Robust adaptive control and parameter estimation using multi objective evolutionary algorithm

Abstract

This paper develops an effective algorithm for simultaneous robust adaptive control (filtering) and 
unknown parameter estimation. The need for adaptive control of poorly known systems is recognized 
in a diverse range of applications from active noise cancellation and structural vibration isolation to 
temperature control in process industry, to equalization in communication systems. Due to the nonlinear 
dependency of the problem on the unknown parameters, an exact solution to this problem is not known 
to date. The approach discussed here uses a robust adaptive filtering/control algorithm to fulfill the control 
objectives while a structured search algorithm (Evolutionary Algorithm) identifies the unknown (possibly 
changing) parameters. This approach overcomes unnecessary approximations that are commonly adopted 
in the existing solutions by casting the nonlinear parameter estimation problem as an optimization problem. 
Furthermore, the evolutionary algorithm formulation provides the framework to include the robustness of 
parameter estimates as an explicit objective in the optimization problem. Robust parameter estimates are 
of added significance when they are used in a feed-forward control path. Simulation results are presented 
to demonstrate the feasibility, performance, and the main features of the proposed approach.