SUB OPTIMAL CONTROL OF LINEAR DYNAMIC SYSTEM USING REDUCED ORDER MODELS

Abstract

The implementation of an optimal control law (minimLsing a quadratic performance index) for linear time—invariant plants usually require the complete measurement of the plant state vector. Hence the optimal control problem will most likely fail to have a closed loop solution if some of the state variables are not available for feedback. Statistical estimation of the missing state variables using Kalman filtering techniques or their reconstruction using Luenberger type observers increase the order of the system and the resultant controller becomes more complex. Using model reduction techniques, two methods are given for arriving at a suboptimal controller using the reduced models. By the method of Pade approximation, dynamic feedback as well as precompensators may be easily incorporated to have an acceptable though suboptimal. responses. The chapter I gives the general introduction of various reduction method i.e. the literature survey. Chapter II describes the linear optimal control and the two reduction techniques i.e., Pade's approximation technique and the continued fraction expansion technique of Chen and Shieh. Chapter III gives information on aggregation matrix. Chapter IV gives the process of PID controller. design. Chapter V contains information about the software developed and the function of various subroutines. Program listing is given in the Appendix.