Genetic Algorithm-Based LQR Controller Optimization Design in MATLAB

This MATLAB source code implements a genetic algorithm-based optimization design for Linear Quadratic Regulator (LQR) controllers, serving as an excellent learning example for MATLAB algorithm development. The source code utilizes genetic algorithms to optimize LQR controller parameters through iterative processes and fitness evaluation to search for optimal solutions. The implementation includes key components such as chromosome encoding for controller parameters, selection operators (roulette wheel or tournament selection), crossover and mutation operations for population evolution, and fitness calculation based on LQR performance indices. This example helps learners understand the application of genetic algorithms in controller design and demonstrates how to write corresponding optimization code in MATLAB. By studying and analyzing this code, learners can gain deep insights into the working principles of both genetic algorithms and LQR controllers, including how to define cost functions, implement genetic operators, and handle convergence testing. The code provides practical knowledge that can be applied to real-world control system optimization projects, covering aspects like parameter tuning, stability analysis, and performance validation through MATLAB's control system toolbox functions.