To improve the control performance and operational performance of complex nonlinear systems with unknown disturbances,an optimal control method for complex nonlinear systems based on disturbance observer(IOCM)is proposed.In order to obtain a more accurate system prediction model,a model approximator based on fuzzy neural networks is designed to capture the nonlinear dynamics,and a disturbance observer is used to describe the unknown disturbances.Then,within the framework of multi-objective model predictive control,an optimal control structure with collaborative cost function and multi-gradient algorithm is proposed to comprehensively solve set-points and control laws.The effectiveness of the method is verified using bench-mark simulation model 1(BSM1)of municipal wastewater treatment process.Experimental results show that the average effluent quality(EQ)is 6 711 mg/L,and the average operating energy consumption(EC)is 3 805 kW·h under rainstorm weather conditions.Compared with other step-by-step optimal control methods,IOCM has better robustness and can improve the optimization control performance of nonlinear systems.
nonlinear systemmodel approximatordisturbance observeroptimal control method for com-plex nonlinear systems
国家科技期刊平台
NSTL
万方数据
