Jagdish Chandra

State-space self-tuning control for nonlinear stochastic and chaotic hybrid systems

This paper presents a new state-space self-tuning control scheme for adaptive digital control of continuous-time multivariable nonlinear stochastic and chaotic systems, which have unknown system parameters, system and measurement noises, and inaccessible system states. Instead of using the moving average (MA)-based noise model commonly used for adaptive digital control of linear discrete-time stochastic systems in the literature, an adjustable auto-regressive moving average (ARMA)-based noise model with estimated states is constructed for state-space self-tuning control of nonlinear continuous-time stochastic systems. By taking advantage of a digital redesign methodology, which converts a predesigned high-gain analog tracker/observer into a practically implementable low-gain digital tracker/observer, and by taking the non-negligible computation time delay and a relatively longer sampling period into consideration, a digitally redesigned predictive tracker/observer has been newly developed in this paper for adaptive chaotic orbit tracking. The proposed method enables the development of a digitally implementable advanced control algorithm for nonlinear stochastic and chaotic hybrid systems.

TRACKING CONTROL OF NONLINEAR SYSTEMS: A SLIDING MODE DESIGN VIA CHAOTIC OPTIMIZATION *

The output tracking for a general family of nonlinear systems presents formidable technical challenges. In this paper, we present a novel scheme for tracking control of a class of affine nonlinear systems with multi-inputs. This effective procedure is based on a new sliding mode design for tracking control of such nonlinear systems. The construction of an optimal sliding mode is a difficult problem and no systematic and efficient method is currently available. Here, we develop an innovative approach that utilizes a chaotic optimizing algorithm, which is then successfully applied to obtain the optimal sliding manifold. The existing efficient reaching law approach is then utilized to synthesize the sliding mode control law. The sliding mode control scheme proposed here is particularly appropriate for robust tracking of the chaotic motion trajectory.

Adaptive control for nonlinear stochastic hybrid systems with input saturation

This paper presents a new state-space self-tuning control scheme for adaptive digital control of continuous multivariable nonlinear stochastic hybrid systems with input saturation. The continuous nonlinear stochastic system is assumed to have unknown system parameters, system and measurement noises, and inaccessible system states. The proposed method enables the development of a digitally implementable advanced control algorithm for chaotic stochastic hybrid systems.

Self-tuning fault-tolerant digital PID controller for MIMO analogue systems with partial actuator and system component failures

A new methodology is presented to synthesize a digitally redesigned, active, self-tuning, fault-tolerant proportional–integral–derivative (PID) controller for multi-input–multi-output (MIMO) analogue systems to against partial actuator and system component failures. The fault-tolerant control (FTC) scheme possesses the ability to accommodate for system failures automatically and maintains the acceptable overall system performance in the event of partial actuator and system component failures. The theoretically well-designed analogue PID controller is refined using the continuous-time linear-quadratic regulator approach to have the high-gain property. Then, a predication-based digital redesign technique is utilized to discretize the cascaded MIMO analogue PID controller for finding a low-gain digital PID controller. Besides, a self-tuning FTC scheme with a modified Kalman filter algorithm is proposed, which is not only for the control system design but also for the faulty system recovery. The designed scheme can easily be implemented using digital processors. An illustrative example is presented to demonstrate the effectiveness of the proposed methodology.

Toward a Reliable and Resilient Mobile Wireless Architecture

Abstract Hybrid mobile wireless architectures that combine the advantages of ad hoc (mobile nodes) and cellular models provide “communications-on-the-move” services with enhanced flexibility and stability. In this article, we investigate the resiliency of such architectures by considering strategies for optimal deployment (number and location) of backup routers that would ensure reliable performance in such interdependent mobile systems.

IDENTIFICATION AND CONTROL OF CHAOTIC SYSTEMS VIA RECURRENT HIGH-ORDER NEURAL NETWORKS

ABSTRACT —In practice, most physical chaotic systems are inherently with unknown nonlinearities, and conventional adaptive control for such chaotic systems typically faces with formidable technical challenges. As a better alternative, we propose using the recurrent high-order neural networks to identify and control the unknown chaotic systems, in which the Lyapunov synthesis approach is utilized for tuning the neural network model parameters. The globally uniform boundedness of the parameters estimation errors and the asymptotical stability of the tracking errors are proved by Lyapunov stability theory and LaSalle-Yoshizawa theorem. This method, in a systematic way, enables stabilization of chaotic motion to a steady state as well as tracking of any desired trajectory. Computer simulation on a complex chaotic system illustrates the effectiveness of the proposed control method. Key Words : Chaotic systems; Adaptive control; Lyapunov function; LaSalle-Yoshizawa theorem 1. INTRODUCTION

Robust and resilient critical infrastructure systems

Seven papers are included in this minitrack, presented in two sessions. First session concentrates on assessment of vulnerabilities in interconnected networked systems. Topics include a survey of interdependencies in critical infrastructure systems, assessment of performance and optimal investments in such interconnected architectures, assessment of vulnerabilities for robust design, and design of survivable distributed systems. The second session concentrates on failure modes in interdependent critical infrastructures, with focus on dynamic and probabilistic approaches to specific infrastructure systems such as blackout vulnerability of power transmission grid and load-dependent cascading failures power grids. In a more general setting, the session also considers control and state estimation techniques for building trustworthy systems.

Introduction to the minitrack on hybrid dynamical systems

Hybrid systems are natural models of complex interactive networks such as manufacturing, communication, power, and transportation systems. Hybrid systems can be viewed as systems that allow interactions between discrete events and continuous dynamics. For example,in the context of power systems, the large disturbance behavior of such systems is characterized by complex interactions between continuous dynamics and discrete events. Components such as generators and loads drive the continuous behavior, while other components such as tap-changing transformers, switched shunts, and protective devices exhibit eventdriven behavior. A satisfactory theory for such systems ,which draws from several fields including control theory, computer science, and applied mathematics, will have an enormous potential for impact on a variety of application domains. The hybrid nature of the problem may also show up in the design and implementation of controls for such systems. As such, computational and algorithmic approaches to such problems encounter considerable difficulties. In addition to modeling and analysis of such systems, this session will explore novel computational implementations that can accommodate uncertainties in the system at various levels.