Safe and Fast Tracking Control on a Robot Manipulator: Robust MPC and Neural Network Control
Safe and Fast Tracking Control on a Robot Manipulator: Robust MPC and Neural Network Control
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发表时间：
2019
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通讯作者：
Julian Nubert;Johannes Köhler;Vincent Berenz;F. Allgöwer;Sebastian Trimpe
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作者：
Julian Nubert;Johannes Köhler;Vincent Berenz;F. Allgöwer;Sebastian Trimpe
——快速反馈控制和安全保证对于现代机器人技术至关重要。我们提出了一种通过（深度）神经网络（NN）将新颖的鲁棒模型预测控制（MPC）与函数逼近相结合来实现这两个目标的方法。其结果是一种新的方法来处理机器人技术中常见的具有非线性、不确定性和约束动力学的复杂任务。具体来说，我们利用 MPC 研究的最新成果提出了一种新的鲁棒设定点跟踪 MPC 算法，该算法实现了动态设定点的可靠和安全跟踪，同时保证稳定性和约束满足。所提出的鲁棒 MPC 方案构成了一种单层方法，通过基于参考和可能的障碍物位置直接计算控制命令，统一了通常分离的规划层和控制层。作为单独的贡献，我们展示了如何通过使用 NN 控制器逼近 MPC 定律来大幅减少 MPC 的计算时间。神经网络根据 MPC 的离线样本进行训练和验证，产生统计保证，并在运行时代替它。我们在最先进的机器人操纵器上进行的实验首次表明，所提出的鲁棒和近似 MPC 方案都可以扩展到现实世界的机器人系统。
—Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems.