Source Code for Neural Network PID Control and Fuzzy PID Control Algorithms

In the field of control engineering, PID controller optimization has always been a key research focus. While traditional PID controllers feature simple structure and easy implementation, they often perform poorly when dealing with nonlinear and time-varying systems. To address this limitation, researchers have introduced neural network and fuzzy control technologies, leading to the development of neural network PID and fuzzy PID control methods. These approaches significantly enhance control system performance through adaptive adjustment of PID parameters.

The core concept of neural network PID control lies in utilizing neural networks' nonlinear mapping and self-learning capabilities to optimize PID parameters. Common implementations include BP (Backpropagation) neural network PID, CMAC (Cerebellar Model Articulation Controller) PID, and RBF (Radial Basis Function) neural network PID. In code implementation, BP neural networks adjust weights through gradient descent algorithms, enabling PID parameters to adapt to system dynamic changes. The MATLAB implementation typically involves defining network layers, activation functions, and training loops. CMAC PID leverages its local approximation characteristics for rapid response to control demands, often implemented using hash coding for memory addressing. RBF neural networks demonstrate exceptional performance in nonlinear systems due to their excellent approximation capabilities, with code implementation focusing on Gaussian basis function centers and width parameter optimization.

Fuzzy PID control employs fuzzy logic to handle system uncertainties and complexities. The fuzzy controller dynamically adjusts PID parameters by defining membership functions for inputs/outputs and fuzzy rules. This method doesn't rely on precise mathematical models but achieves adaptive control through empirical rules, making it particularly suitable for complex systems difficult to model mathematically. In MATLAB implementation, this typically involves creating Fuzzy Inference System (FIS) objects, defining membership functions using "addvar" and "addmf" functions, and establishing rule bases with the "addrule" function.

When implementing these control algorithms in MATLAB environment, engineers typically combine Simulink for simulation validation. By building system models and loading control algorithms, users can visually observe control effects and analyze key performance indicators such as overshoot and response time. The source code implementation not only provides convenience for academic research but also establishes a technical foundation for industrial controller design. The implementation often involves creating S-function blocks or using MATLAB Function blocks to integrate the control algorithms into Simulink models.

Both neural network PID and fuzzy PID methods aim to enhance the adaptive capability and robustness of control systems. With the development of technologies like deep learning, these control methods are expected to play more significant roles in increasingly complex scenarios. Future implementations may incorporate deep neural networks with multiple hidden layers or hybrid approaches combining fuzzy logic with reinforcement learning techniques.