RBF Neural Network Tuned PID Control with Radial Basis Functions

The PID control system based on RBF neural network tuning utilizes a three-layer feedforward network architecture with a single hidden layer. This configuration represents a locally approximating network that has been mathematically demonstrated to approximate almost all continuous functions with arbitrary accuracy. The core concept of this network involves employing radial basis functions for both signal processing and control operations. Through systematic network tuning, this approach enables precise system control and optimization. The implementation typically involves adjusting network parameters such as center vectors, widths of radial basis functions, and output layer weights. Key algorithmic steps include calculating the Euclidean distance between input vectors and center nodes, applying Gaussian activation functions, and updating weights using gradient descent or recursive least squares methods. This network architecture exhibits strong adaptability and learning capabilities, allowing continuous parameter adjustments based on real-time operational conditions to achieve improved control performance. In practical applications, this technology has been widely implemented in industrial automation systems, robotic control platforms, and intelligent transportation networks, making significant contributions to enhancing system performance and operational efficiency.