Implementation and comparative study of Model Reference Adaptive Control algorithms for Slow Processes

Adaptive control involves modifying the control law used by the controller to cope with the fact that the parameters of the system being controlled change drastically due to change in environmental conditions or change in system itself. This paper deals with application of model reference adaptive control schemes and the system performance is compared with direct and indirect approaches. The plant which is taken for the controlling purpose is the linear level process. The comparison is done using different criteria such as plant parameter variation, noise rejection, model structure mismatching, and sensitivity to adaptation gains. Simulation is done in MATLAB and Simulink and the results are compared for the same model using different adaptive algorithms.

Implementation and comparative study of Model Reference Adaptive Control algorithms for Slow Processes Keywords:

level process , Model reference adaptive control , Lyapunov theory , Gradient method