Neural network-based sliding mode controllers applied to robot manipulators: A review

Abstract

In recent years, numerous attempts have been made to integrate sliding mode control (SMC) and neural networks (NN) in order to leverage the advantages of both methods while mitigating their respective disadvantages. These endeavors have yielded significant achievements, leading to diverse applications in enhancing control performance for nonlinear objects, including robots. This paper primarily focuses on investigating critical technical research issues, potential applications, and future perspectives of SMC based on NNs when applied to robot manipulators. Firstly, a comprehensive examination is conducted to assess the advantages, disadvantages, and potential applications of SMC and its various variants. Secondly, recent advancements in control systems have introduced NNs as a promising innovation. NNs offer an alternative approach to adaptive learning and control, effectively addressing the technical challenges associated with SMCs. Finally, the assessment of these combined approaches' advantages and limitations is based on studies conducted over the last few decades, along with their future development directions.