论文标题
输出学习模型预测控制
Output-Lifted Learning Model Predictive Control
论文作者
论文摘要
我们提出了一种计算有效的学习模型预测控制（LMPC）方案，以对一类非线性系统的最佳控制，其中可以使用升起的输出重建状态和输入。对于所考虑的系统类别，我们展示了如何使用在迭代任务中收集的历史轨迹数据来构建凸值函数近似以及在虚拟输出的提升空间中的凸安全集。这些构造迭代使用历史数据进行更新，并用于合成预测性控制策略。我们表明，所提出的策略保证了递归约束满意度，渐近稳定性和在每个策略更新中不稳定的闭环性能。最后，仿真结果证明了拟议策略对分段仿射（PWA）系统，运动学独轮车和双线性直流电动机的有效性。
We propose a computationally efficient Learning Model Predictive Control (LMPC) scheme for constrained optimal control of a class of nonlinear systems where the state and input can be reconstructed using lifted outputs. For the considered class of systems, we show how to use historical trajectory data collected during iterative tasks to construct a convex value function approximation along with a convex safe set in a lifted space of virtual outputs. These constructions are iteratively updated with historical data and used to synthesize predictive control policies. We show that the proposed strategy guarantees recursive constraint satisfaction, asymptotic stability and non-decreasing closed-loop performance at each policy update. Finally, simulation results demonstrate the effectiveness of the proposed strategy on a piecewise affine (PWA) system, kinematic unicycle and bilinear DC motor.
