论文标题
模型预测控制不受约束和约束的线性二次问题及其他
Performance Bounds of Model Predictive Control for Unconstrained and Constrained Linear Quadratic Problems and Beyond
论文作者
论文摘要
我们研究了不受限制的和约束的线性二次问题，并研究了用于此类问题的模型预测控制（MPC）方法的次优。将MPC视为解决相关固定点方程的近似方案，我们在MPC下得出了闭环系统的性能界限。我们的分析以及数值示例提出了选择终端成本和终端约束的新方法，这些方法与原始问题的riccati方程有关。所得的方法可以具有更大的可行区域，并且在无限视野上的闭环成本方面几乎不会造成任何性能的损失。
We study unconstrained and constrained linear quadratic problems and investigate the suboptimality of the model predictive control (MPC) method applied to such problems. Considering MPC as an approximate scheme for solving the related fixed point equations, we derive performance bounds for the closed-loop system under MPC. Our analysis, as well as numerical examples, suggests new ways of choosing the terminal cost and terminal constraints, which are \emph{not} related to the solution of the Riccati equation of the original problem. The resulting method can have a larger feasible region, and cause hardly any loss of performance in terms of the closed-loop cost over an infinite horizon.
