Adaptive neural network control for robotic manipulators with guaranteed finite-time convergence

Although adaptive control with neural networks has been widely studied for robotic systems, the classical adaptive laws have been derived by using the gradient algorithm to minimize the tracking error, and thus their sluggish convergence may lead to performance degradation or even affect the operation safety. In this paper, we propose an adaptive neural control strategy for nonlinear robot manipulators with new adaptive laws. We first reformulate the robotic model by defining a set of auxiliary variables to avoid using the joint acceleration signals. Then an adaptive law with the extracted estimation error being new leakage terms is developed to update the unknown weight of the radial basis function neural network (RBFNN) used to address the unknown dynamics. With this new learning algorithm, both the tracking error and estimation error will exponentially converge to a small set around zero, whose size solely depends on the RBFNN approximation error. Moreover, we incorporate the sliding mode technique into the design of adaptive law and feedback control, such that finite-time convergence can be proved. The robustness of the proposed estimation and control methods is also investigated. Finally, simulation results based on a SCARA robot model are provided to demonstrate the effectiveness of the proposed approaches, and illustrate their superior response over classical adaptive control schemes with sigma-modification method. (C) 2019 Elsevier B.V. All rights reserved.