In this paper, a novel framework to address the problem of parametric estimation for continuous-time linear time-invariant dynamic systems is dealt with. The proposed methodology entails the design of suitable kernels of non-anticipative linear integral operators thus obtaining estimators showing, in the ideal case, “non-asymptotic” (i.e., “finite-time”) convergence. The analysis of the properties of the kernels guaranteeing such a convergence behaviour is addressed and a novel class of admissible kernel functions is introduced. The operators induced by the proposed kernels admit implementable (i.e., finite-dimensional and internally stable) state-space realizations. Extensive numerical results are reported to show the effectiveness of the proposed methodology. Comparisons with some existing continuous-time estimators are addressed as well and insights on the possible bias affecting the estimates are provided.

An Adaptive Observer-Based Switched Methodology for the Identification of a Perturbed Sinusoidal Signal: Theory and Experiments

This paper deals with a novel adaptive observer-based technique for estimating the amplitude, frequency, and phase of a single sinusoidal signal from a measurement affected by structured and unstructured disturbances. The structured disturbances are modeled as a time-polynomial so as to represent bias and drift phenomena typically present in applications, whereas the unstructured disturbances are modelled as bounded noise signals. The proposed estimation technique exploits a specific adaptive observer scheme equipped with a switching criterion allowing to properly address in a stable way poor excitation scenarios. The estimators stability properties are analyzed by input-to-state stability arguments. The practical characteristics of the proposed estimation approach are evaluated and compared with other existing tools by extensive simulation trials. Real experimental results are provided as well.

Approximate model predictive control laws for constrained nonlinear discrete-time systems: analysis and offline design

The objective of this work consists in the offline approximation of possibly discontinuous model predictive control laws for nonlinear discrete-time systems, while enforcing hard constraints on state and input variables. Obtaining an offline approximation of the receding horizon control law may lead to a very significant reduction of the online computational burden with respect to algorithms based on iterated optimization, thus allowing the application to fast dynamics plants. The proposed approximation scheme allows to cope with discontinuous control laws, such as those arising from constrained nonlinear finite horizon optimal control problems. A detailed stability analysis of the closed-loop system driven by the approximated state-feedback controller shows that the devised technique guarantees the input-to-state practical stability with respect to the (non-fading) approximation-induced errors. Two examples are provided to show the effectiveness of the method when the approximator is chosen either as a discontinuous nearest point function or as a smooth neural network.