论文标题
使用优化的约束拧紧计算有效的鲁棒MPC
Computationally efficient robust MPC using optimized constraint tightening
论文作者
论文摘要
为受界添加剂干扰影响的线性，时间不变的系统提供了强大的模型预测控制（MPC）方法。主要的贡献是脱机 - 伴随反馈增益的脱机设计，从而最大程度地减少了限制的拧紧。这是通过将约束收紧问题作为变量的凸优化问题来实现的。所得的MPC控制器具有名义MPC的计算复杂性，并保证了递归可行性，稳定性和约束满意度。与现有的强大MPC方法相比，该方法的优势使用数值示例证明。
A robust model predictive control (MPC) method is presented for linear, time-invariant systems affected by bounded additive disturbances. The main contribution is the offline design of a disturbance-affine feedback gain whereby the resulting constraint tightening is minimized. This is achieved by formulating the constraint tightening problem as a convex optimization problem with the feedback term as a variable. The resulting MPC controller has the computational complexity of nominal MPC, and guarantees recursive feasibility, stability and constraint satisfaction. The advantages of the proposed approach compared to existing robust MPC methods are demonstrated using numerical examples.
