AI-enabled bumpless transfer control strategy for legged robot with hybrid energy storage system

Designing Hybrid energy storage system (HESS) for a legged robot is significant to improve the motion performance and energy efficiency of the robot. However, switching between the driving mode and regenerative braking mode in the HESS may generate a torque bump, which has brought significant challenges to the stability of the robot locomotion. To address this issue, an AI-enabled control strategy for bumpless transfer switching is proposed, which is composed of a Proximal Policy Optimisation (PPO)-based non-linear active disturbance rejection controller and Deep neural network (DNN)-based bumpless transfer strategy. We indicate that the proposed intelligent strategy solves bumpless transfer by reducing the torque bump during the system switching process. Meanwhile, the authors analyse the energy driving subsystem and the energy regenerating subsystem based on operational modes and the energy flow. Via the parameters tuning criterion, the authors adopt a PPO algorithm to adaptively tune the parameters of the non-linear active disturbance rejection controller, which can improve the performance of the primary torque control. A DNN-based intelligent bumpless transfer strategy is proposed to set the initial value of two switching controllers at a switching moment. Simulations and experimental results validate the effectiveness of the proposed control strategy.