  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.
