Deep Reinforcement Learning-Based Control for Asynchronous Motor-Actuated Triple Pendulum Crane Systems With Distributed Mass Payloads

Abstract

For transporting large cargoes in practice, the triple pendulum hoisting pattern is widely utilized, where the distributed mass payload (DMP) is suspended under the hanger, the hanger is suspended on the hook, and the hook is connected with the hoisting mechanism. However, existing studies mainly focus on single-pendulum and double-pendulum hoisting patterns, and few of them pay attention to triple-pendulum hoisting patterns. Compared with single-pendulum and double-pendulum cranes, triple-pendulum cranes, which control four degrees of freedom (DoFs) with merely one control input, have a higher underactuation degree, with higher control difficulties. In addition, the nonlinear characteristics of alternating current (AC) asynchronous motors, such as unknown nonlinear dead zones and time delays, will degrade tracking accuracy and control performance. To solve the above problems, a model-free deep reinforcement learning-based trajectory planning method is proposed for triple-pendulum DMP cranes. Furthermore, an identification model for ac asynchronous motors is established. On this basis, a trajectory tracking method including an adaptive dead-zone compensation and a modified Smith predictor is proposed. Finally, experiments are carried out on a self-built 2-ton crane experimental platform, which is actuated by ac asynchronous motors. Hardware experiments demonstrate that triple pendulum cranes can accurately track the planned trajectory, and the DMP residual swing is effectively suppressed.