High-Precision Adaptive Control of Cable-Driven Parallel Robots With Convergence Guarantee

The development of cable-driven parallel robots (CDPRs) is restricted because of model uncertainty caused by multicable-driven structures and diverse task requirements, which mainly leads to a loss of control accuracy. To address this problem, a novel high-precision adaptive control scheme with convergence guarantee (HAC-CG) is proposed in this study to improve the control performance in the presence of dynamic uncertainties. Except for the traditional control objective related to tracking errors, the cooperative motion characteristics among all cables are discussed based on a parallel topology, and the additional control objective of cable global cooperation is investigated. Subsequently, a novel parameter adaptation law is designed according to the control objectives to achieve faster convergence of the uncertainty parameters. Using the Lyapunov method, system stability with a convergence guarantee of the HAC-CG is strictly proven, and its robustness is theoretically analyzed. Simulations and experiments are performed, and the results indicate that the HAC-CG can significantly ensure the cooperative motion of all cables, effectively adapt the inertial parameters at a faster speed, and eventually achieve gratifying tracking accuracy. Additionally, the HAC-CG has superior robustness in ensuring gratifying control performance of the CDPRs in scenarios with disturbances.