Application of Improved Sliding Mode and Artificial Neural Networks in Robot Control
Source abstract
Abstract
Mobile robots are autonomous devices capable of self-motion, and are utilized in applications ranging from surveillance and logistics to healthcare services and planetary exploration. Precise trajectory tracking is a crucial component in robotic applications. This study introduces the use of improved sliding surfaces and artificial neural networks in controlling mobile robots. An enhanced sliding surface, combined with exponential and hyperbolic tangent approach laws, is employed to mitigate chattering phenomena in sliding mode control. Nonlinear components of the sliding control law are estimated using artificial neural networks. The weights of the neural networks are updated online using a gradient descent algorithm. The stability of the system is demonstrated using Lyapunov theory. Simulation results in MATLAB/Simulink R2024a validate the effectiveness of the proposed method, with rise times of 0.071 s, an overshoot of 0.004%, and steady-state errors approaching zero meters. Settling times were 0.0978 s for the x-axis and 0.0902 s for the y-axis, and chattering exhibited low amplitude and frequency.
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Research context
OpenAlex topics: Advanced Sensor and Control Systems
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Affiliations
Department of Mechanical System Engineering, Gyeongsang National University, Tongyeong 53064, Republic of Korea
Training Ship Operation Center, Gyeongsang National University, Tongyeong 53064, Republic of Korea
Funding
the Gyeongsang National University
References (33)
Show source-provided references
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