Abstract
Using reinforcement learning (RL) for torque distribution of skid steering vehicles has attracted increasing attention recently. Various RL-based torque distribution methods have been proposed to deal with this classical vehicle control problem, achieving a better performance than traditional control methods. However, most RL-based methods focus only on improving the performance of skid steering vehicles, while actuator faults that may lead to unsafe conditions or catastrophic events are frequently omitted in existing control schemes. This study proposes a meta-RL-based fault-tolerant control (FTC) method to improve the tracking performance of vehicles in the case of actuator faults. Based on meta deep deterministic policy gradient (meta-DDPG), the proposed FTC method has a representative gradient-based metalearning algorithm workflow, which includes an offline stage and an online stage. In the offline stage, an experience replay buffer with various actuator faults is constructed to provide data for training the metatraining model; then, the metatrained model is used to develop an online meta-RL update method to quickly adapt its control policy to actuator fault conditions. Simulations of four scenarios demonstrate that the proposed FTC method can achieve a high performance and adapt to actuator fault conditions stably.
Highlights
Due to their simple mechanical structure and flexible control, skid steering distributed drive vehicles are widely applied in various scenarios, including the construction industry, wheeled robots, agricultural vehicles, military vehicles, and so on
We propose a meta-DDPG-based fault-tolerant control (FTC) method for skid steering vehicles moving under actuator faults
Based on the DDPG algorithm, we developed an agent that can perform dual-channel control over the longitudinal speed and yaw rate of skid steering vehicles
Summary
Due to their simple mechanical structure and flexible control, skid steering distributed drive vehicles are widely applied in various scenarios, including the construction industry, wheeled robots, agricultural vehicles, military vehicles, and so on. Without FTC, the vehicle would operate under an instability condition and collide with nearby facilities; with FTC, the vehicle’s collision risk may be avoided [3] To deal with such unforeseen and undesired faults, the controller must quickly learn their models under new runtime conditions and adapt proper torque distribution . This study, in combination with the DDPG-based torque distribution method, proposes a meta-DDPG-based FTC method for skid steering vehicles so as to improve the tracking performance when an actuator fault occurs. (2) We construct an offline actuator fault dataset—based on this, the meta-DDPG-based FTC method is proposed to quickly adapt to the vehicle’s model with actuator faults and to improve the desired value of tracking performance in the degraded conditions.
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