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| Title: | Discrete-time neural network output feedback control of nonlinear discrete-time systems in non-strict form |
| Author (s): | Vance, Jonathan Blake Sarangapani, Jagannathan |
| Department/Lab Affiliations: | Computer Science Electrical and Computer Engineering Engineering Management & Systems Engineering Intelligent Systems Center |
| Keywords: | discrete-time control neural network control non-strict feedback nonlinear system output feedback control |
| Issue Date: | 2008-04 |
| Publisher: | Elsevier |
| Citation: | J. Vance and S. Jagannathan," Discrete-time neural network output feedback control of nonlinear discrete-time systems in non-strict form", Automatica, Vol. 44(4), 2008. |
| Abstract: | An adaptive neural network (NN)-based output feedback controller is proposed to deliver a desired tracking performance for a class of discrete-time nonlinear systems, which are represented in non-strict feedback form. The NN backstepping approach is utilized to design the adaptive output feedback controller consisting of: (1) an NN observer to estimate the system states and (2) two NNs to generate the virtual and actual control inputs, respectively. The non-causal problem encountered during the control design is overcome by using a dynamic NN which is constructed through a feedforward NN with a novel weight tuning law. The separation principle is relaxed, persistency of excitation condition (PE) is not needed and certainty equivalence principle is not used. The uniformly ultimate boundedness (UUB) of the closed-loop tracking error, the state estimation errors and the NN weight estimates is demonstrated. Though the proposed work is applicable for second order nonlinear discrete-time systems expressed in non-strict feedback form, the proposed controller design can be easily extendable to an nth order nonlinear discrete-time system. |
| Type: | Article - Journal text |
| In Title: | Automatica |
| Copyright Notice: | Pre-print: author can archive with restrictions;Restriction: This does not include Cell Press; Post-print: author can archive; 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: |
| Publisher URL: | |
| Link to this page: |
| title | Discrete-time neural network output feedback control of nonlinear discrete-time systems in non-strict form |
| contributor.author | Vance, Jonathan Blake |
| contributor.author | Sarangapani, Jagannathan |
| contributor.deptlab | Computer Science |
| contributor.deptlab | Electrical and Computer Engineering |
| contributor.deptlab | Engineering Management & Systems Engineering |
| contributor.deptlab | Intelligent Systems Center |
| contributor.sponsor | National Science Foundation |
| subject | discrete-time control |
| subject | neural network control |
| subject | non-strict feedback nonlinear system |
| subject | output feedback control |
| date.issued | 2008-04 |
| publisher | Elsevier |
| identifier.citation | J. Vance and S. Jagannathan," Discrete-time neural network output feedback control of nonlinear discrete-time systems in non-strict form", Automatica, Vol. 44(4), 2008. |
| identifier.pub.URI | |
| description.abstract | An adaptive neural network (NN)-based output feedback controller is proposed to deliver a desired tracking performance for a class of discrete-time nonlinear systems, which are represented in non-strict feedback form. The NN backstepping approach is utilized to design the adaptive output feedback controller consisting of: (1) an NN observer to estimate the system states and (2) two NNs to generate the virtual and actual control inputs, respectively. The non-causal problem encountered during the control design is overcome by using a dynamic NN which is constructed through a feedforward NN with a novel weight tuning law. The separation principle is relaxed, persistency of excitation condition (PE) is not needed and certainty equivalence principle is not used. The uniformly ultimate boundedness (UUB) of the closed-loop tracking error, the state estimation errors and the NN weight estimates is demonstrated. Though the proposed work is applicable for second order nonlinear discrete-time systems expressed in non-strict feedback form, the proposed controller design can be easily extendable to an nth order nonlinear discrete-time system. |
| type | Article - Journal |
| type.DCMIType | text |
| type.status | Postprint |
| rights | Pre-print: author can archive with restrictions;Restriction: This does not include Cell Press; Post-print: author can archive; |
| 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 | |
| relation.isPartOf | Automatica |
| date.available | 2008-07-14T20:11:33Z |
| identifier.persist.URI |