Using a Memory of Motion to Efficiently Warm-Start a Nonlinear Predictive Controller
Using a Memory of Motion to Efficiently Warm-Start a Nonlinear Predictive Controller
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DOI：
10.1109/icra.2018.8463154
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
N. Mansard;A. DelPrete;Mathieu Geisert;S. Tonneau;O. Stasse
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文献类型：
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作者：
N. Mansard;A. DelPrete;Mathieu Geisert;S. Tonneau;O. Stasse
预测控制是一种基于模型的控制复杂动态系统的有效方法。一般说来，它可以归结为大型非线性优化问题在每个控制周期的解。然后，一个关键问题是提供一个很好的猜测来初始化非线性求解器，以加快收敛速度。当环境中的干扰或变化阻止使用在前一个控制周期计算的轨迹作为初始猜测时，这一点尤其重要。在本文中，我们介绍了一个新颖而非常有效的解决方案来自动生成这个初始猜测。我们建议依靠离线计算来建立最优轨迹的近似值，该近似值可用于在线初始化预测控制器。为此，我们结合了基于抽样的计划、策略学习与通用表示法(如神经网络)和直接最优控制的使用。我们首先提出了一种算法，可以同时建立运动学概率路线图(PRM)和近似值函数以及控制策略。该算法快速收敛到最优状态控制轨迹的近似值(以及最优PRM)。然后，我们提出了两种方法来存储最优轨迹，并用它们来初始化预测控制器。我们的实验表明，直接存储状态控制轨迹会使预测控制器快速收敛(2到5次)到(全局)最优解。仿真结果在无人机和其他动力系统上得到了验证。
Predictive control is an efficient model-based methodology to control complex dynamical systems. In general, it boils down to the resolution at each control cycle of a large nonlinear optimization problem. A critical issue is then to provide a good guess to initialize the nonlinear solver so as to speed up convergence. This is particularly important when disturbances or changes in the environment prevent the use of the trajectory computed at the previous control cycle as initial guess. In this paper, we introduce an original and very efficient solution to automatically build this initial guess. We propose to rely on off-line computation to build an approximation of the optimal trajectories, that can be used on-line to initialize the predictive controller. To that end, we combined the use of sampling-based planning, policy learning with generic representations (such as neural networks), and direct optimal control. We first propose an algorithm to simultaneously build a kinodynamic probabilistic roadmap (PRM) and approximate value function and control policy. This algorithm quickly converges toward an approximation of the optimal state-control trajectories (along with an optimal PRM). Then, we propose two methods to store the optimal trajectories and use them to initialize the predictive controller. We experimentally show that directly storing the state-control trajectories leads the predictive controller to quickly converges (2 to 5 iterations) toward the (global) optimal solution. The results are validated in simulation with an unmanned aerial vehicle (UAV) and other dynamical systems.