Research On Predictive Control Theory For Comprehensive State Extension Design And Novel Constraint Processing Method

Due to the advantages of convenience in dealing with various constraints,low requirements in model accuracy,and so on,model predictive control(MPC)has acquired a lot of progress in both theory and applications.However,disturbances,uncertainties and constraints on the key variables exist in industrial systems inevitably,which cause the control performance of relevant controller to deteriorate to be unacceptable.Therefore,the improvement of MPC strategies control performance is strictly required.Aiming at the control problem of the above industrial processes,this paper proposes the research of the extended state space model predictive control method.The main contents are as follows:(1)The background and significance of the proposed predictive control method based on extended state space model are introduced.The related research contents at home and abroad are summarized,and the basic knowledge of this paper is given.(2)For the traditional constrained model predictive control algorithm based on quadratic programming(QP),when the constraints are strict,feasible solutions may not be obtained,which will deteriorate the performance of the control system,an improved constrained predictive function control method based on the extended state space model is presented to ensure that the quadratic programming problem always has acceptable solutions under various conditions.To improve the control effect of the presented strategy,first,an extended state-space model in which the changes of the output predictions can be regulated to achieve smoother system dynamics is constructed.Then,a modified constraint dealing scheme,which integrates the relaxation factors to improve the success probability of solving the relevant optimization,is also introduced for the proposed method.On the basis of the two merits,better ensemble control performance is anticipated.Simulations on the regulation of the NOx concentration in the denitration process prove the validity of the proposed constrained PFC strategy.(3)For polytopic description systems with uncertainties,a novel design of robust constrained model predictive tracking control is proposed.Based on the proposed new model,the process state variables and tracking error are combined such that they can be tuned in the cost function optimization separately,which can provide more degree of freedom for the controller design.On this basis,the performance index is decomposed into two parts,and the constrained optimization problem in the infinite horizon is transformed into a convex optimization problem including LMIs,a free control variable plus a robust constrained MPC based on linear feedback law is constructed,thereby reduce the complexity of the algorithm and enhancing the control performance of the system.The relevant feasibility and robust stability issues are further discussed,and the effectiveness of the proposed approach is tested on the control of a system which is open-loop unstable with dead time and reverse responses.