# Application of Improved Sliding Mode and Artificial Neural Networks in Robot Control
## 1 Introduction
To address these challenges, this study proposes the use of an improved sliding surface based on Artificial Neural Networks (specifically, Radial Basis Function networks) for robot trajectory tracking control. By integrating these methods, the proposed hybrid control strategy aims to overcome the limitations of each approach while enhancing the overall performance of robotic systems. Specifically, this paper discusses the implementation of this hybrid control mechanism in various robotic applications, detailing its potential to improve stability, reduce chattering, and enhance adaptability to dynamic environmental changes. The improved sliding surface is designed to replace the classical sliding surface combined with exponential and hyperbolic tangent (tanh) [18; 19; 20] approach functions to minimize chattering phenomena. The RBF network [21; 22] is employed to estimate the nonlinear components in the sliding control law, calculated based on Lyapunov stability theory. This paper is organized into four sections: Section 2 presents the application of the improved sliding surface and artificial neural networks to robots, results are presented in Section 3, and Section 4 includes a discussion and concludes the paper. This study introduces an innovative approach to mobile robot control by integrating improved sliding surfaces with artificial neural networks. The proposed method aims to fill the knowledge gaps left by previous research by addressing the following unresolved issues:  
* Reduction in Chattering: Traditional SMC techniques suffer from chattering, which reduces control accuracy and efficiency. This paper proposes the use of exponential and hyperbolic tangent approach laws combined with an improved sliding surface to significantly mitigate chattering. * Real-Time Adaptation: While ANNs have been used in control systems, their application in real-time adaptation for mobile robots remains underexplored. This study employs Radial Basis Function (RBF) networks to estimate nonlinear components of the control law, updating weights online using a gradient descent algorithm. Thisreal-time learning capability enhances the robot's adaptability to dynamic changes in the environment. * Comprehensive Validation: Many existing studies lack extensive validation of their proposed methods. This paper provides thorough simulation results using MATLAB/Simulink to demonstrate the effectiveness of the proposed control strategies. Key performance metrics such as rise time, settling time, steady-state error, and overshoot are evaluated to highlight the improvements achieved. By addressing these critical gaps, the proposed hybrid control strategy not only enhances the performance of mobile robots, but also opens avenues for research in robotic control systems. The introduction of this innovative control mechanism promises to refine the operational capabilities of robots, making them more adaptable, efficient, and capable of operating autonomously in a wide range of environments. The introduction of this innovative control strategy not only promises to refine the operational capabilities of robots, but also opens avenues for research in robotic control systems. This paper provides a comprehensive analysis of the theoretical foundations, supplemented by simulation results and practical implementations, to demonstrate the effectiveness and versatility of the combined approach.