Constrained neural adaptive PID control for robot manipulators

• Published in: Journal of the Franklin Institute Vol. 357; no. 7; pp. 3907 - 3923
• Main Author: Rahimi Nohooji, Hamed
• Format: Journal Article
• Language: English
• Published: Elmsford Elsevier Ltd 01.05.2020; Elsevier Science Ltd
• Subjects: Adaptive control; Computer simulation; Constraint modelling; Controllers; Disturbances; Dynamic models; Dynamical systems; Feedback control; Liapunov functions; Neural networks; Nonlinear control; Nonlinear systems; Numerical controls; Proportional integral derivative; Radial basis function; Robot arms; Robot control; Robotics; Signal processing; Stability augmentation; System dynamics
• ISSN: 0016-0032; 1879-2693; 0016-0032
• DOI: 10.1016/j.jfranklin.2019.12.042

The problem of designing an analytical gain tuning and stable PID controller for nonlinear robotic systems is a long-lasting open challenge. This problem becomes even more intricate if unknown system dynamics and external disturbances are involved. This paper presents a novel adaptive neural-based control design for a robot with incomplete dynamical modeling and facing disturbances based on a simple structured PID-like control. Radial basis function neural networks are used to estimate uncertainties and to determine PID gains through a direct Lyapunov method. The controller is further augmented to provide constrained behavior during system operation, while stability is guaranteed by using a barrier Lyapunov function. The paper provides proof that all signals in the closed-loop system are bounded while the constraints are not violated. Finally, numerical simulations provide a validation of the effectiveness of the reported theoretical developments.