Inferential Self-tuning Controller And Global Convergence Analysis

A new inferential self-tuning controller is presented for inferring unmeasured controlled output and increasing the performance of the control system by using double measurements outputs. This controller uses the strategy of the generalized minimum variance control law and inferential strategy adopting double measurements as auxiliary outputs. It is adaptively implemented in direct form. It can guarantee the variance of difference of the controlled output and the ideal output to tend to minimum. The analysis of stability and globally convergence of this controller is also given. It is shown that even for non-minimum phase systems, this adaptive algorithm has globally convergence properties. The results of simulations is proposed to demonstrate the effectiveness of the algorithm.