Minimum Entropy Control for Non-Gaussian Stochastic Networked Control Systems and Its Application to a Networked DC Motor Control System

In this brief, a new tracking control algorithm for a class of networked control systems (NCSs) with non-Gaussian random disturbances and delays is proposed. Due to non-Gaussian random noises involved in the systems, solely controlling the expected value of the linear quadratic performance index is insufficient to obtain a satisfactory optimal control algorithm. The proposed method in this note applies the (h, φ)-entropy of the quadratic performance index to characterize the randomness of the closed-loop system. In order to calculate the entropy, the formulation of the joint probability density functions (JPDFs) of the quadratic performance index is presented in terms of known JPDFs of disturbances and time-delay. By minimizing the entropy of the performance index, a new control algorithm is obtained for the considered nonlinear and non-Gaussian NCSs. In addition, the proposed control strategy is applied to a networked DC motor control system, which is subjected to non-Gaussian random disturbances and delays. The experimental results show the effectiveness of the obtained method.