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Memory Augmented Neural Network Adaptive Controllers: Performance and Stability
[article]
2021
arXiv
pre-print
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The proposed architecture, in the setting of standard Neural Network (NN) based adaptive control, augments an external working memory to the NN. ...
Through extensive simulations and specific metrics, such as peak deviation and settling time, we show that memory augmentation improves learning significantly. ...
In Section II we propose the Memory Augmented Neural Network (MANN) adaptive controller. ...
arXiv:1905.02832v16
fatcat:cklxss6wsrfwbaqb4yqwl2sjba
Memory Augmented Neural Network Adaptive Controller for Strict Feedback Nonlinear Systems
[article]
2020
arXiv
pre-print
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Here, each NN, in the backstepping NN adaptive controller, is augmented with an external working memory. ...
We propose a novel backstepping memory augmented NN (MANN) adaptive control method for the control of strict feedback non-linear systems. ...
In a very recent paper [25] , we introduced a memory augmented neural network adaptive controller for model reference adaptive control (MRAC) and robot arm trajectory tracking controller. ...
arXiv:1906.05421v7
fatcat:dfrpysrf7rhgdoaqpemhtzkclm
Improved Attention Models for Memory Augmented Neural Network Adaptive Controllers
[article]
2020
arXiv
pre-print
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We introduced a working memory augmented adaptive controller in our recent work. The controller uses attention to read from and write to the working memory. ...
Attention allows the controller to read specific information that is relevant and update its working memory with information based on its relevance. ...
In Section II, we revisit the Memory Augmented Neural Network (MANN) adaptive controller proposed in our recent works [2] , [3] . ...
arXiv:1910.01189v7
fatcat:6xjsmsxbbjfgbmygkebccozu6y
Memory Augmented Neural Network-Based Intelligent Adaptive Fault Tolerant Control for a Class of Launch Vehicles Using Second-Order Disturbance Observer
2021
Mathematical Problems in Engineering
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Simulation results demonstrate the desired performance and the advantages of the proposed control algorithm. ...
This paper focuses on the MANN-based intelligent adaptive fault tolerant control for a class of launch vehicles. ...
Memory Augmented Neural Networks (MANNs). ...
doi:10.1155/2021/9961278
fatcat:vaaf4jhktvdjflq2n7yo74rnty
Adaptive output feedback control of nonlinear systems using neural networks
2000
Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334)
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To handle the uncertainty, the controller, in the form of a dynamic compensator, is augmented by a single hidden layer (SHL) neural network that adjusts on-line for unknown nonlinearities. ...
The parameter update laws for a SHL neural network are derived from stability analysis. Simulations illustrate the theoretical results. ...
Fig.4 represents the closed-loop performance of the output feedback controller without neural network. The tracking performance of the linear controller is very poor in this case. ...
doi:10.1109/acc.2000.879146
fatcat:knqrziiarnfvthssuwksokqrnm
A penalty function method for exploratory adaptive-critic neural network control
2009
2009 17th Mediterranean Conference on Control and Automation
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A constrained penalty function method for exploratory adaptive-critic neural network (NN) control is presented. ...
While constrained approximate dynamic programming has been effective to guarantee closed-loop system performance and stability objectives, in the presence of a change in the plant dynamics it may not have ...
Forgetting: exploratory adaptive function approximation Suppose that an Artificial Neural Network (ANN) is composed of Short Term Memory (STM) and Long Term Memory (LTM) connections and that it has been ...
doi:10.1109/med.2009.5164744
fatcat:z76gzjek5vf2zcoxq6x7quy73a
Nonlinear adaptive flight control using neural networks
1998
IEEE Control Systems
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Feedback linearization and adaptive neural networks provide a powerful controller architecture. ...
A description of the controller architecture and associated stability analysis is given, followed by a more in-depth look at its application to a tiltrotor aircraft. ...
This research supported by ARO, AFWL, AFOSR, and NASA. The authors gratefully acknowledge the feedback received from anonymous reviewers. ...
doi:10.1109/37.736008
fatcat:5horwo6ry5fmdfg2qa7ngrtcvm
Fuzzy PD Control of Networked Control Systems Based on CMAC Neural Network
2012
Mathematical Problems in Engineering
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The cerebellar model articulation controller (CMAC) neural network and a PD controller are combined to achieve the forward feedback control. ...
The network and plant can be regarded as a controlled time-varying system because of the random induced delay in the networked control systems. ...
Acknowledgments This work is supported by the Chinese National Science Foundation no. 61203004 , the Natural Science Foundation of Heilongjiang province no. 42400621-1-12201 , and the Fundamental Research ...
doi:10.1155/2012/347217
fatcat:srnrfoog3nhhtixmtbyrj7xiua
Flight Testing of Reconfigurable Control Law on the X-36 Tailless Aircraft
2001
Journal of Guidance Control and Dynamics
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The reconfigurable control law employs an adaptive neural network to augment a dynamic inversion control law design. ...
Real-time performance and long-term stability of the adaptive neural network based reconfigurable con- trol law was demonstrated through the HILS and flight testing ef- forts. ...
doi:10.2514/2.4826
fatcat:wpswomi6gngejlsgiut3w3kkxm
Implementation of an Adaptive Robust Neural Network Based Motion Controller for Position Tracking of AC Servo Drives
2009
International Journal of Fuzzy Logic and Intelligent Systems
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The neural network with radial basis function is introduced for position tracking control of AC servo drive with the existence of system uncertainties. ...
The stability and the convergence of the system are proved by Lyapunov theory. The validity and robustness of the controller are verified through simulation and experimental results ...
Adaptive Robust Neural Network Controller This section introduces the design of proposed controller for the AC servo drive and the proof of stability. ...
doi:10.5391/ijfis.2009.9.4.294
fatcat:anktkiw7rnauhhrxtt2xbmxdgy
Missile Control Using Fuzzy Cerebellar Model Arithmetic Computer Neural Networks
1997
Journal of Guidance Control and Dynamics
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More importantly, the missile dy- namics is stabilized before the fuzzy CMAC learning process be- gins, which makes the neural network easier to adapt and results in high performance. ...
The inner feedback control level guarantees the stability of the flight control system, and the fuzzy CMAC adaptive learning controller enhances the performance and reliability by compensating for the ...
doi:10.2514/2.4077
fatcat:hltjtnedyfbwnkzlxrhbpfgave
Nonlinear Flight Control Using Neural Networks
1997
Journal of Guidance Control and Dynamics
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Simulation results for an F-18 aircraft model are presented to illustrate the performance of the on-line neural network based adaptation algorithm. ...
The theoretical development of a direct adaptivetracking control architecture using neural networks is presented. ...
Acknowledgments This research was supported by the Naval Air Warfare Center under Contract N62269-91-C-0038,and by the Army Research Ofce under Grant DAAH04-93-G-0002. The authors are grateful to P. ...
doi:10.2514/2.4029
fatcat:kdkhr4smgjci3ebjpjltcupyhm
An adaptive critic global controller
2002
Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301)
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A nonlinear control system comprising a network of networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design ...
The result is an adaptive controller that is as conservative as the linear designs and as effective as the global controller. ...
The neural control architecture specified in Section 4 is initialized based on the performance criteria established locally by the linear gains and the augmented Riccati matrix, with NNF approximating ...
doi:10.1109/acc.2002.1025189
fatcat:avl5bmtpnbhv3fswfu64nwuy4e
Filtering and Control for Unreliable Communication: The Discrete-Time Case
2014
Discrete Dynamics in Nature and Society
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The global exponential stability analysis of discrete-time neural networks is discussed in "Global exponential stability of discrete-time neural networks with time-varying delays" by S. Udpin and P. ...
Niamsup, and some global stability criteria of discrete-time neural networks with time-varying delays are proposed. ...
Acknowledgments This special issue is a timely reflection of the recent research progress in the area of filtering and control for unreliable communication in the discrete-time domain. ...
doi:10.1155/2014/890275
fatcat:6dettkhmxrhhhdreo6flwkr4pa
Decentralized model reference adaptive control without restriction on subsystem relative degrees
1999
IEEE Transactions on Automatic Control
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When the direct model reference adaptive control (MRAC) scheme with first-order local estimators is employed to design totally decentralized controllers, the stability result can only be applied to a system ...
With this analysis, the class of interactions and subsystem unmodeled dynamics can be enlarged to include those having infinite memory. ...
STABILITY OF THE DECENTRALIZED ADAPTIVE CONTROL SYSTEMS We need to establish the robustness of the local adaptive controllers in the presence of ignored interactions, unmodeled dynamics, and external disturbances ...
doi:10.1109/9.774124
fatcat:f3upzpxmxbb75bb2si6hibrkty
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