Reinforcement Learning-Based Composite Controller for Cable-Driven Parallel Suspension System at High Angles of Attack

Abstract

This paper investigates an intelligent method for the motion control of a cable-driven parallel suspension system (CDPSS) in wind tunnel tests, especially at high angles of attack, which is characterized by unsteady and nonlinear aerodynamics. Considering the modeling uncertainties and the complex aerodynamic interference, a composite controller that combines deep deterministic policy gradient (DDPG) and computed-torque is proposed to improve the control performance. The tasks at hand consist in the construction of the training environment based on the dynamic equations and the Markov Decision Process (MDP) design. The supplementary computed-torque control is used to enhance the learning rate of the agent. Then a well-trained agent is applied in the high angles of attack maneuvers control with different examples, including single-DOF and multi-DOF coupled motion. The simulation results show the controller could fulfill the training tasks efficiently, and it turns out to be robust and maintain strong generalization ability despite handling the unlearned tasks.

Keywords

• Wind tunnel test
• cable-driven parallel mechanism
• high angle of attack
• motion control
• DDPG