Research Of LMI Nonlinear Model Predictive Control Based On Polynomial Mode

With the development of modern industry and the progress of science and technology, the manufacturing process is changed to more complex and with strong nonlinear characteristics so that linear model predictive control (LMPC) may not always obtain satisfactory control results. Nonlinear model predictive control(NMPC) has become an important research issue in the control engineering field.After twenty years of development, much progress has been made in NMPC and some theory has reached a relative mature stage. While, comparing with the great success in the applications of the linear model predictive control, few applications of NMPC have been reported. This may attributes to the complexity of NMPC, and some fundamental problems of NMPC theory have not been well resolved yet. Among them, the problems of stability, robustness and efficiency of the optimization are the three ones, and make the main obstacles in the applications of NMPC. So the current NMPC researches still focus on the problems of stability, robust, and optimization efficiency.Firstly, brief introductions to advantage and disadvantage of the nonlinear model predictive control by analyzing the linear matrix inequation based on the general concept of polynomial mode. As the discussions pointed, the one of the main reasons for the small applicable domain of class algorithms are the stable constraint are too strong. Secondly, aiming at this problem the applicable areas are broadened through modulating the parameter. The algorithm is advanced on the basis of the conventional method, thereby effectively mend the problem which make the whole optimize problem become infeasible, the problem usually is formed duo to several the stable restriction condition go strong, by broadening the restriction condition and leaving adequate margin,ultimately influence the controller effect such as the attraction area, the response speed. Simultaneity, the algorithm which based on LMI design have the characteristics of smaller optimization calculation quantity and disposing definite model indeterminacy, therefore the algorithm can be applied to actual system. Finally, a great number of simulations are performed based on the antenna track system model such as nominal system stability, parameter to the influence of attraction area, robustness stability and so on. The experiment simulation result commendably achieve by the analysis of algorithm example.