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| Title: | Dynamic reoptimization of a missile autopilot controller in presence of unmodeled dynamics |
| Author (s): | Unnikrishnan, Nishant Balakrishnan, S. N. |
| Department/Lab Affiliations: | Mechanical & Aerospace Engineering Space Systems Engineering |
| Keywords: | dynamic systems neural networks robust controllers |
| Issue Date: | 2005 |
| Publisher: | American Institute of Aeronautics and Astronautics AIAA |
| Citation: | Unnikrishnan, Nishant and S. N. Balakrishnan. "Dynamic Reoptimization of a Missile Autopilot Controller in Presence of Unmodeled Dynamics," AIAA Guidance, Navigation, and Control Conference and Exhibit (August 2005): 6387. |
| Abstract: | Online trained neural networks have become popular in recent years in designing robust and adaptive controllers for dynamic systems with uncertainties in their system equations because of their universal function approximation property. This paper discusses a technique that dynamically reoptimizes a Single Network Adaptive Critic (SNAC) based optimal controller in the presence of unmodeled uncertainties. The controller design is carried out in two steps: (i) synthesis of a set of online neural networks that capture the uncertainties in the plant equations on-line (ii) re-optimization of the existing optimal controller to drive the states of the plant to a desired reference by minimizing a predefined cost function. The neural network weight update rule for the online networks has been derived using Lyapunov theory that guarantees both the stability of the error dynamics as well as the boundedness of network weights. This approach has been applied in the online re-optimization of a missile longitudinal autopilot controller and numerical results from simulation studies are presented here. |
| Type: | Article - Conference proceedings text |
| In Title: | AIAA Guidance, Navigation, and Control Conference and Exhibit 2005 |
| 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: archiving status unclear; Post-print: author cannot archive; FULL COPYRIGHT INFORMATION: |
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| title | Dynamic reoptimization of a missile autopilot controller in presence of unmodeled dynamics |
| contributor.author | Unnikrishnan, Nishant |
| contributor.author | Balakrishnan, S. N. |
| contributor.deptlab | Mechanical & Aerospace Engineering |
| contributor.deptlab | Space Systems Engineering |
| subject | dynamic systems |
| subject | neural networks |
| subject | robust controllers |
| date.issued | 2005 |
| publisher | American Institute of Aeronautics and Astronautics AIAA |
| identifier.citation | Unnikrishnan, Nishant and S. N. Balakrishnan. "Dynamic Reoptimization of a Missile Autopilot Controller in Presence of Unmodeled Dynamics," AIAA Guidance, Navigation, and Control Conference and Exhibit (August 2005): 6387. |
| identifier.pub.URI | |
| description.abstract | Online trained neural networks have become popular in recent years in designing robust and adaptive controllers for dynamic systems with uncertainties in their system equations because of their universal function approximation property. This paper discusses a technique that dynamically reoptimizes a Single Network Adaptive Critic (SNAC) based optimal controller in the presence of unmodeled uncertainties. The controller design is carried out in two steps: (i) synthesis of a set of online neural networks that capture the uncertainties in the plant equations on-line (ii) re-optimization of the existing optimal controller to drive the states of the plant to a desired reference by minimizing a predefined cost function. The neural network weight update rule for the online networks has been derived using Lyapunov theory that guarantees both the stability of the error dynamics as well as the boundedness of network weights. This approach has been applied in the online re-optimization of a missile longitudinal autopilot controller and numerical results from simulation studies are presented here. |
| type | Article - Conference proceedings |
| type.DCMIType | text |
| 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: archiving status unclear; Post-print: author cannot archive; |
| rights.URI | |
| relation.isPartOf | AIAA Guidance, Navigation, and Control Conference and Exhibit 2005 |
| date.available | 2008-09-23T20:04:17Z |
| identifier.persist.URI |