Adaptive Neural Tracking Control of Robotic Manipulators with Guaranteed NN Weight Convergence

Although adaptive control for robotic manipulators has been widely studied, most of them require the acceleration signals of the joints, which are usually difficult to measure directly. Although neural networks (NNs) have been used to approximate the unknown nonlinear dynamics in the robotic systems, the conventional adaptive laws for updating the NN weights cannot guarantee that the obtained NN weights converge to their ideal values, which could degrade the tracking control response. To address these two issues, a new adaptive algorithm with the extracted NN weights error is incorporated into adaptive control, where a novel leakage term is superimposed on the gradient method. By using the Lyapunov approach, the convergence of both the tracking error and the estimation error can be guaranteed simultaneously. In addition, two auxiliary functions are introduced to reformulate the robotic model for designing the adaptive law, and a filter operation is used to avoid measuring the acceleration signals. Comparisons to other well-recognized adaptive laws are given, and extensive simulations based on a 2-DOF SCARA robotic system are given to verify the effectiveness of the proposed control strategy.