Optimal control method for complex nonlinear systems based on disturbance observer
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
