Explicit Neural Network-Based Nonlinear Predictive Control with Low Computational Complexity

This paper describes a nonlinear Model Predictive Control (MPC) algorithm based on neural models. Two neural models are used on-line: from a dynamic model the free trajectory (the influence of the past) is determined, the second neural network approximates the time-varying feedback law. In consequence, the algorithm is characterised by very low computational complexity because the control signal is calculated explicitly, without any on-line optimisation. Moreover, unlike other suboptimal MPC approaches, the necessity of model linearisation and matrix inversion is eliminated. The presented algorithm is compared with linearisation-based MPC and MPC with full nonlinear optimisation in terms of accuracy and computational complexity.