In practice, robots operate as nonlinear systems and often encounter factors like nonlinear friction, load variations, and external disturbances during tasks. To address these challenges, a smart control approach has been developed that combines the strengths of fuzzy logic and sliding mode control (SMC) for precise robot manipulator positioning. The key benefit of SMC lies in its robustness, maintaining stability despite noise or parameter changes in the system. However, designing an SMC system often faces difficulties due to practical limitations, making deployment not always feasible in real-world applications. Additionally, a large control law amplitude can lead to chattering around the sliding surface. To overcome these issues, the study introduces a fuzzy logic-based method to adaptively estimate the control law's magnitude, guided by Lyapunov stability principles. This control scheme is tested on a four-degree-of-freedom robot manipulator, with simulation results confirming its effectiveness in MATLAB.
Co et al. (Fri,) studied this question.