In order to solve the problems of low control accuracy and poor system fitness caused by the difficulty of selecting the weight matrix of quadratic linear regulator(LQR),this paper was designed a nonlinear decreasing weight particle swarm optimization(NLDW-PSO)algorithm.Based on the two-degree-of-freedom vehicle dynamics model,the lateral tracking error model is constructed,and the LQR steady-state error is eliminated by feedforward control.With lateral deviation,heading deviation and front wheel steering angle as evaluation functions,the system output error state is fed back to NLDW-PSO algorithm,The designed nonlinear decreasing inertia weight factor can improve the particle population optimization performance,which adaptively adjusts the LQR weight coefficient update strategy to form a closed-loop optimization control,and finally obtains the extreme value of objective function of the system.The tracking effect of the designed controllers is compared,the results showed that the proposed NLDW-PSO optimized LQR algorithm has the best tracking control effect,and it's maximum Lateral error was 0.076m by Carsim/Smulink co-simulation,and the mean Lateral error was reduced by 69.74％compared with the fixed weight coefficient LQR.The tracking control accuracy and adaptive ability of the vehicle are significantly improved,and it has strong robustness to velocity change.
nonlinear decreasing weightparticle swarm optimization PSOquadratic linear regulator LQRtrajectory tracking control
维普
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