Michael Chang

Switching control of a family of plants

In this paper, the adaptive control problem dealing with a family of MIMO LTI finite-dimensional plants recently examined by Miller and Davison (1990) is reconsidered. In particular, we present a new robust switching mechanism which requires less a priori system information than previously assumed in Miller-Davison. Simulation results using this new controller are also given, and are compared with the output responses obtained using the controllers presented in Miller-Davison and Morse.

Control of unknown systems using switching controllers: an experimental study

In this paper, the problem of implementing the Miller-Davison (1989) self-tuning integral robust servomechanism for the case of constant references and disturbances is considered and examined using an experimental multi-input multi-output apparatus; cases involving both known and unknown estimates of the steady state gain matrix T are considered. In order to improve the speed of response, proportional feedback has been added in both instances, and this has yielded a superior transient response.

Control of unknown systems using switching controllers: the self-tuning robust servomechanism problem

In this paper, new theoretical and simulated results of a self-tuning proportional-integral robust servomechanism (for the class of constant reference and disturbance signals) are given for cases involving both known and unknown estimates of the steady-state gain matrix T.<<ETX>>

Control of unknown MIMO systems using self-tuning PID controllers

In this paper, new theoretical and simulated results of a self-tuning proportional-integral-derivative (PID) robust servomechanism (for the class of constant reference and disturbance signals) are given, for cases involving both known and unknown estimates of the steady-state gain matrix /spl Tscr/.

Adaptive Proportional-Integral-Derivative Controllers with Control Input Constraints

Abstract When designing conventional controllers for multivariable systems, the general approach often adopted is to find a suitable nominal model for the plant, which is often a very difficult task, and then to design a controller based upon this model. If, however, large unexpected structural changes subsequently occur in the system, severe limitations in practical performance may occur since conventional control schemes usually do not have the ability to control systems which are subject to unplanned extreme changes. Moreover, for the realistic situation when a control input constraint exists, few results for continuous time multivariable systems are available. In this paper, a continuation of the authors earlier work is presented for a class of self-tuning proportional-integral-derivative controllers for open loop asymptotically stable MIMO systems. Results of this new controller when applied to several multivariable systems will also be described.

New Directions in Industrial Control — An Experimental Study Applied to a Multivariable Hydraulic System

In the operation of today’s modern industrial society, it is generally necessary to apply control to regulate/optimize the outputs of the system, in order to provide high quality outputs and to minimize the energy cost requirements for the system. Under nominal operating conditions, such controllers are generally found to be satisfactory. However, severe limitations in potential performance are often present in such systems, when large unexpected structural changes occur in the system, i.e., ’conventional’ control schemes generally do not have the ability to control systems which are subject to unplanned extreme changes; we will call controllers which have this ability as being ’intelligent’.