High-Precision Quick Control in Multivariable Time-Varying Nonlinear System: A Biological Decision Model Predictive Control Algorithm

To solve the problem of unsatisfactory control and poor real-time performance of nonlinear time-varying multi-input systems, this article proposes an intelligent model predictive control (MPC) algorithm inspired by heuristic dynamic programming (HDP), biological control theory, and operations research. Considering that the internal feedback information from a neural network (NN) is low, a multilevel feedback NN is proposed. Combining an NN with a biofeedback mechanism increases the internal feedback information and improves the convergence accuracy of the NN. The multilevel feedback network is used in three internal networks of the intelligent MPC algorithm. In order to improve the convergence speed of the proposed algorithm, a biologically inspired central coordination module and operations research theory inspired priority factor module is incorporated within the HDP algorithm. The prediction accuracy and control speed of the algorithm for nonlinear time-varying systems is greatly improved without affecting the control accuracy. The stability and convergence of the intelligent MPC algorithm is demonstrated on test data. Finally, the effectiveness and superiority of the proposed MPC algorithm is verified and compared against several traditional algorithms.