模型预测控制器权重参数整定非线性规划法

模型预测控制(MPC)权重参数的整定是其取得良好控制性能的关键。针对基于双层结构多目标优化的MPC权重参数整定方法存在求解过程较慢、耗时较长的问题，提出了一种非线性规划整定方法。该方法将MPC权重参数整定中每个时间采样点的MPC子优化问题等价为外层MPC权重参数整定优化问题的最优KKT(Karush-Kuhn-Tucker)条件，将MPC权重参数整定的双层多目标优化问题转化为单层非线性规划问题。仿真案例表明，基于单层非线性规划整定方法的MPC控制性能优于或近似于基于双层多目标优化整定方法的MPC控制性能；而且基于单层非线性规划的整定方法能够快速获得MPC权重参数，时间成本由基于多目标优化整定方法所需的1.0～1.5 h缩短到10～90 s。

Nonlinear programming method for tuning weight parameters of model predictive controller

The tuning of the weight parameters on the input and output variables can significantly affect the performance of a model predictive controller (MPC) to achieve a good closed-loop dynamic response. However, the currently available approaches based on the bi-layer multi-objective optimization (MOO) for tuning MPC weight parameters are computation-consuming. In this study, a new tuning algorithm is proposed, which converts the bi-layer MOO-based approach into a single-layer nonlinear programming (NLP) problem by treating the sub-optimization problem of MPC in the lower layer as the optimal KKT (Karush-Kuhn-Tucker) condition of the optimization in the upper layer, so as to reduce the computational cost. The simulation results demonstrate that the MPC tuned by NLP method shows similar or even better performance than the MPC tuned by MOO-based method. Moreover, by using the NLP tuning method, the computational time of the MPC tuning can be significantly reduced from a range of 1.0 h to 1.5 h for the MOO-based tuning method to a range of 5 s to 90 s.