论文标题
具有参数和添加剂不确定性的线性系统的强大MPC：一种新的约束拧紧方法
Robust MPC for Linear Systems with Parametric and Additive Uncertainty: A Novel Constraint Tightening Approach
论文作者
论文摘要
我们提出了一种新型方法，用于设计可靠的模型预测控制器（MPC），以针对受约束的不确定线性系统设计。不确定的系统被建模为线性参数随添加干扰而变化。假定系统矩阵的设定界限和添加剂不确定性是已知的。我们围绕着使用这些界限的预测标称轨迹制定了一种新型的基于优化的约束拧紧策略。通过适当设计的终端成本函数和约束集，我们证明了与不确定系统的闭环中所得的MPC对施加的约束的满意度，以及对原点的状态稳定性的输入。我们通过数值示例强调了我们提出的方法的功效。
We propose a novel approach to design a robust Model Predictive Controller (MPC) for constrained uncertain linear systems. The uncertain system is modeled as linear parameter varying with additive disturbance. Set bounds for the system matrices and the additive uncertainty are assumed to be known. We formulate a novel optimization-based constraint tightening strategy around a predicted nominal trajectory which utilizes these bounds. With an appropriately designed terminal cost function and constraint set, we prove robust satisfaction of the imposed constraints by the resulting MPC in closed-loop with the uncertain system, and Input to State Stability of the origin. We highlight the efficacy of our proposed approach via a numerical example.
