关键词: 非线性模型预测控制, 时间最优, 逐步倒退法, Lipschitz条件

Abstract: A new nonlinear time optimal model predictive control paradigm is proposed for a class of nonlinear system with constraints. Firstly, this method of nonlinear linearized based on Jacobian linearization is used to be derive a series of convex optimization problem, and the linearization error determines the upper boundary under Lipschitz conditions. Then it uses dual model strategy, in the case of offline condition step structures a series of ellipsoid sets to describe t step feasible region, balance point of each ellipsoid sets is based on an ellipsoid to choose, according to the ellipsoid sets suitable input, it makes the system stable by online computation. Using a step by step backwards computation method can ensure the feasibility and stability of the iteration, and reduce the computation burden greatly. Numerical examples prove the feasibility of the algorithm.

Key words: nonlinear model predictive control, time optimal, step by step backwards, Lipschitz condition