Neural Network PID Adaptive Control MATLAB Program Collection

Neural Network PID adaptive control combines the advantages of traditional PID controllers with neural networks, utilizing the self-learning capability of neural networks to achieve online adjustment of controller parameters. This approach significantly enhances system control performance under complex operating conditions.

The core concept involves a dual-loop structure: the inner loop employs a traditional PID controller acting directly on the controlled plant, while the outer loop uses a neural network to identify system dynamic characteristics in real-time and dynamically adjust PID parameters (proportional gain Kp, integral time Ti, derivative time Td). The neural network typically adopts a BP (Backpropagation) structure, continuously reducing the error between system output and desired values through online training.

In MATLAB implementation, Simulink serves as the system simulation platform, integrated with the Neural Network Toolbox for network training and parameter updates. Key implementation steps include: 1) Establishing mathematical models of controlled objects; 2) Designing neural network architecture (input layer typically includes error signals, error derivatives, etc.); 3) Programming parameter adaptation algorithms using functions like 'feedforwardnet' and 'train'; 4) Validating dynamic response and disturbance rejection capabilities through simulation studies.

Compared to fixed-parameter PID controllers, this method's advantage lies in adapting to nonlinear and time-varying systems, though careful attention must be paid to balancing network training speed with real-time performance. Practical implementation should begin with offline pre-training before switching to online fine-tuning mode, which can be implemented using MATLAB's batch training and adaptive training functions.