Sliding Mode Control with Adaptive Learning of Upper Bound via RBF Neural Network

This program implements sliding mode control with adaptive learning of upper bounds through RBF neural networks, specifically designed for situations where upper bound values cannot be accurately measured in practical applications. In real-world scenarios, precise measurement of certain upper bounds is often challenging, which complicates control system design. The implemented solution utilizes RBF neural networks to estimate these upper bounds and dynamically adjusts control strategies based on the estimated values. The core algorithm involves neural network weight adaptation using gradient descent methods and implements a sliding mode control law with adaptive gain adjustment. This approach ensures system stability and robustness even without precise upper bound measurements. The program structure includes modules for neural network training, real-time estimation, and control signal generation. Therefore, this design not only addresses the problem of unmeasurable upper bounds but also enhances control system performance and adaptability through intelligent estimation techniques.