ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Robust/Optimal Temperature Profile Control of a High-Speed Aerospace Vehicle Using Neural Networks

Yadav, V and Padhi, R and Balakrishnan, SN (2007) Robust/Optimal Temperature Profile Control of a High-Speed Aerospace Vehicle Using Neural Networks. In: IEEE Transactions on Neural Networks, 18 (4). 1115-1128 .

[img] PDF
Robust.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy
Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumb...

Abstract

An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A I-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.

Item Type: Journal Article
Publication: IEEE Transactions on Neural Networks
Publisher: IEEE
Additional Information: Copyright 2007 IEEE. Personal use of this material is permitted.However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 26 Apr 2011 06:55
Last Modified: 16 Jan 2013 11:30
URI: http://eprints.iisc.ac.in/id/eprint/36883

Actions (login required)

View Item View Item