论文标题
基于MPC的层次结构任务空间控制不足和受约束的机器人执行多个任务
MPC-Based Hierarchical Task Space Control of Underactuated and Constrained Robots for Execution of Multiple Tasks
论文作者
论文摘要
本文提出了一个基于MPC的控制器，以有效地执行多个层次结构任务，以实现未经限制和约束的机器人系统。现有的任务空间控制器或全身控制器解决了任务轨迹和机器人植物动力学的瞬时优化问题。但是，我们在这里提出的任务空间控制方法依赖于未来状态轨迹的预测以及对计算控制命令的有限时间莫的相应成本术语。我们采用加速能量误差作为优化问题的性能指数，并将其扩展到MPC的有限时间范围。我们的方法采用二次约束二次编程，其中包括二次约束来处理多个层次任务，并且比依赖于非线性编程的非线性MPC方法在计算上更有效。我们使用新型机器人操纵器系统的数值模拟来验证我们的方法，该系统包含不足和约束的机械结构。
This paper proposes an MPC-based controller to efficiently execute multiple hierarchical tasks for underactuated and constrained robotic systems. Existing task-space controllers or whole-body controllers solve instantaneous optimization problems given task trajectories and the robot plant dynamics. However, the task-space control method we propose here relies on the prediction of future state trajectories and the corresponding costs-to-go terms over a finite time-horizon for computing control commands. We employ acceleration energy error as the performance index for the optimization problem and extend it over the finite-time horizon of our MPC. Our approach employs quadratically constrained quadratic programming, which includes quadratic constraints to handle multiple hierarchical tasks, and is computationally more efficient than nonlinear MPC-based approaches that rely on nonlinear programming. We validate our approach using numerical simulations of a new type of robot manipulator system, which contains underactuated and constrained mechanical structures.
