Optimization Design of LQR Controller Based on Genetic Algorithm

Applying genetic algorithms to LQR controller design fully utilizes GA's advantages in global optimization. The genetic algorithm optimizes weighting matrices by treating active suspension performance indicators as objective functions, thereby enhancing LQR design efficiency and performance. This approach also improves the stability and precision of LQR controllers, making them more effective in practical applications. The design process must consider various factors including system dynamic response, controller stability, and robustness. Key implementation steps involve: encoding weighting matrix elements into chromosomes, defining fitness functions based on performance metrics (e.g., ITAE, ISE), and executing genetic operations (selection, crossover, mutation) across generations. Comprehensive experimentation and testing are necessary to validate the method's effectiveness and reliability. Ultimately, this optimized approach can be implemented in practical control systems to enhance performance and stability, meeting real-world application requirements.