An Adaptive Position Control Algorithm of a DC Motor based on the First Order Approximation using Recursive Least Squares with Forgetting

This paper describes an adaptive position control algorithm of a DC motor based on the first order approximation using recursive least squares with forgetting. The main concept of the proposed control algorithm is that the relationship between single-input and single-output of the system can be approximated as a first order system in a particular time region. The approximated first order system can be used for optimal control instead of actual complicated DC motor dynamics. In order to approximate the input and output relationship of the system, the method of recursive least squares with forgetting has been used. The disturbance observer has been employed to estimate the error between actual DC motor system and approximated first order system. Based on the approximated first order system that represents the DC motor dynamics, the optimal voltage input has been computed using the Lyapunov direct method. The actual DC motor system equipped with encoder has been used for performance evaluation of the proposed control algorithm. The results show good control performance in terms of error convergence and adaption ability.