Research On Nonlinear Predictive Control And Its Simulation

Predictive control is a class of digital control algorithms developed from industrial process in the late 1970's. The popularity of these methods is due to the facts that they can offer good performance, be understood and formulated easily and robustness to the model uncertainty. So far, the theory of linear predictive control has matured considerably, but it dose not apply to the strong nonlinear objects where nonlinear MPC is required. Nonlinear MPC is rather difficult both in theory and in application because of its complexity. Therefore, the study of nonlinear MPC algorithm is of great significance. Based on the factual problems in industrial process, nonlinear predictive control is researched profoundly in this paper.Firstly, a primary algorithm theory of generalized predictive control is introduced. In order to solve the problem that the coefficient of random disturbing item can't be estimated on-line. A kind of Generalized Predictive Control (GPC) is researched, and filter is adopted in GPC. The specific algorithm is put forward, and the function of the filter is summarized by simulating GPC. Then the selection of GPC parameters is analyzed rationally in the paper, and the method of selecting main parameters is discussed to make GPC more perfect.Secondly, based on non-linear system's structure and characteristics, two methods of moving horizon optimizations are put forward, both of the methods are suitable for non-linear predictive control. One is the method of two-step, and Hammerstein model is adopted in the method. In this strategy, MPC problem is divided into a dynamic optimization problem upon linear model and a static rooting problem of nonlinear algebraic equation. The Generalized Predictive Control algorithm is firstly applied to the linear part of the model, and then the required control variable is obtained by extracting the root of the nonlinear equation. Based on the non-linear equation without real root, two approximate calculation methods are offered, and two methods are compared with each other. The other is the method of entire solution; genetic algorithm is adopted in the method. In the algorithm, controlling variables are coded directly nonlinear part is included in performance indexes as a target function to solve the control law. Real-time problem is solved by improving the quality of initial stock in algorithm.Through the simulation of several typical examples, the feasibility of the nonlinear MPC algorithms and the correlative improved measures presented in this paper are proved, and the advantages and disadvantages of these algorithms are compared according to the results of simulation. And it lays the foundation of using in the industrial field control.