Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

TL;DR

This paper introduces a robust MPC framework combined with neural network approximation for fast, safe, and reliable control of robotic manipulators with nonlinear and uncertain dynamics, demonstrating real-world applicability.

Contribution

It presents a novel robust setpoint tracking MPC algorithm and a neural network-based approximation method, unifying planning and control for robotic systems.

Findings

Robust MPC guarantees stability and constraint satisfaction.

Neural network approximation drastically reduces computation time.

Both methods scale effectively to real-world robotic manipulators.

Abstract

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.