Constrained neural adaptive PID control for robot manipulators
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 c...
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| Published in | Journal of the Franklin Institute Vol. 357; no. 7; pp. 3907 - 3923 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Elmsford
Elsevier Ltd
01.05.2020
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0016-0032 1879-2693 0016-0032 |
| DOI | 10.1016/j.jfranklin.2019.12.042 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Rahimi Nohooji, Hamed |
| Author_xml | – sequence: 1 givenname: Hamed surname: Rahimi Nohooji fullname: Rahimi Nohooji, Hamed email: hamed.rahimi@uclouvain.be organization: Center for Research in Mechatronics; Institute of Mechanics, Materials, and Civil Engineering; Institute of Neuroscience; and Louvain Bionics, Universitécatholique de Louvain, Belgium |
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| Snippet | The problem of designing an analytical gain tuning and stable PID controller for nonlinear robotic systems is a long-lasting open challenge. This problem... |
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| SubjectTerms | 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 |
| Title | Constrained neural adaptive PID control for robot manipulators |
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