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| Title: | Adaptive critic-based neural network controller for uncertain nonlinear systems with unknown deadzones | |
| Author (s): | Pingan He Sarangapani, Jagannathan Balakrishnan, S. N. | |
| Department/Lab Affiliations: | Computer Science Electrical and Computer Engineering Engineering Management & Systems Engineering Mechanical & Aerospace Engineering | |
| Keywords: | Lyapunov approach adaptive control adaptive critic-based neural network controller closed loop systems closed-loop tracking error control nonlinearities control system synthesis deadzone compensation scheme deadzone nonlinearity discrete time systems dynamics approximation learning (artificial intelligence) multilayer neural network controller multilayer perceptrons neurocontrollers nonlinear control systems reinforcement learning scheme tracking performance uncertain nonlinear systems uncertain systems uniform ultimately boundedness unknown deadzones weight estimates | |
| Issue Date: | 2002 | |
| Publisher: | Institute of Electrical and Electronics Engineers | |
| Citation: | Pingan He; Jagannathan, S.; Balakrishnan, S. N. "Adaptive critic-based neural network controller for uncertain nonlinear systems with unknown deadzones" Proceedings of the 41st IEEE Conference on Decision and Control, 2002. 10-13 Dec. 2002 Pages: 955- 960 vol.1 | |
| Abstract: | A multilayer neural network (NN) controller in discrete-time is designed to deliver a desired tracking performance for a class of nonlinear systems with input deadzones. This multilayer NN controller has an adaptive critic NN architecture with two NNs for compensating the deadzone nonlinearity and a third NN for approximating the dynamics of the nonlinear system. A reinforcement learning scheme in discrete-time is proposed for the adaptive critic NN deadzone compensator, where the learning is performed based on a certain performance measure, which is supplied from a critic. The adaptive generating NN rejects the errors induced by the deadzone whereas a second NN based critic generates a signal, which is used to tune the weights of the action generating NN so that the deadzone compensation scheme becomes adaptive whereas a third multilayer NN simultaneously approximate the nonlinear dynamics of the system. Using the Lyapunov approach, the uniform ultimately boundedness (UUB) of the closed-loop tracking error and weight estimates of action generating NN, critic NN and the third NN are shown by using a novel weight update. | |
| Type: | Article - Conference proceedings text | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: | |
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| title | Adaptive critic-based neural network controller for uncertain nonlinear systems with unknown deadzones | |
| contributor.author | Pingan He | |
| contributor.author | Sarangapani, Jagannathan | |
| contributor.author | Balakrishnan, S. N. | |
| contributor.deptlab | Computer Science | |
| contributor.deptlab | Electrical and Computer Engineering | |
| contributor.deptlab | Engineering Management & Systems Engineering | |
| contributor.deptlab | Mechanical & Aerospace Engineering | |
| subject | Lyapunov approach | |
| subject | adaptive control | |
| subject | adaptive critic-based neural network controller | |
| subject | closed loop systems | |
| subject | closed-loop tracking error | |
| subject | control nonlinearities | |
| subject | control system synthesis | |
| subject | deadzone compensation scheme | |
| subject | deadzone nonlinearity | |
| subject | discrete time systems | |
| subject | dynamics approximation | |
| subject | learning (artificial intelligence) | |
| subject | multilayer neural network controller | |
| subject | multilayer perceptrons | |
| subject | neurocontrollers | |
| subject | nonlinear control systems | |
| subject | reinforcement learning scheme | |
| subject | tracking performance | |
| subject | uncertain nonlinear systems | |
| subject | uncertain systems | |
| subject | uniform ultimately boundedness | |
| subject | unknown deadzones | |
| subject | weight estimates | |
| date.issued | 2002 | |
| date.submitted | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers | |
| identifier.citation | Pingan He; Jagannathan, S.; Balakrishnan, S. N. "Adaptive critic-based neural network controller for uncertain nonlinear systems with unknown deadzones" Proceedings of the 41st IEEE Conference on Decision and Control, 2002. 10-13 Dec. 2002 Pages: 955- 960 vol.1 | |
| identifier.issn | 0191-2216 | |
| identifier.pub.URI | ||
| description.abstract | A multilayer neural network (NN) controller in discrete-time is designed to deliver a desired tracking performance for a class of nonlinear systems with input deadzones. This multilayer NN controller has an adaptive critic NN architecture with two NNs for compensating the deadzone nonlinearity and a third NN for approximating the dynamics of the nonlinear system. A reinforcement learning scheme in discrete-time is proposed for the adaptive critic NN deadzone compensator, where the learning is performed based on a certain performance measure, which is supplied from a critic. The adaptive generating NN rejects the errors induced by the deadzone whereas a second NN based critic generates a signal, which is used to tune the weights of the action generating NN so that the deadzone compensation scheme becomes adaptive whereas a third multilayer NN simultaneously approximate the nonlinear dynamics of the system. Using the Lyapunov approach, the uniform ultimately boundedness (UUB) of the closed-loop tracking error and weight estimates of action generating NN, critic NN and the third NN are shown by using a novel weight update. | |
| type | Article - Conference proceedings | |
| type.DCMIType | text | |
| type.status | Final version | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights.URI | ||
| date.accessioned | 2007-04-05T14:15:57Z | |
| date.available | 2007-04-05T14:15:57Z | |
| identifier.persist.URI | ||
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