PID Control with Genetic Algorithm Optimization

PID control is widely used in industrial production, but obtaining optimal PID parameters remains challenging. This example provides a reference for PID control enthusiasts by demonstrating genetic algorithm-based PID optimization. The implementation includes parameter tuning through selection, crossover, and mutation operations to achieve better system performance.

MATLAB-Based Crack and Scratch Detection

In industrial production, surface defects such as cracks, scratches, and discoloration frequently pose challenges for both manual inspection and machine vision systems. These defects are difficult to detect due to their irregular shapes, low contrast variations, and interference from natural textures or patterns on product surfaces. Successful surface defect detection requires optimized lighting conditions, high-resolution cameras, precise component-camera positioning, and advanced machine vision algorithms. The scratch detection process typically involves two key steps: first, identifying the presence of scratches on the product surface, and second, extracting the scratch features using image processing techniques.

PID Control Widely Adopted in Industrial Production with Genetic Algorithm Implementation

While PID control is extensively utilized in industrial processes, determining optimal PID parameters remains a key challenge. This example provides a reference implementation using genetic algorithms for PID enthusiasts, demonstrating how evolutionary computation techniques can optimize controller tuning through population-based search and fitness evaluation.