Constrained Neural Adaptive PID Control for Robot Manipulators07 Jan 2020 10:32
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.
This paper presents very simple constrained PID-like control for nonlinear robot manipulators that do not require system dynamics nor external disturbances. Also, the PID gains are updated automatically where no specific information or extra modification related to gains are required. Also, the stability of the system is guaranteed using kinds of Lyapunov analysis.