The Closed-Loop Feedback And Model Structure Analysis In The Application Of Multivariable Predictive Control

Decades of industrial experience shows that in the actual industry to obtain the complexprocess of accurate model is very difficult or even impossible. And the not accurate model willaffect the control effect seriously. The industry demands and the development of control theoryand computer technology, strongly promote the development of the advanced control technology.Model predictive control is one of the typical representative. It is easy to modeling androbustness is a significant advantage. So as to restrain the sensitivity of the algorithm for themodel parameters of the change effectively.However, although one of the advantages of model predictive control is not demanding theprecision of the model is very high. Usually, it does not require the steady-state gain with actualsituation equal completely, as long as the trend is consistent, it will bring good control effect, butthis is only a qualitative description. Under the description of the input and output model, it isdifficult to give the quantitative conclusions between the model mismatch and systemperformance. So, in the condition of existing the identification error, how can you still get thegood control effects will be a worth further discussing. This article through the methods of theclose-loop feedback and improve the model structure, to improve the control effect even in thecondition of existing model error. The main work and results are as follows:1．Aiming at the problem of the unsatisfactory control requirement while the dynamicmatrix control is merely used when the model is serious mismatched in model predictive control.An integration of closed-loop feedback for multivariable predictive control strategy is proposed.Firstly, we use the relative gain principle to judgment the coupling degree of multivariablesystem. Secondly, for the system of low levels coupling degree, the matching problem of theinput and output is discussed in the multi-input multi-output system, as well as the PID controlstrength in the control algorithm. Finally, we combin the dynamic matrix control algorithm with PID control, putting forward the method of close-loop feedback. It improves the robustness ofthe algorithms.2. The improved algorithm is used in a water level control experiment, including modelidentification, liquid level control, verifying the validity of the proposed algorithm.3. The effect of the model structural problems for multivariable model predictive control isconsidered. The singular value decomposition, condition number and relative gain are applied tomodel predictive control, analyzing the linear dependence of model structure，and coming upwith a method of judging model structure critical unstable. Finally, through the perturbationmethod to improve the structure of the model.