Adaptive neural motion control of n-link robot manipulators subject to unknown disturbances and stochastic perturbations

The position tracking control problem for rigid n-link robot manipulators operating under unknown external disturbances and stochastic perturbations is addressed. The robot model is considered to be completely uncertain and therefore the proposed controller uses suitable neural network designs and adaptive bounding algorithms for the approximation of all the unknown non-linear uncertainties and the deterministic and stochastic disturbances while effectively penalises the position tracking error. Stability analysis based on Lyapunov functions proves that all the error variables are bounded in probability; simultaneously, the mean square tracking error enters in finite time in an arbitrarily selected small region around the origin wherein it remains thereafter. The controller performance is evaluated by two representative examples: a two-link and a three-link robot manipulator. An excellent tracking response is verified while the effective approximation achieved by the adaptive neural design is clearly demonstrated.