论文标题
自适应模型预测控制中的主动探索
Active exploration in adaptive model predictive control
论文作者
论文摘要
为线性，时间不变的系统提供了双重自适应模型预测控制（MPC）算法，但在状态空间矩阵中受到有界的干扰和参数不确定性的影响。进行在线设置会员身份识别以减少不确定性，因此控制会影响识别的信息和系统的性能。本文的主要贡献是在目标函数中使用预测的最差成本来包括MPC优化问题中的这种双重效应。这使控制器可以执行主动探索，即控制输入降低参数空间区域中对性能最大的不确定性。此外，MPC算法可确保对状态和输入约束的强大约束满意度。通过将其与文献中的被动自适应MPC算法进行比较，可以显示出所提出的算法的优势。
A dual adaptive model predictive control (MPC) algorithm is presented for linear, time-invariant systems subject to bounded disturbances and parametric uncertainty in the state-space matrices. Online set-membership identification is performed to reduce the uncertainty and thus control affects both the informativity of identification and the system's performance. The main contribution of the paper is to include this dual effect in the MPC optimization problem using a predicted worst-case cost in the objective function. This allows the controller to perform active exploration, that is, the control input reduces the uncertainty in the regions of the parameter space that have most influence on the performance. Additionally, the MPC algorithm ensures robust constraint satisfaction of state and input constraints. Advantages of the proposed algorithm are shown by comparing it to a passive adaptive MPC algorithm from the literature.
