Fuzzy Control Algorithm for Automotive Cruise Control Systems with Optimization Techniques

This document presents a fuzzy control algorithm designed for automotive cruise control systems, along with detailed explanations of how gradient descent and Kalman filtering techniques can optimize the fuzzy controller's performance. The implementation typically involves defining fuzzy membership functions for speed error and acceleration, followed by rule-base design using Mamdani or Sugeno inference systems. Gradient descent optimization helps fine-tune the fuzzy parameters by minimizing a cost function that represents control performance, while Kalman filtering improves state estimation for better system response. Additional optimization algorithms such as genetic algorithms and simulated annealing can also be integrated to further enhance the controller's efficiency. Through exploring various algorithms and optimization methods, developers gain deeper insights into control system mechanics and performance improvement strategies. The same fuzzy control principles can be extended to other applications including robotic control and aviation systems, providing valuable hands-on experience for building expertise in intelligent control systems. Continuous research and practical implementation in these areas help strengthen technical skills and lay a solid foundation for future work and advanced studies.