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

TL;DR

This paper introduces an MPC-based hierarchical control method for underactuated, constrained robots that predicts future states and optimizes control inputs over a finite horizon, improving efficiency in executing multiple tasks.

Contribution

It presents a novel MPC approach using quadratic programming for hierarchical control of complex robotic systems, enhancing computational efficiency over nonlinear methods.

Findings

Effective handling of multiple hierarchical tasks

Improved computational efficiency

Successful validation on simulated underactuated robots

Abstract

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.