LQT-Based Energy-Efficient Control for Intelligent Vehicles Optimized by Adaptive Genetic Algorithm

Existing research on vehicle energy saving primarily focuses on longitudinal control, whereas path tracking control mainly emphasizes control accuracy. To simultaneously ensure both lateral control accuracy and energy-saving performance, this study proposes an Adaptive Genetic Algorithm LQT Controller to address the complex parameter tuning of traditional LQT controllers and the lack of feedback dynamic adjustment capability in conventional Genetic Algorithm LQT controllers. The proposed method dynamically adjusts the crossover and mutation rates of the Q and R matrices by evaluation of the control signals and results from the population, effectively balancing the global search and local convergence during the optimization process. Finally, joint simulations using Carsim and Simulink demonstrated that the proposed controller not only achieved high path tracking accuracy and smooth control signals but also significantly improved energy-saving performance under high-speed, large-curvature operating conditions. By optimizing the kinetic energy loss, the residual vehicle speed is notably increased, and the kinetic energy consumption is reduced by 14.45% compared with the traditional LQT controller. This proves the potential of the lateral control algorithm in enhancing the energy-saving performance.