Data-Driven Model Predictive Control of DC-to-DC Buck-Boost Converter

The data-driven model predictive control (DDMPC) scheme is proposed to obtain fast convergence to a desired reference and to be utilised to mitigate the destabilising effects that a DC-to-DC buck-boost converter (BBC) with an active load experiences. The DDMPC strategy uses the observed state to derive an optimal control policy using a reinforcement learning (RL) algorithm. The employed Proximal Policy Optimisation (PPO) algorithm's performance is benchmarked against the PI controller. From the simulated results obtained using the MATLAB Simulink solver, the most robust methods for short settling time and stability were the hybrid methods. These methods take advantage of the short settling time provided by the PPO algorithm and the stability provided by the PI controller or the filtering mechanism over the transient time. The source code for this study is available on GitHub to support reproducible research in industrial electronics society.