Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints

• Published in: Mechanical systems and signal processing 2018-11, Vol.112, p.44-60
• Main Authors: N. Rahimi, Hamed, Howard, Ian, Cui, Lei
• Format: Article
• Language: English
• Subjects: Actuators; Adaptive control; Constraints; Feedback control; Input saturation; Manipulators; Neural networks; Radial basis function neural networks; Robot arms; Robot control; Tangent barrier Lyapunov function; Time-varying asymmetric constraints; Tracking control; Uncertain actuator; Uncertain manipulator; Uncertainty
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2018.03.042

[Display omitted] •Constrained adaptive neural control is developed for uncertain robot manipulators.•Tangent-type asymmetric and time-varying barrier Lyapunov functions are utilized.•Uncertainties in both manipulator dynamics and actuator dynamics are handled.•The semi-globally uniformly ultimately boundedness of all signals is achieved.•The effectiveness of the theoretical developments is verified through simulations. This paper presents a control design for a robotic manipulator with uncertainties in both actuator dynamics and manipulator dynamics subject to asymmetric time-varying joint space constraints. Tangent-type time-varying barrier Lyapunov functionals (tvBLFs) are constructed to ensure no constraint violation and to remove the need for transforming the original constrained system into an equivalent unconstrained one. Adaptive Neural Networks (NNs) are proposed to handle uncertainties in manipulator dynamics and actuator dynamics in addition to the unknown disturbances. Proper input saturation is employed, and it is proved that under the proposed method the stability and semi-global uniform ultimate boundedness of the closed-loop system can be achieved without violation of constraints. The effectiveness of the theoretical developments is verified through numerical simulations.