Infinite horizon robust linear Minkowski regulator

The authors present a sufficient condition for quadratic stabilizability of uncertain linear discrete time systems using LQ (linear quadratic) regulators. The true system is represented by a nominal model plus additive terms representing the uncertainties in the state and input matrices. With a proper choice of these terms, one may represent the most common types of uncertainties encountered in the literature. The main result of the present work is a robustification procedure for discrete time LQ regulators, which is based on a sufficient condition for quadratic stabilizability. The conservativeness of the proposed condition is discussed and some examples are given. >

In this paper, we consider the regulation problem for uncertain linear discrete time systems with bounded disturbance, bounded input and bounded output. Based on the input-output representation, an extended state space model is constructed via the delayed inputs and outputs of the systems and hence there is no need for any estimate of the unmeasured states. The control is obtained using an interpolation technique, which assures feasibility and a robustly asymptotically stable closed loop behavior.

A new sufficient condition is proposed for robust root clustering in an arbitrary subregion of the complex plane for both linear continuous time and linear discrete time uncertain systems. The systems are described by state space models with structured parameter uncertainty. Explicit bounds on uncertain parameters are derived to guarantee root clustering inside any specified region which may be demanded by stability and performance robustness. Illustrative examples show that the results of the proposed method can be much less conservative compared with the results of existing methods

In this paper, the Bellman equation is used to solve the optimal adaptive control of quantized linear discrete-time system with unknown dynamics. To mitigate the effect of the quantization errors, the dynamics of the quantization error bound and an update law for tuning the range of the dynamic quantizer are derived. Subsequently, by using adaptive dynamic programming technique, the infinite horizon optimal regulation problem of the uncertain quantized linear discrete-time system is solved in a forward-in-time manner without using value and/or policy iterations. The asymptotic stability of the closed-loop system is verified by standard Lyapunov stability approach in the presence of state and input quantizers. The effectiveness of the proposed method is verified via simulation.

In this paper, a class of discrete-time uncertain linear hybrid systems, which is affected by both parameter variations and exterior disturbances, is considered. The goal here is to synthesize a hybrid control law, which includes the continuous variable control law and the discrete event control signal, so as to guarantee the controlled invariance for a specific region in the hybrid state space. It turns out that the continuous variable control law is a piecewise linear state feedback control based on the partition of the given region's continuous variable state space projection. A numerical example is given for illustration.

This paper studies the design of uncertain linear discrete-time Networked Control Systems over imperfect communication channels with time-varying data loss. This leakage is modeled as stochastic parameter and norm-bounded uncertainties. In addition, this paper defines the necessary conditions for the design of the dynamic output feedback controller; subsequently, an iterative LMI approach is specified to compute the controller state parameters. The simulation results show that the designed controller improves the H∞ disturbance attenuation performance of the concerned uncertain system.

The regulator problem for linear uncertain continuous-time systems having control constraints is considered. Necessary and sufficient conditions of positive invariance of polyhedral domains are extended to the case of continuous-time uncertain systems. Robust constrained regulators are then derived. An application to the control of a stirred tank pH is then achieved. First, the process is modeled as models studied hereafter, then a robust constrained regulator is calculated. Simulation results show that this control law is easy to implement and guarantees robust asymptotic stability and control admissibility.

Robust linear controller design of uncertain discrete systems with time-varying delay

An adaptive control framework for stabilization of 1-dimensional linear uncertain discrete-time dynamical systems with symbolic feedback is developed. The plant output is assumed to be quantized and only its symbolic quantity is fed back to the controller. In the case of multi-dimensional systems, ideal system matrix is constructed so that the approach for the scalar systems can be applied in separate subspaces in the state space.

In this study, an optimal adaptive control approach is established to solve the robust output tracking problem of a class of continuous time uncertain linear systems based on the policy iteration (PI) in actor-critic algorithm. First, by augmenting the integral variables of the tracking error into state variables, the robust tracking problem is transformed into a robust control problem of an augmented uncertain linear system. It is proven that the robust control law of the augmented system enables the output of the considered system to track a polynomial time signal asymptotically. Second, an optimal control method in the corresponding auxiliary nominal system is established, and based on the Bellman optimality principle, PI algorithms are proposed to solve online tracking controllers for the matched and the mismatched uncertain systems. Finally, for testing the availability of the proposed approach and theoretical results, two numerical experiments are provided.

This paper presents a method for assigning the poles in a specified disk by state feedback for a linear discrete or continuous time uncertain system, the uncertainty being norm bounded. For this the "quadratic d stabilizability" concept which is the counterpart of quadratic stabilizability in the context of pole placement is defined and a necessary and sufficient condition for quadratic d stabilizability derived. This condition expressed as a parameter dependent discrete Riccati equation enables one to design the control gain matrix by solving iteratively a discrete Riccati equation. >

Robust H∞ control for uncertain discrete-time state-delayed linear systems with markovian jumping parameters

Backward reachability analysis is essential to synthesizing controllers that ensure the correctness of closed-loop systems. This article is concerned with developing scalable algorithms that underapproximate the backward reachable sets, for discrete-time uncertain linear and nonlinear systems. Our algorithm sequentially linearizes the dynamics and uses constrained zonotopes for set representation and computation. The main technical ingredient of our algorithm is an efficient way to underapproximate the Minkowski difference between a constrained zonotopic minuend and a zonotopic subtrahend, which consists of all possible values of the uncertainties and the linearization error. This Minkowski difference needs to be represented as a constrained zonotope to enable subsequent computation, but, as we show, it is impossible to find a polynomial-size representation for it in polynomial time. Our algorithm finds a polynomial-size underapproximation in polynomial time. We further analyze the conservatism of this underapproximation technique and show that it is exact under some conditions. Based on the developed Minkowski difference technique, we detail two backward reachable set computation algorithms to control the linearization error and incorporate nonconvex state constraints. Several examples illustrate the effectiveness of our algorithms.

This paper considers a subclass of hybrid systems, which is the discrete-time linear switching systems whose changes in the dynamics are determined by exogenous, uncontrollable events, and resets are present. In the presence of uncertain parameter and non-symmetrical constrained control, based on the results of nominal linear switching systems, a necessary and sufficient condition to test the robustness of a state feedback controller is given. The candidate state feedback controller for this test is that stabilizing nominal discrete-time linear switching systems. Also, the method in this paper can be extended to the case which has state constraints. At last, a numerical example is presented to illustrate the technique.

This study is concerned with the robust energy-to-peak (l2–l∞) filtering problem for a class of uncertain linear discrete-time networked control systems with time-varying delays and randomly missing data. The system matrices are assumed to reside in a convex polytope, and the time-varying delays appearing in system states are assumed to lie in a given interval. Moreover, the random data missing is supposed to satisfy the Bernoulli random binary distribution. The authors' goal is to design a full-order filter such that the filtering error dynamic system is exponentially mean-square stable for all admissible time delays and randomly missing data while a prescribed l2–l∞ performance is achieved. Sufficient delay- and parameter-dependent conditions for the existence of the filter and for the solvability of the addressed problem are given in terms of a set of linear matrix inequalities. Finally, simulation examples and comparison studies are provided to demonstrate the effectiveness of the proposed approach.