Data-Driven Model-Predictive Control for Nonlinear Systems With Stochastic Sampling Interval
Honggui Han, Shijia Fu, Haoyuan Sun, Junfei Qiao
Abstract
Stochastic sampling is ubiquitous for the complex nonlinear systems, which may destroy the stability of the closed-loop system. Concerned with the sampled-data stabilization problem for the nonlinear systems with complex dynamics and stochastic sampling, a data-driven model-predictive control (DDMPC) strategy is proposed to control the system at each sampling instant. The main merits of DDMPC are threefold. First, a data-driven method based on the fuzzy neural network (FNN) is used to establish the mathematical model for the complex nonlinear system. Especially, considering the stochastic sampling characteristics of the system, a multimodel structure is constructed to predict the future system outputs at possible sampling instants. Second, the objective function is designed to deal with the stochastic sampling control problem. The reasonable control laws are computed to stabilize the nonlinear system with stochastic sampling. Finally, the stability of DDMPC is proved in theory, and the numerical simulation results and industrial application for the wastewater treatment process (WWTP) reveal that the proposed DDMPC can achieve a satisfactory control performance for the stochastic sampling nonlinear system (SSNS).