ABSTRACT
This chapter deals with some novel ideas in self-tuning control and discusses the robustness studies. Shieh et al. propose a state space approach for self-tuning control of a class of multivariable stochastic systems having the same number of inputs as outputs. The chapter considers some methods of improvement of control behavior during the adaptation phase. It explains the nonlinear systems and stochastic systems and also deals with multivariable systems. A stochastic adaptive controller is an algorithm which combines on-line parameter estimation with on-line control to generate a control law which can be applied to systems having unknown parameters and random disturbances. The chapter also considers unexplored area of distributed parameter systems and argues the model updating, which seems to have a lot of promise. The chapter examines the important issue of persistent excitation. The concept of persistent excitation is of great importance in many contexts in adaptive systems.
