论文标题
使用控制收缩指标的强大自适应MPC
Robust adaptive MPC using control contraction metrics
论文作者
论文摘要
我们为非线性连续时系统提供了一个可靠的自适应模型预测控制（MPC）框架，具有有界参数不确定性和加性干扰。我们利用一般控制收缩指标（CCM）来参数化围绕包含所有不确定轨迹的名义预测的同型管。此外，我们使用集合成员估计进行了模型适应。结果，提出的MPC公式适用于大量非线性系统，减少在线操作期间的保守主义，并确保稳健的约束满意度和收敛到所需设定点的邻里。主要的技术贡献之一是基于CCM的相应管动力学的推导，该CCM占模型不匹配的状态和输入依赖性。此外，我们在线优化了名义参数，该参数可以为MPC中的参数不确定性提供一般的设置会员更新。使用涉及平面四极管的数值示例证明了所提出的同型管MPC和在线适应的好处。
We present a robust adaptive model predictive control (MPC) framework for nonlinear continuous-time systems with bounded parametric uncertainty and additive disturbance. We utilize general control contraction metrics (CCMs) to parameterize a homothetic tube around a nominal prediction that contains all uncertain trajectories. Furthermore, we incorporate model adaptation using set-membership estimation. As a result, the proposed MPC formulation is applicable to a large class of nonlinear systems, reduces conservatism during online operation, and guarantees robust constraint satisfaction and convergence to a neighborhood of the desired setpoint. One of the main technical contributions is the derivation of corresponding tube dynamics based on CCMs that account for the state and input dependent nature of the model mismatch. Furthermore, we online optimize over the nominal parameter, which enables general set-membership updates for the parametric uncertainty in the MPC. Benefits of the proposed homothetic tube MPC and online adaptation are demonstrated using a numerical example involving a planar quadrotor.
