Combined minimum entropy and output PDFS control via neural networks

By combining the entropy with the recent developed control strategies on the shape control of the output probability density function for dynamic stochastic systems, a new control algorithm is formulated for a class of unknown dynamic stochastic systems. The obtained control input minimizes a combined performance function for the closed loop system and can thus realizes the control of the shape of the output probability density functions and, at the same time, minimizes the system entropy so as to reduce the uncertainties for the closed loop system. Since the system considered is unknown, a neural network model is used online to update the optimal control input. This leads to an adaptive control framework for the closed loop control of the system. A simulated example is included to show the effectiveness of the proposed algorithm and encouraging results were obtained.