Adaptive H∞ neural network tracking controller for electrically driven manipulators

A new robust learning controller for uncertain rigid-link electrically driven (RLED) manipulators is presented. This new contrcll 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 NPJ devices is to adaptively learn the structured and unstructured uncertain dynamics. Then, the effects of the approximation error of the NPJ devices on the tracking performance are attenuated to a prescribed level by the embedded nonlinear H, control. Via 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. For economic reasons, this thrift usage of the NN devices is preferred. 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.