Nonlinear Model Predictive Control with VPPSO Optimization for Complex Nonlinear System with Input Saturation

In real industrial production, saturation phenomena such as actuator saturation and state saturation are especially common in complex nonlinear systems. For nonlinear systems with input saturation, this paper proposes a control strategy based on nonlinear model predictive control (NMPC), and uses the velocity pausing particle swarm optimization (VPPSO) algorithm to optimize the parameters of NMPC. The proposed algorithm obtains the optimal control sequence under the input saturation constraints using the interior point method by adding input saturation as a hard constraint directly into the optimization solution of NMPC. By comparing with NMPC saturation algorithm with random parameters, model-free adaptive control(MFAC) saturation algorithm, and limiting proportional-integral-derivative (PID) control algorithm, it is verified that the proposed algorithm has a better control effect when dealing with nonlinear systems with input saturation.