This article focuses on model predictive control (MPC) of nonlinear systems in the case that the system parameters are inaccurate due to equipment wear or environmental changes. An MPC where the parameters of the predictive model are recursive estimated is proposed for nonlinear continuous time systems. The range of initial state that is able to guarantee the state always bounded in an allowable stability region, even when there does not exist any robust control law designed based on the mismatched initial model, is deduced. The corresponding optimization problem is designed based on Lyapunov controller techniques and includes parameter estimation parts. By this method, the state will eventually converge to a small neighborhood of the desired set‐point. Stability analysis is performed and an application of the proposed method to the chemical process is presented to show the effectiveness of the proposed method.

中文翻译：

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具有时变动力学并保证Lyapunov稳定性的非线性系统的模型预测控制

由于设备磨损或环境变化导致系统参数不准确时，本文重点介绍非线性系统的模型预测控制（MPC）。针对非线性连续时间系统，提出了一种递归估计预测模型参数的MPC算法。推论即使不存在基于不匹配初始模型设计的鲁棒控制律，也能够保证始终将其限制在允许的稳定区域内的初始状态的范围。基于Lyapunov控制器技术设计了相应的优化问题，并包括参数估计部分。通过这种方法，状态最终将收敛到所需设置点的较小邻域。