Adaptive Neural Network Control of an Uncertain Robot With Full State Constraints
This paper studies the tracking control problem for an uncertain {n} link robot with full state constraints. The rigid robotic manipulator is described as a multiinput and multioutput system. Adaptive neural network (NN) control for the robotic system with full state constraints is designed. In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances. The Moore Penrose inverse term is employed in order to prevent the violation of the full state constraints. A barrier Lyapunov function is used to guarantee the uniform ultimate boundedness of the closed loop system. The control performance of the closed loop system is guaranteed by appropriately choosing the design parameters. Simulation studies are performed to illustrate the effectiveness of the proposed control.
IEEE Transactions on Cybernetics