Neural Adaptive Backstepping Control of a Robotic Manipulator With Prescribed Performance Constraint

• Published in: IEEE transaction on neural networks and learning systems 2019-12, Vol.30 (12), p.3572-3583
• Main Authors: Guo, Qing, Zhang, Yi, Celler, Branko G., Su, Steven W.
• Format: Article
• Language: English
• Subjects: Actuators; Adaptive control; Adaptive estimation law; adaptive neural network (NN) control; Adaptive systems; Artificial neural networks; Backstepping; Computer simulation; Derivatives; Economic models; Integrals; Iterative methods; manipulator; Manipulator dynamics; Manipulators; Neural networks; prescribed performance constraint (PPC); Radial basis function; Robot arms; Robot control; Robust control; Tracking errors; two-degree-of-freedom (Two-DOF); weighted performance function
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2018.2854699

This paper presents an adaptive neural network (NN) control of a two-degree-of-freedom manipulator driven by an electrohydraulic actuator. To restrict the system output in a prescribed performance constraint, a weighted performance function is designed to guarantee the dynamic and steady tracking errors of joint angle in a required accuracy. Then, a radial-basis-function NN is constructed to train the unknown model dynamics of a manipulator by traditional backstepping control (TBC) and obtain the preliminary estimated model, which can replace the preknown dynamics in the backstepping iteration. Furthermore, an adaptive estimation law is adopted to self-tune every trained-node weight, and the estimated model is online optimized to enhance the robustness of the NN controller. The effectiveness of the proposed control is verified by comparative simulation and experimental results with Proportional-integral-derivative and TBC methods.