Abstract
This article presents a tracking control design for two-link robot manipulators. To achieve robust tracking control performance, a super-twisting sliding mode control (STSMC) is derived. The stability of the system based on the proposed approach is proved based on the Lyapunov theorem. However, one problem with the designed STSMC is to properly set its parameters during the design. Therefore, it is proposed a social spider optimization (SSO) to tune these design parameters to improve the dynamic performance of the robot manipulator controlled considering STSMC. The performance of the STSMC approach based on SSO is compared to that based on particle swarming optimization (PSO) in terms of dynamic performance and robustness characteristics. The effectiveness of the proposed optimal controllers is verified by simulations within the MATLAB software. It is verified that the performance given by SSO-based STSMC outperforms that resulting from PSO-based STSMC. The experimental results are conducted based on LabVIEW 2019 software to validate the numerical simulation.
Highlights
Robot manipulators are widely used to assist workers in repetitive and insecurity tasks in a myriad of industrial processes, performing them faster and more efficiently
This table indicates that the particle swarming optimization (PSO)-based super-twisting sliding mode control (STSMC) is better than that based on social spider optimization (SSO) algorithm in terms of transient and robustness characteristics
An STSMC has been developed for tracking control of robot manipulator
Summary
Robot manipulators are widely used to assist workers in repetitive and insecurity tasks in a myriad of industrial processes, performing them faster and more efficiently. Wang et al proposed a robust adaptive fuzzy terminal sliding mode controller (FTSMC) based on low-pass filter (LPF) for trajectory tracking of robot manipulator in the presence of parametric uncertainty and external disturbance. The proposed controller integrates a fractional order control, which is responsible for fast finite-time convergence, chatter-free control inputs, and better tracking performance and robustness, with an adaptive STSMC, which is designed to cope with the unknown upper-bounded uncertainty. Two optimization techniques, represented by SSO and PSO algorithms, are applied for the finding of optimal design parameters in terms of best dynamic performance of a 2-DOF manipulator controlled by the STSMC. Social spider optimization-based optimization of super-twisting sliding mode control In this part, the performance of the robot manipulator is optimized by the SSO algorithm, which is formulated to find an optimal value for the design parameters of the STSMC.
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