| Linear Model Predictive Control (MPC) has been widely applied in modern process control in industry, but it does 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 generalization of linear MPC algorithm and the characteristic of nonlinear systems, several moving horizon optimization algorithms of nonlinear MPC are studied and corresponding simulations are put into practice. The main contents are as follows:1. The two-step moving horizon optimization strategy is researched in connection with Hammerstein model, and its stability is proved. In this strategy, 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. Two approximate calculation methods are offered and their error is analyzed. Finally the two-step strategy is extended to nonlinear systems with similar structure.2. The complexity of multi-goal and constrains in industrial process control are analyzed, therefore the moving horizon optimization strategy based on genetic algorithm is presented. The genetic operators to improve the performance of the algorithm are also provided.3. The multi-model strategy is introduced, and a multi-model modeling method of nonlinear state space model is presented. Considering the number of models, the selection of switch time and construction of reference trajectory, some measure is presented to improve the performance of the algorithm.4. Through the simulation of six 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.
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