基于分散化神经鲁棒控制的轨迹跟踪算法研究
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摘要
针对外部扰动以及建模误差对机械臂轨迹跟踪精度影响的问题,利用递归神经网络设计了分散化的神经鲁棒控制器,采用机械臂各个关节状态方程的子系统表示整个系统。使用滤错训练算法估计神经网络未知权重系数,同时引入鲁棒项抑制关节神经控制器之间的相互影响和建模误差,并利用Lyapunov函数进行稳定性证明。与没有鲁棒项的仿真结果对比表明,设计的分散化神经鲁棒控制器具有更精确的轨迹跟踪精度,误差的收敛性更好,稳定性更高。
In order to eliminate the influence of external disturbance and modeling error on manipulator ′ s trajectory tracking accuracy,the recurrent neural network is used to design the decentralized neural robust controller,and the subsystem of each joint state equation of the manipulator is used to represent the whole system. The error-filtered training algorithm is adopted to estimate the unknown weight coefficients of the neural network. The robust item is introduced to suppress the mutual influence and modeling error between the joint neural controllers. The stability of the neural network is proved by Lyapunov function. In comparison with the simulation results without robust item,the decentralized neural robust controller has more precise trajectory tracking accuracy,better error convergence,and higher stability.
In order to eliminate the influence of external disturbance and modeling error on manipulator ′ s trajectory tracking accuracy,the recurrent neural network is used to design the decentralized neural robust controller,and the subsystem of each joint state equation of the manipulator is used to represent the whole system. The error-filtered training algorithm is adopted to estimate the unknown weight coefficients of the neural network. The robust item is introduced to suppress the mutual influence and modeling error between the joint neural controllers. The stability of the neural network is proved by Lyapunov function. In comparison with the simulation results without robust item,the decentralized neural robust controller has more precise trajectory tracking accuracy,better error convergence,and higher stability.
引文
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[3] LEWIS F L,YEGILDIREK A,LIU K. Multilayer neural-net robot controller with guaranteed tracking performance[J]. IEEE transactions on neural networks,1996,7(2):388-399.
[4] VAZQUEZ L A,JURADO F,CASTAENDA C E,et al. Realtime decentralized neural control via backstepping for a robotic arm powered by industrial servomotors[J]. IEEE transactions on neural networks&learning systems,2016,29(2):419-426.
[5] GUO D,LI K,YAN L,et al. The application of Li-function activated RNN to acceleration-level robots′kinematic control via time-varying matrix inversion[C]//2016 Chinese Control and Decision Conference. Yinchuan:IEEE,2016:3455-3460.
[6] MALINOWSKI M T. Fuzzy and set-valued stochastic differential equations with local Lipschitz condition[J]. IEEE transactions on fuzzy systems,2015,23(5):1891-1898.
[7] EKRAMAIN M,HOSSEINNIA S,SHEIKHOLESLAM F. Observer design for non-linear systems based on a generalised Lipschitz condition[J]. Control theory&applications,2011,5(16):1813-1818.
[8] KOSMATOPOULOS E B,POLYCARPOU M M,CHRISTODOULOU M A,et al. High-order neural network structures for identification of dynamical systems[J]. IEEE transactions on neural networks,1995,6(2):422-431.
[9] RASTOGI E,PRASAD L B. Comparative performance analysis of PD/PID computed torque control,filtered error approximation based control and NN control for a robot manipulator[C]//2015 IEEE UP Section Conference on Electrical Computer and Electronics. Allahabad:IEEE,2016:1-6.
[10] JIN Y,FU J,ZHANG L,et al. Filtered-error-based control of a class of nonlinear systems with nonsmooth nonlinearities[C]//2012 IEEE the 51st Cnference on Decision and Control.Maui:IEEE,2012:3463-3468.
[11]方一鸣,李叶红,石胜利,等.液压伺服位置系统的神经网络backstepping控制[J].电机与控制学报,2014,18(6):108-115.FANG Y M,LI Y H,SHI S L,et al. Neural network backstepping control of hydraulic servo position system[J]. Electric machines and control,2014,18(6):108-115.
[12] ATHALYE C D. Necessary condition on Lyapunov functions corresponding to the globally asymptotically stable equilibrium point[C]//2015 IEEE the 54th Conference on Decision and Control. Osaka:IEEE,2015:1168-1173.