LQR Trajectory Tracking Control of Unmanned Wheeled Tractor Based on Improved Quantum Genetic Algorithm

In the process of trajectory tracking using linear quadratic regulator (LQR) for driverless wheeled tractors, a weighting matrix optimization method based on an improved quantum genetic algorithm (IQGA) is proposed to solve problem weight selection. Firstly, kinematic model tractor established according Ackermann steering model, and linearized discretized. Then, gate rotation angle adaptive strategy optimized adjust required individual evolution ensure timely jumping out local optimum. Secondly, populations were perturbed by chaotic perturbation Hadamard variation their dispersion degree in order increase diversity search accuracy, respectively. Thirdly, state Q control R LQR IQGA obtain increments with circular double-shifted orbits. Finally, simulation orbits combination Carsim Matlab was carried compare performance five algorithms, including traditional LQR, (GA), particle swarm (PSO), (QGA), IQGA. The results show that speeds up algorithm’s convergence, increases population’s diversity, improves global ability, preserves excellent information population, has substantial advantages over other algorithms terms performance. When tracked at 5 m/s, root mean square error (RMSE) four parameters, speed, lateral displacement, longitudinal heading angle, reduced about 30%, 1%, 55%, 3%, RMSE such as displacement error, 32%, 25%, 37%,