Journal of Physics Conference Series · 2021 · 14 citations · 3 references
Natural SciencesAutomotive EngineeringFlight DynamicsOptimizationVehicle DynamicsControl Of Dynamical SystemsControl SystemsControl TheoryGenetic AlgorithmsOptimal ControlVehicle ControlControl AlgorithmsGenetic Algorithm OptimizationPath Following ControlPath TrackingPath Tracking ControlTrajectory Tracking ControlVehicle Dynamics ControlVehicle Path Following
Abstract In order to study path tracking of AGV, the lateral dynamic model of AGV is established, and the state equation of the system is obtained. In order to make the state equation better adapt to the LQR controller, a lateral error integral is added to the controller. Then an improved state equation is obtained. The energy function is constructed and the LQR controller is designed. In order to find the optimal value of the weight Q and R in the LQR controller, Genetic Algorithm (GA) is introduced so as to realize the optimization of the LQR controller. The simulation results show that compared with the empirical method to determine the Q and R of LQR control, the LQR control method optimized by GA can effectively reduce the overshoot of the system, improve the convergence speed and stability of the system, and obtain a better path tracking effect.
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