RBF Neural Network Adaptive Control for Autonomous Electric Vehicles Based on Parameter Prediction
Based on parameter prediction,a KBF neural network adaptive control scheme was pro-posed for the motion control problems of autonomous electric vehicles with uncertainties.Firstly,the influences of system parameter uncertainties and external interferences were considered,and a dynam-ic model which might reflect the tracking and following behaviors of vehicles was established by the preview method.Secondly,RBF neural network compensator was adopted to compensate system un-certainties adaptively,and a generalized coordinated control law was designed for the lateral and longi-tudinal motions of vehicles.Thirdly,the impacts from the front vehicle speeds and road curvatures were taken into account,and the minimization of the energy consumption and the average jerks in the tracking and following control processes were regarded as the optimization objects.Afterwards,PSO algorithm was utilized to rolling optimize the gain parameter K in the coordinated control law,and then a series of optimized sample data were obtained.Then,to ensure the economy and ride comfort of vehicles,a BP neural network was designed and trained to realize the real-time prediction of gain parameter K in the generalized coordinated control law.Simulation results validate the effectiveness of the proposed control scheme.
autonomous electric vehicleuncertaintyradial basis function(RBF)neural networkparticle swarm optimization(PSO)algorithmparameter prediction
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