MPC-Based Hierarchical Task Space Control of Underactuated and Constrained Robots for Execution of Multiple Tasks

This paper proposes an MPC-based controller to efficiently execute multiple\nhierarchical tasks for underactuated and constrained robotic systems. Existing\ntask-space controllers or whole-body controllers solve instantaneous\noptimization problems given task trajectories and the robot plant dynamics.\nHowever, the task-space control method we propose here relies on the prediction\nof future state trajectories and the corresponding costs-to-go terms over a\nfinite time-horizon for computing control commands. We employ acceleration\nenergy error as the performance index for the optimization problem and extend\nit over the finite-time horizon of our MPC. Our approach employs quadratically\nconstrained quadratic programming, which includes quadratic constraints to\nhandle multiple hierarchical tasks, and is computationally more efficient than\nnonlinear MPC-based approaches that rely on nonlinear programming. We validate\nour approach using numerical simulations of a new type of robot manipulator\nsystem, which contains underactuated and constrained mechanical structures.\n