A neurodynamic optimization approach to nonlinear model predictive control

Abstract

This paper presents a recurrent neural network (RNN) approach to nonlinear model predictive control (MPC). By using decomposition, the original optimization associated with nonlinear MPC is reformulated as a quadratic programming problem with unknown parameters. We employ an RNN and develop a learning algorithm for solving the formulated problem. The proposed RNN approach has many desirable properties such as global convergence and low complexity. Finally, we apply the neurodynamic approach to mobile robot navigation to demonstrate its effectiveness and efficiency.