Title :
Neural network based adaptive dynamic surface control for flexible-joint robots
Author :
Liu Jinkun ; Guo Yi
Author_Institution :
Sch. of Autom. Sci. & Electr. Eng., Beihang Univ., Beijing, China
Abstract :
A neural network based adaptive dynamic surface controller is proposed for uncertain flexible-joint robot systems. The dynamic surface control method eliminates the problem of “explosion of complexity” existing in traditional backstepping approach by the addition of low pass filters. RBF neural networks are used to approximate the unknown nonlinearities of the model. Nonlinear damping items are used to overcome the external disturbances. Adaptive laws are designed to estimate the weight values of the neural networks and unknown parameters. From Lyapunov stability analysis, it is shown that the control strategy can guarantee the semi-global stability of the closed-loop system and arbitrarily small tracking error by adjusting the controller parameters. Simulation results are presented to validate the good tracking performance of the control system.
Keywords :
Lyapunov methods; adaptive control; closed loop systems; control nonlinearities; low-pass filters; neurocontrollers; nonlinear control systems; radial basis function networks; tracking; uncertain systems; Lyapunov stability analysis; RBF neural networks; adaptive laws; backstepping approach; closed-loop system; control strategy; controller parameters; external disturbances; low pass filters; neural network based adaptive dynamic surface controller; nonlinear damping items; semiglobal stability; tracking error; tracking performance; uncertain flexible-joint robot systems; unknown nonlinearities; weight values; Adaptive systems; Backstepping; Joints; Manipulators; Neural networks; Three-dimensional displays; adaptive; dynamic surface control; flexible-joint robots; neural network;
Conference_Titel :
Control Conference (CCC), 2014 33rd Chinese
Conference_Location :
Nanjing
DOI :
10.1109/ChiCC.2014.6896473
