论文标题
无限马可分子可区分模型预测控制
Infinite-Horizon Differentiable Model Predictive Control
论文作者
论文摘要
本文提出了一个可靠的线性二次模型预测控制（MPC）框架，用于安全模仿学习。使用从离散时间代数riccati方程（DARE）获得的终端成本函数来实施无限 - 摩恩，因此可以证明学习控制器可以在闭环中稳定。中心贡献是敢于解决方案的分析衍生物的推导，从而允许使用基于分化的学习方法。另一个贡献是MPC优化问题的结构：增强的拉格朗日方法可确保MPC优化在整个训练过程中都是可行的，同时对状态和输入进行硬性约束，并确保MPC解决方案和衍生剂在每次迭代时都准确。在一系列数值研究中证明了该框架的学习能力。
This paper proposes a differentiable linear quadratic Model Predictive Control (MPC) framework for safe imitation learning. The infinite-horizon cost is enforced using a terminal cost function obtained from the discrete-time algebraic Riccati equation (DARE), so that the learned controller can be proven to be stabilizing in closed-loop. A central contribution is the derivation of the analytical derivative of the solution of the DARE, thereby allowing the use of differentiation-based learning methods. A further contribution is the structure of the MPC optimization problem: an augmented Lagrangian method ensures that the MPC optimization is feasible throughout training whilst enforcing hard constraints on state and input, and a pre-stabilizing controller ensures that the MPC solution and derivatives are accurate at each iteration. The learning capabilities of the framework are demonstrated in a set of numerical studies.
