Modified State Observer based Decentralized Neuroadaptive Controller for Large Scale Interconnected Uncertain Systems
A decentralized neural network controller design for large scale system with multiple uncertainties is presented in this paper. Controller synthesis relies primarily on an observer design, called the modified state observer (MSO). The uncertainties in the system are calculated online with the MSO. The MSO formulation of uncertainty estimation has two tunable gains which allow for fast estimation of the uncertainties and yet avoid high frequency oscillations that are usually observed with typical model reference adaptive controllers. Lyapunov analysis is used to show the stability of the system. Efficacy of the proposed controller is demonstrated using a bench mark numerical example.
A. Ghafoor et al., "Modified State Observer based Decentralized Neuroadaptive Controller for Large Scale Interconnected Uncertain Systems," Proceedings of the 2018 Annual American Control Conference (2018, Milwaukee, WI), pp. 1701 - 1706, Institute of Electrical and Electronics Engineers (IEEE), Jun 2018.
The definitive version is available at https://doi.org/10.23919/ACC.2018.8431513
2018 Annual American Control Conference, AAC (2018: Jun. 27-29, Milwaukee, WI)
Mechanical and Aerospace Engineering
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Article - Conference proceedings
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jun 2018
This research was supported in part by the National Aeronautics and Space Administration under Grant NNX15AM51A