Neural Network Application To Optimal Control Of Nonlinear Systems

Abstract

This paper presents the derivation of the numerical algorithm for optimal control of nonlinear multivariable systems with control and state vectors constraints. The algorithm derivation is based on the backpropagationthrough-time (BPTT) algorithm which is used as a learning algorithm for recurrent neural networks. This approach is not based on Lagrange multiplier techniques and the calculus of variations. The derived algorithm is used for the control of the cooperative work of two robots with two degrees of freedom. The main problem is the determination of the control vectors of robots for the transfer of rigid load from the initial state to the final one in a fixed time while maintaining constant distance between robot hands and avoiding the cross-section of the robot hands.