Adaptive H∞ neural network tracking controller for electrically driven manipulators

A new robust learning controller for rigid-link electrically driven (RLED) manipulators is presented. This new control scheme integrates H_∞ disturbance attenuation design and the direct adaptive neural networks (NN) technique into the well-known computed torque (CT) framework. The role of the NN devices is to adaptively learn the structured and unstructured uncertain dynamics. Then, the effects of the approximation error of the NN devices on the tracking performance are attenuated to a prescribed level by the embedded nonlinear H_∞ control. Based on a tuning-function-like design, each unknown mapping, in the dynamics model of an RLED manipulator, can be learned by only one set NN device in the proposed control structure. Finally, a simulation study for a planar two-link RLED manipulator is given. Simulation results indicate that the proposed adaptive H_∞ NN tracking controller achieves better tracking performances than the standard CT controller