Robust control of robot manipulators based on H∞ neural network

The trajectory tracking of a class of robot manipulators with disturbance and parameter uncertainties is considered. This paper proposes a adaptive neural-variable Structure control algorithm based on H_∞ theory. The controller that integrates neutral network with variable by way of smooth function. This integrated controller can reduce chattering of variable structure control input, overcome the deficiencies of local generalization neural networks and improve control precision and convergence speed. In addition, This controller is still able to ensure the system maintains good robustness and stability in the case of neural network disabled. the integrated controller is used to adaptive learn and compensate the unknown system, approach error as disturbance is eliminated by robust controller. Robust controller and compensator with robust adaptive laws are constructed based on stability of closed-loop system stability. The simulation results show the presented method is possesses great control accuracy and dynamic function.