AUV 3D trajectory tracking control identified online by recurrent immune network
In order to improve the 3D trajectory tracking accuracy of autonomous underwater vehicle(AUV)under dis-turbances such as ocean current,ocean wave and underwater noise,a PID self-tuning trajectory tracking controller identified online by recurrent immune neural network(PID-RINN)is proposed.First,the kinematic model of the AUV is established,and the depth distance,heading angle and pitch angle of the AUV are taken as the control variables,and a PID controller identified online by the neural network is designed.Then a recursive immune network is constructed by referring to the in-formation processing mechanism of the biological immune system.After that,the lateral distance between the underwater ro-bot and the preview waypoint on the horizontal plane is defined as the vaccine,and is inoculated into the synaptic hidden lay-er of the recurrent immune network together with the output of the cellular hidden layer.Finally,based on the gradient meth-od,the online self-tuning of the PID controller identified by the recursive immune network is realized.Test results show that compared with PID,GA_PID,RBF_PID,the average and maximum position errors of the proposed PID-RINN are respect-ively reduced by an average of 31.91％and 25.81％,the average and maximum heading angle errors are reduced by an aver-age of 32.54％and 25.27％respectively,and the average and maximum pitch angle errors are reduced by an average of 61.93％and 61.26％respectively,which verifies that the 3D trajectory tracking identified online by the recursive immune network has the advantages of high control accuracy and strong disturbance suppression.
万方数据
维普
