Super-Twisting Sliding Mode- and Iterative Learning-Based Synchronization Control for 2R1T Parallel Robots

Abstract

Two-rotation-one-translation (2R1T) parallel robots, which is capable of providing high precision and stability even in very complex operating conditions, are suitable for hybrid machining applications. However, the motion coupling issues of multiple kinematic chains in 2R1T parallel robot can introduce significant nonlinearities to the system, increasing the complexity of control and the risk of instability under disturbances and model uncertainty. Accordingly, we propose a supertwisting sliding mode and iterative learning-based synchronization control (STILSC) for 2R1T parallel robot to achieve high tracking accuracy. The using of synchronization control improves motion coordination among all the chains in 2R1T parallel robot by considering the cross-coupling errors. Moreover, the iterative learning section of STILSC helps estimate the system dynamics, while the supertwisting sliding mode controller section of STILSC is to reduce model estimating errors and external disturbances. A detailed and rigorous derivation is also provided. numerical simulations and experimental validations of the tracking control were both carried out on a 2R1T parallel robot mathematical model and prototype, respectively. The trajectory tracking performance of STILSC are assessed by comparing it with other controllers. The results demonstrate the effectiveness of the proposed STILSC, with detailed error analysis confirming that it exhibits superior performance and enhanced stability.