This paper proposes a novel approach to controller design for MR-damped
vehicle suspension system. This approach is predicated on the premise that the
optimal control strategy can be learned through real-world or simulated
experiments utilizing a reinforcement learning algorithm with continuous
states/actions. The sensor data is fed into a Twin Delayed Deep Deterministic
Policy Gradient (TD3) algorithm, which generates the actuation voltage required
for the MR damper. The resulting suspension space (displacement), sprung mass
acceleration, and dynamic tire load are calculated using a quarter vehicle
model incorporating the modified Bouc-Wen MR damper model. Deep RL's reward
function is based on sprung mass acceleration. The proposed approach
outperforms traditional suspension control strategies regarding ride comfort
and stability, as demonstrated by multiple simulated experimentsComment: 19 pages , 9 figures , 5 table