Event sampled adaptive neural network control of robot manipulators
Event based sampling of feedback signals and control inputs are shown to reduce computations. In this paper, the design of event sampled adaptive neural network (NN) state feedback control of robot manipulators is presented in the presence of uncertain robot dynamics. The event sampled NN approximation property is utilized to represent the uncertain nonlinear dynamics of the robotic manipulator which is subsequently employed to generate the control torque. A novel weight tuning rule is designed using the Lyapunov method. Further, the Lyapunov stability theory is utilized to develop the event sampling condition and to demonstrate the tracking performance of the robot manipulator. Finally, simulation results are presented to verify the theoretical claims and to demonstrate the reduction in the computations with event sampled control execution.
2016 International Joint Conference on Neural Networks (IJCNN)