Robust control for nonlinear discrete-time systems with quantitative input to state stability requir

In this paper, we consider state feedback robust control problems for discrete-time nonlinear systems subject to disturbances. The objective of the control is to minimize a performance function while guaranteeing a prescribed quantitative input to state stability (ISS) property for the closed-loop systems. By introducing the concept of ISS control invariant set, a sufficient condition for the problem to be feasible is given. Built on the sufficient condition, a computationally efficient control design algorithm based on one-step min-max optimization is developed. An example is given to illustrate the proposed strategy. Copyright © 2007 International Federation of Automatic Control All Rights Reserved

Analysis of input-to-state stability for discrete time nonlinear systems via dynamic programming

The input-to-state stability (ISS) property for systems with disturbances has received considerable attention over the past decade or so, with many applications and characterizations reported in the literature. The main purpose of this paper is to present analysis results for ISS that utilize dynamic programming techniques to characterize minimal ISS gains and transient bounds. These characterizations naturally lead to computable necessary and sufficient conditions for ISS. Our results make a connection between ISS and optimization problems in nonlinear dissipative systems theory (including L2-gain analysis and nonlinear H∞ theory). As such, the results presented address an obvious gap in the literature. © 2005 Elsevier Ltd. All rights reserved

On stability and stabilization of periodic discrete-time systems with an application to satellite attitude control

An alternative stability analysis theorem for nonlinear periodic discrete-time systems is presented. The developed theorem offers a trade-off between conservatism and complexity of the corresponding stability test. In addition, it yields a tractable stabilizing controller synthesis method for linear periodic discrete-time systems subject to polytopic state and input constraints. It is proven that in this setting, the proposed synthesis method is strictly less conservative than available tractable synthesis methods. The application of the derived method to the satellite attitude control problem results in a large region of attraction

Stability and convergence properties of forced infinite-dimensional discrete-time Lur'e systems

Incremental stability and convergence properties for forced, infinite-dimensional, discrete-time Lur'e systems are addressed. Lur'e systems have a linear and nonlinear component and arise as the feedback interconnection of a linear control system and a static nonlinearity. Discrete-time Lur'e systems arise in, for example, sampled-data control and integro-difference models. We provide conditions, reminiscent of classical absolute stability criteria, which are sufficient for a range of incremental stability properties and input-to-state stability (ISS). Consequences of our results include sufficient conditions for the converging-input converging-state (CICS) property, and convergence to periodic solutions under periodic forcing

Robust Fault Tolerant Control for Discrete-Time Dynamic Systems With Applications to Aero Engineering Systems

Unexpected faults in actuators and sensors may degrade the reliability and safety of aero engineering systems. Therefore, there is motivation to develop integrated fault tolerant control techniques with applications to aero engineering systems. In this paper, discrete-time dynamic systems, in the presence of simultaneous actuator/sensor faults, partially decoupled unknown input disturbances, and sensor noises, are investigated. A jointly state/fault estimator is formulated by integrating an unknown input observer, augmented system approach, and optimization algorithm. Unknown input disturbances can be either decoupled by an unknown input observer, or attenuated by a linear matrix inequality optimization, enabling the estimation error to be input-to-state stable. Estimator-based signal compensation is then implemented to mitigate adverse effects from the unanticipated actuator and sensor faults. A pre-designed controller, which maintains normal system behaviors under a fault-free scenario, is allowed to work along with the presented fault tolerant mechanism of the signal compensations. The fault-tolerant closed-loop system can be ensured to mitigate the effects from the faults, guarantee the input-to-state stability, and satisfy the required robustness performance. The proposed fault estimation and fault tolerant control methods are developed for both discrete-time linear and discrete-time Lipschitz nonlinear systems. Finally, the proposed techniques are applied to a jet engine system and a flight control system for simulation validation

Qualitative Studies of Nonlinear Hybrid Systems

A hybrid system is a dynamical system that exhibits both continuous and discrete dynamic behavior. Hybrid systems arise in a wide variety of important applications in diverse areas, ranging from biology to computer science to air traffic dynamics. The interaction of continuous- and discrete-time dynamics in a hybrid system often leads to very rich dynamical behavior and phenomena that are not encountered in purely continuous- or discrete-time systems. Investigating the dynamical behavior of hybrid systems is of great theoretical and practical importance. The objectives of this thesis are to develop the qualitative theory of nonlinear hybrid systems with impulses, time-delay, switching modes, and stochastic disturbances, to develop algorithms and perform analysis for hybrid systems with an emphasis on stability and control, and to apply the theory and methods to real-world application problems. Switched nonlinear systems are formulated as a family of nonlinear differential equations, called subsystems, together with a switching signal that selects the continuous dynamics among the subsystems. Uniform stability is studied emphasizing the situation where both stable and unstable subsystems are present. Uniformity of stability refers to both the initial time and a family of switching signals. Stabilization of nonlinear systems via state-dependent switching signal is investigated. Based on assumptions on a convex linear combination of the nonlinear vector fields, a generalized minimal rule is proposed to generate stabilizing switching signals that are well-defined and do not exhibit chattering or Zeno behavior. Impulsive switched systems are hybrid systems exhibiting both impulse and switching effects, and are mathematically formulated as a switched nonlinear system coupled with a sequence of nonlinear difference equations that act on the switched system at discrete times. Impulsive switching signals integrate both impulsive and switching laws that specify when and how impulses and switching occur. Invariance principles can be used to investigate asymptotic stability in the absence of a strict Lyapunov function. An invariance principle is established for impulsive switched systems under weak dwell-time signals. Applications of this invariance principle provide several asymptotic stability criteria. Input-to-state stability notions are formulated in terms of two different measures, which not only unify various stability notions under the stability theory in two measures, but also bridge this theory with the existent input/output theories for nonlinear systems. Input-to-state stability results are obtained for impulsive switched systems under generalized dwell-time signals. Hybrid time-delay systems are hybrid systems with dependence on the past states of the systems. Switched delay systems and impulsive switched systems are special classes of hybrid time-delay systems. Both invariance property and input-to-state stability are extended to cover hybrid time-delay systems. Stochastic hybrid systems are hybrid systems subject to random disturbances, and are formulated using stochastic differential equations. Focused on stochastic hybrid systems with time-delay, a fundamental theory regarding existence and uniqueness of solutions is established. Stabilization schemes for stochastic delay systems using state-dependent switching and stabilizing impulses are proposed, both emphasizing the situation where all the subsystems are unstable. Concerning general stochastic hybrid systems with time-delay, the Razumikhin technique and multiple Lyapunov functions are combined to obtain several Razumikhin-type theorems on both moment and almost sure stability of stochastic hybrid systems with time-delay. Consensus problems in networked multi-agent systems and global convergence of artificial neural networks are related to qualitative studies of hybrid systems in the sense that dynamic switching, impulsive effects, communication time-delays, and random disturbances are ubiquitous in networked systems. Consensus protocols are proposed for reaching consensus among networked agents despite switching network topologies, communication time-delays, and measurement noises. Focused on neural networks with discontinuous neuron activation functions and mixed time-delays, sufficient conditions for existence and uniqueness of equilibrium and global convergence and stability are derived using both linear matrix inequalities and M-matrix type conditions. Numerical examples and simulations are presented throughout this thesis to illustrate the theoretical results

Semiglobal exponential input-to-state stability of sampled-data systems based on approximate discrete-time models

Several control design strategies for sampled-data systems are based on a discrete-time model. In general, the exact discrete-time model of a nonlinear system is difficult or impossible to obtain, and hence approximate discrete-time models may be employed. Most existing results provide conditions under which the stability of the approximate discrete-time model in closed-loop carries over to the stability of the (unknown) exact discrete-time model but only in a practical sense, meaning that trajectories of the closed-loop system are ensured to converge to a bounded region whose size can be made as small as desired by limiting the maximum sampling period. In addition, some sufficient conditions exist that ensure global exponential stability of an exact model based on an approximate model. However, these conditions may be rather stringent due to the global nature of the result. In this context, our main contribution consists in providing rather mild conditions to ensure semiglobal exponential input-to-state stability of the exact model via an approximate model. The enabling condition, which we name the Robust Equilibrium-Preserving Consistency (REPC) property, is obtained by transforming a previously existing consistency condition into a semiglobal and perturbation-admitting condition. As a second contribution, we show that every explicit and consistent Runge-Kutta model satisfies the REPC condition and hence control design based on such a Runge-Kutta model can be used to ensure semiglobal exponential input-to-state stability of the exact discrete-time model in closed loop.Comment: 10 page

Finite-horizon optimal control of linear and a class of nonlinear systems

Traditionally, optimal control of dynamical systems with known system dynamics is obtained in a backward-in-time and offline manner either by using Riccati or Hamilton-Jacobi-Bellman (HJB) equation. In contrast, in this dissertation, finite-horizon optimal regulation has been investigated for both linear and nonlinear systems in a forward-in-time manner when system dynamics are uncertain. Value and policy iterations are not used while the value function (or Q-function for linear systems) and control input are updated once a sampling interval consistent with standard adaptive control. First, the optimal adaptive control of linear discrete-time systems with unknown system dynamics is presented in Paper I by using Q-learning and Bellman equation while satisfying the terminal constraint. A novel update law that uses history information of the cost to go is derived. Paper II considers the design of the linear quadratic regulator in the presence of state and input quantization. Quantization errors are eliminated via a dynamic quantizer design and the parameter update law is redesigned from Paper I. Furthermore, an optimal adaptive state feedback controller is developed in Paper III for the general nonlinear discrete-time systems in affine form without the knowledge of system dynamics. In Paper IV, a NN-based observer is proposed to reconstruct the state vector and identify the dynamics so that the control scheme from Paper III is extended to output feedback. Finally, the optimal regulation of quantized nonlinear systems with input constraint is considered in Paper V by introducing a non-quadratic cost functional. Closed-loop stability is demonstrated for all the controller designs developed in this dissertation by using Lyapunov analysis while all the proposed schemes function in an online and forward-in-time manner so that they are practically viable --Abstract, page iv