Sample-efficient Model Predictive Control Design of Soft Robotics by Bayesian Optimization

TL;DR

This paper introduces a sample-efficient Bayesian optimization approach to design model predictive control for soft robots, avoiding complex modeling and enabling effective control with minimal data.

Contribution

It proposes a data-driven MPC design method using Bayesian optimization to iteratively improve a low-dimensional prediction model for soft robotics.

Findings

Achieves desired tracking control without prior model knowledge

Converges to a (sub-)optimal controller in few iterations

Validated through high-fidelity simulation of a soft robot

Abstract

This paper presents a sample-efficient data-driven method to design model predictive control (MPC) for cable-actuated soft robotics using Bayesian optimization. Instead of modeling the complex dynamics of the soft robots, the proposed approach uses Bayesian optimization to search the best-guessed low-dimensional prediction model and its associated controller to minimize the objective function of closed-loop responses. The prediction model is updated by Bayesian optimization from the closed-loop input-output data in each iteration. A linear MPC is then designed based on the updated prediction model, and evaluated based on the closed-loop responses. Different from directly searching controller parameters, the closed-loop system stability, and inputs/outputs constraints can be easily handled in the MPC design. After a few iterations, a convergent solution of a (sub-)optimal controller can be obtained, which minimizes the user-defined closed-loop performance index. The proposed method is simulated and validated by a high-fidelity simulation of a cable-actuated soft robot. The simulation results demonstrate that the proposed approach can achieve desired tracking controller for the soft robot without a prior-known model.