A Novel Nonlinear Model Predictive Control Strategy for a Class of Nonlinear Systems with Multiple Actuators’ Response Time-Delays

This paper investigates the problem of nonlinear model predictive control (NMPC) strategy for a class of nonlinear systems with multiple actuators’ response time-delays. Considering the response time-delays of the actuators when constructing NMPC problem will usually greatly increase the scale of the optimization problem. To address these problems, a novel NMPC strategy is designed. At the first stage, the NMPC strategy is designed for the nonlinear system without actuator’s response time-delay, which will not increase the scale of the optimization problem. The optimal control sequence solved by NMPC is polynomial fitted into a time-continuous polynomial function, which is used as the reference signal of the actuators’ response time-delay models. At the second stage, combining inverse model and inverse Laplace transform techniques, a novel inverse model compensation control (IMCC) strategy is designed for actuators’ response time-delays, which can track the reference signal without phase time-delay and amplitude deviation. In order to compare the performance of the proposed novel NMPC and IMCC algorithms, we also design the NMPC strategy by model augmentation to overcome the actuators’ response time-delays, which will increase the scale of the optimization problem. By quantitative analysis, the model augmentation NMPC strategy will increase the number of optimal variables and equality constraints of the optimization problem. Finally, vehicle control of transport vehicle in open-pit mine is taken as simulation example, the simulation results show that both the proposed novel NMPC and IMCC algorithms and model augmentation NMPC algorithm can achieve high precision control performance, the maximum and average calculation time of the proposed novel NMPC and IMCC algorithms are 31.9% and 46.2% lower than that of model augmentation NMPC algorithm, respectively.