This thesis explores a Bayesian Optimization technique for improving the tuning process of Model Predictive Control systems applied to soft robotics. Due to their high compliance and actuation redundancy, soft robotic systems are challenging to control through traditional rigid control frameworks. The objective of this study is to automate the hyperparameter tuning of MPC in order to enhance adaptability and efficiency in the control systems, handling the intricate behaviour of soft robots.

The study employs BO, leveraging its capability to efficiently navigate complex and high-dimensional optimization landscapes through a Gaussian Process (GP)-based surrogate model. This allows a systematic exploration and exploitation of the hyperparameter space toward setting up MPC optimal conditions that improve the performance metrics, such as response time and stability, under operational constraints. Two main acquisition functions, Expected Improvement (EI) and Lower Confidence Bound (LCB), are evaluated for their effectiveness in balancing exploration of the parameter space with exploitation of promising regions.

Obtained results, from simulations analyses, show that BO significantly reduces the manual effort involved in MPC tuning. The study also looks into the performance of the BO approach under varying initial conditions and introduces variance to the weights of the MPC to analyse the performance of BO under uncertainty.