Design of stable model reference adaptive system via Lyapunov rule for control of a chemical reactor

Abstract

In this paper, two model reference adaptive control strategy including MIT rule and Lyapunov rule are used to design iterative learning controllers for a chemical-reactor system with uncertain parameters, initial output resetting error and input disturbance. The learning controller compensates for the unknown parameters, uncertainties, and nonlinearity using adaptation law which updates control parameters. It is shown that the internal signals remain bounded if we use a Lyapunov base algorithm, but the algorithm via MIT rule can!t guarantee the stability of system in all conditions. The output tracking error will converge to a profile which can be tuned by design parameters and the convergence speed is improved if the adaptation gain is large. The proposed control algorithm was simulated using MATLAB / Simulink software package to validate the performance of designed algorithm.