Adaptive Sliding Mode Control of Parallel Sorting Robots Using Variable-Gain Super-Twisting ESO

Abstract

Parallel robots have uncertain problems such as time-varying model parameters and external disturbances. When the sorting load is unknown and changes dynamically, the load moment of inertia will change significantly when the sorting objects are connected in series. This paper proposes a sorting parallel robot control system that combines ESO and adaptive control, thereby improving the control effect of the sorting parallel robot and improving the control efficiency of the parallel robot. The new controller (IM-ST-ESO) is based on OLI-SMC and IASMC. And designs an adaptive law to weaken the dependence of the generalized super-twisting sliding mode algorithm on the disturbance boundary, improve the anti-disturbance ability of the system, and further improve the convergence speed of the system through the linear terms in the integral fast non-singular sliding surface. Combined with the experimental analysis, The experimental method has achieved significant results in optimizing the running time of the Delta robot sorting process. After optimization, the running time is 0.231s, which is 6.60% lower than before optimization. The average impact of each joint of the driving arm is significantly reduced, and the impact is reduced by 80.00%. Reducing joint impact helps improve the operational efficiency of robots and extend their lifespan. At the same time, it significantly reduces the average impact of each joint of the drive arm, and the impact is reduced by 80.00%. Therefore, it can be seen that the sorting parallel robot control system combined with ESO and adaptive control can effectively improve sorting efficiency and system performance, and can play an important role in subsequent intelligent production and intelligent operation.