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| Title: | A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems |
| Author (s): | Padhi, Radhakant Unnikrishnan, Nishant Wang, Xiaohua Balakrishnan, S. N. |
| Department/Lab Affiliations: | Mechanical & Aerospace Engineering Space Systems Engineering |
| Keywords: | SNAC architecture adaptive critic approximate dynamic programming nonlinear control optimal control single network adaptive critic |
| Issue Date: | 2006 |
| Publisher: | Elsevier |
| Citation: | Padhi, Radhakant Unnikrishnan,N. , Wang, X.and Balakrishnan, S. N.A “Single Network Adaptive Critic (SNAC) Architecture for Optimal Control Synthesis of a Class of Nonlinear Systems”, Neural Networks, Vol. 19, 2006, pp. 1648-1660, 2006. |
| Abstract: | Even though dynamic programming offers an optimal control solution in a state feedback form, the method is overwhelmed by computational and storage requirements. Approximate dynamic programming implemented with an Adaptive Critic (AC) neural network structure has evolved as a powerful alternative technique that obviates the need for excessive computations and storage requirements in solving optimal control problems. In this paper, an improvement to the AC architecture, called the “Single Network Adaptive Critic (SNAC)” is presented. This approach is applicable to a wide class of nonlinear systems where the optimal control (stationary) equation can be explicitly expressed in terms of the state and costate variables. The selection of this terminology is guided by the fact that it eliminates the use of one neural network (namely the action network) that is part of a typical dual network AC setup. As a consequence, the SNAC architecture offers three potential advantages: a simpler architecture, lesser computational load and elimination of the approximation error associated with the eliminated network. In order to demonstrate these benefits and the control synthesis technique using SNAC, two problems have been solved with the AC and SNAC approaches and their computational performances are compared. One of these problems is a real-life Micro-Electro-Mechanical-system (MEMS) problem, which demonstrates that the SNAC technique is applicable to complex engineering systems. |
| Type: | Article - Journal text |
| In Title: | Neural Networks Volume 19, Issue 10, December 2006 |
| 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. Pre-print: author can archive with restrictions;Restriction: This does not include Cell Press; Post-print: author can archive; FULL COPYRIGHT INFORMATION: |
| Publisher URL: | |
| Link to this page: |
| title | A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems |
| contributor.author | Padhi, Radhakant |
| contributor.author | Unnikrishnan, Nishant |
| contributor.author | Wang, Xiaohua |
| contributor.author | Balakrishnan, S. N. |
| contributor.deptlab | Mechanical & Aerospace Engineering |
| contributor.deptlab | Space Systems Engineering |
| subject | SNAC architecture |
| subject | adaptive critic |
| subject | approximate dynamic programming |
| subject | nonlinear control |
| subject | optimal control |
| subject | single network adaptive critic |
| date.issued | 2006 |
| publisher | Elsevier |
| identifier.citation | Padhi, Radhakant Unnikrishnan,N. , Wang, X.and Balakrishnan, S. N.A “Single Network Adaptive Critic (SNAC) Architecture for Optimal Control Synthesis of a Class of Nonlinear Systems”, Neural Networks, Vol. 19, 2006, pp. 1648-1660, 2006. |
| identifier.pub.URI | |
| description.abstract | Even though dynamic programming offers an optimal control solution in a state feedback form, the method is overwhelmed by computational and storage requirements. Approximate dynamic programming implemented with an Adaptive Critic (AC) neural network structure has evolved as a powerful alternative technique that obviates the need for excessive computations and storage requirements in solving optimal control problems. In this paper, an improvement to the AC architecture, called the “Single Network Adaptive Critic (SNAC)” is presented. This approach is applicable to a wide class of nonlinear systems where the optimal control (stationary) equation can be explicitly expressed in terms of the state and costate variables. The selection of this terminology is guided by the fact that it eliminates the use of one neural network (namely the action network) that is part of a typical dual network AC setup. As a consequence, the SNAC architecture offers three potential advantages: a simpler architecture, lesser computational load and elimination of the approximation error associated with the eliminated network. In order to demonstrate these benefits and the control synthesis technique using SNAC, two problems have been solved with the AC and SNAC approaches and their computational performances are compared. One of these problems is a real-life Micro-Electro-Mechanical-system (MEMS) problem, which demonstrates that the SNAC technique is applicable to complex engineering systems. |
| type | Article - Journal |
| type.DCMIType | text |
| type.status | Postprint |
| 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 | Pre-print: author can archive with restrictions;Restriction: This does not include Cell Press; Post-print: author can archive; |
| rights.URI | |
| relation.isPartOf | Neural Networks Volume 19, Issue 10, December 2006 |
| date.available | 2008-10-15T22:32:08Z |
| identifier.persist.URI |