Multivariable Process Identification And Decoupling Control Based On Intelligent Method

Multivariable coupling phenomenon is existed widely in industry practice. In order to control multivariable systems accurately, it is needed to solve accuracy problem of system models. The method of system identification for particle swarm optimization (PSO) algorithm is used by contrast with traditional system identification. Using identified system models as predictive models, the multivariable decoupling control method of intelligent predictive function is introduced.The PSO algorithm is used in multivariable model identification, which turned system identification problems into optimization problems in parameter space by qualitatively analyze the scope of system parameter space. The PSO algorithm can effectively avoid getting into local optimum and is used to obtain the optimal solution by searching in the whole parameter space in parallel. The simulations were done for different model examples. The experiment results know that PSO algorithm is an effective method that performed better than genetic algorithm (GA) for system identification.In the aspect of MIMO systems decoupling control, a multivariable predictive function decoupling control (PFDC) algorithm is proposed. This algorithm can decompose the decoupling control problem of an MIMO system into predictive function controls (PFC) of several SISO systems. The decentralized optimization method was adopted to deal with coupled variables instead of the whole optimization. Utilizing the characteristics of PFC, base functions increase freedom of design, reduce the online calculation amount significantly, and thus efficiently simplify parameter design and the calculation load. Then, an analytical linear decoupling control equation can be derived with the PFDC algorithm, and the controller parameters can be calculated off-line. Therefore, the obtained algorithm is simple and can solve complicated high dimensions multivariable coupling control problems. The simulations were done for multivariable coupling systems of the first order plus time delay and the second order plus time delay, which show that the proposed control system is efficient and effective.Regarding to the model mismatch caused by randomicity and uncertainty of multivariable system processes, the intelligent PFDC is proposed, which mainly include fuzzy PFDC (F-PFDC), GA-PFDC and PSO-PFDC.The method of F-PFDC is importing fuzzy mathematics into PFDC. By using the fuzzy modeling method, it solves the instability in control process which is caused by severe model mismatch. GA and PSO have excellent effect in parameter optimization, which can be applied to mismatched model optimization. Taking optimized models as predictive models, GA-PFDC and PSO-PFDC are composed. The mentioned methods effectively get ride of the model mismatch and system uncertainty, and have good real-time performance. Via the comparison of practical industry model simulation and self-turning PID control, the proposed control system is efficient and effective.