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Memory Augmented Neural Network Adaptive Controller for Strict Feedback Nonlinear Systems [article]

Deepan Muthirayan, Pramod P. Khargonekar
2020 arXiv   pre-print
We propose a novel backstepping memory augmented NN (MANN) adaptive control method for the control of strict feedback non-linear systems.  ...  In this work, we consider the adaptive nonlinear control problem for strict feedback nonlinear systems, where the functions that determine the dynamics of the system are completely unknown.  ...  In this paper, we focus on control of a certain class of nonlinear systems, namely strict feedback nonlinear systems. There is a rich history of adaptive control for this class of nonlinear systems.  ... 
arXiv:1906.05421v7 fatcat:dfrpysrf7rhgdoaqpemhtzkclm

2020 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 31

2020 IEEE Transactions on Neural Networks and Learning Systems  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TNNLS Jan. 2020 235-245 Barrier Function-Based Adaptive Control for Uncertain Strict-Feedback Systems Within Predefined Neural Network Approximation Sets.  ...  ., +, TNNLS Dec. 2020 5390-5401 Barrier Function-Based Adaptive Control for Uncertain Strict-Feedback Systems Within Predefined Neural Network Approximation Sets.  ... 
doi:10.1109/tnnls.2020.3045307 fatcat:34qoykdtarewhdscxqj5jvovqy

Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network Controller

Lisheng Pan
2018 Polish Maritime Research  
To study the autonomous learning model of the learning robot for marine resource exploration, an adaptive neural network controller was applied.  ...  In summary, the identification method of underwater robot system based on neural network is effective.  ...  Taking the neural network as the starting point, the nonlinear system identification method based on neural network and the network characteristics commonly used for identification are discussed.  ... 
doi:10.2478/pomr-2018-0115 fatcat:todswaykxndf7fdjsbn3hv6fre

Page 7484 of Mathematical Reviews Vol. , Issue 90M [page]

1990 Mathematical Reviews  
Farrell, Design techniques of neural networks for associative memories (pp. 252-259); N. Basile and M.  ...  Kokotovi¢é, Adaptive track- ing for feedback linearizable SISO systems (pp. 1002-1007); J.-B. Pomet and L.  ... 

Memory Augmented Neural Network Adaptive Controllers: Performance and Stability [article]

Deepan Muthirayan, Pramod P. Khargonekar
2021 arXiv   pre-print
The proposed architecture, in the setting of standard Neural Network (NN) based adaptive control, augments an external working memory to the NN.  ...  In this paper, we propose a novel control architecture, inspired from neuroscience, for adaptive control of continuous-time systems.  ...  In Section II we propose the Memory Augmented Neural Network (MANN) adaptive controller.  ... 
arXiv:1905.02832v16 fatcat:cklxss6wsrfwbaqb4yqwl2sjba


2021 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)  
Adaptive Funnel Control for Strict-Feedback Systems with Tracking Error and State Constraints………………………………………………………………………….Yun Cheng, Xuemei Ren 415 Adaptive and Robust Tracking for Radar Maneuvering Targets  ...  Control for a Class of Unknown Nonlinear Time-varying Systems Using Improved PID Neural Network and Cohen-coon Approach…………………………..……...  ... 
doi:10.1109/ddcls52934.2021.9455485 fatcat:7n7tpgqsuvg55og6dwwuj6g2xe

Neural adaptive control for uncertain nonlinear system with input saturation: State transformation based output feedback

Shigen Gao, Hairong Dong, Bin Ning, Lei Chen
2015 Neurocomputing  
This idea is partially inspired by [29] where adaptive neural control is designed for strict-feedback systems without backstepping.  ...  Concluding Remarks This paper proposes two neural adaptive control methods for a class of nonlinear system by state feedback and output feedback.  ... 
doi:10.1016/j.neucom.2015.02.012 fatcat:znv4mochurapxpaf3zx2mqh5hq

2dof controller parametrization for systems with a single i/o delay

L. Mirkin, Qing-Chang Zhong
2003 IEEE Transactions on Automatic Control  
This note puts forward a parametrization of all stabilizing two-degrees-of-freedom controllers for (possibly unstable) processes with dead-time.  ...  The proposed parametrization is based on a doubly coprime factorization of the plant and takes the form of a generalized Smith predictor (dead-time compensator) feedback part and a finite-dimensional feedforward  ...  In other words, the latter quantity imposes a strict limitation of the achievable disturbance attenuation performance in any dead-time system regardless the choice of the controller.  ... 
doi:10.1109/tac.2003.819286 fatcat:ktqwtwacnvc3bipu4tdpf2ehxu

Adaptive robust control of MIMO nonlinear systems in semi-strict feedback forms

Bin Yao, Masayoshi Tomizuka
2001 Automatica  
Adaptive robust control (ARC) laws are developed for MIMO nonlinear systems transformable to two semi-strict feedback forms.  ...  The results are then used to construct speci"c ARC control laws for MIMO nonlinear systems in the semi-strict-feedback forms.  ...  Acknowledgements The authors would like to thank the anonymous reviewers for their valuable comments in improving the quality of the paper.  ... 
doi:10.1016/s0005-1098(01)00082-6 fatcat:sg5ltxcfyrcp7k5xwcxotdxwfy

Verification for Machine Learning, Autonomy, and Neural Networks Survey [article]

Weiming Xiang and Patrick Musau and Ayana A. Wild and Diego Manzanas Lopez and Nathaniel Hamilton and Xiaodong Yang and Joel Rosenfeld and Taylor T. Johnson
2018 arXiv   pre-print
This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereof.  ...  Autonomy in CPS is enabling by recent advances in artificial intelligence (AI) and machine learning (ML) through approaches such as deep neural networks (DNNs), embedded in so-called learning enabled components  ...  The controller is adapted for nonstrict-feedback systems but can be applied to controlling both nonstrict-and strict-feedback systems.  ... 
arXiv:1810.01989v1 fatcat:a5ax66lsxbho3fuxuh55ypnm6m

Gesture Classification with Hierarchically Structured Recurrent Self-Organizing Maps

Volker Baier, Lorenz Mosenlechner, Matthias Kranz
2007 2007 Fourth International Conference on Networked Sensing Systems  
We derived motion data using a so called Gesture Cube [1], a cubic tangible user interface developed for one-handed control of media appliances in a home environment.  ...  We constructed a hierarchically structured neural network assembly based on recurrent self-organizing maps which is able to process and to classify motion data.  ...  It is an example of sensory augmentation of an object that perceives rolls and records what face it lands on. Neural networks are used for classification here.  ... 
doi:10.1109/inss.2007.4297394 fatcat:nxrgqsnzwfhqrpfuus5b3yio7q

Discrete-time weight updates in neural-adaptive control

D. Richert, K. Masaud, C. J. B. Macnab
2012 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
Typical neural-adaptive control approaches update neural-network weights as though they were adaptive parameters in a continuous-time adaptive control.  ...  We show that digital weight update causes the neural network to estimate a discrete-time model of the system, assuming that state feedback is still applied in continuous time.  ...  When the nonlinearities are not matched, Lyapunov backstepping is appropriate for systems in strict-feedback form.  ... 
doi:10.1007/s00500-012-0918-1 fatcat:laponzu2onarngvrv337vnaru4

Computational intelligence in control

António E. Ruano, Shuzhi Sam Ge, Thierry Marie Guerra, Frank L. Lewis, Jose C. Principe, Matjaž Colnarič
2014 Annual Reviews in Control  
It focuses on four topics within the Computational intelligence area: neural network control, fuzzy control, reinforcement learning and brain machine interfaces.  ...  Subsequently, it addresses the role of computational intelligence in control.  ...  In early works of neural network control theory, much research effort has been made on designing stable adaptive neural network control for single-input-single-output (SISO) continuous-time systems in  ... 
doi:10.1016/j.arcontrol.2014.09.006 fatcat:uo2mqqanz5f55kuo25kfjpobka

Semi-Supervised Trajectory-Feedback Controller Synthesis for Signal Temporal Logic Specifications [article]

Karen Leung, Marco Pavone
2022 arXiv   pre-print
Offline, we synthesize a trajectory-feedback neural network controller via an adversarial training scheme that summarizes past spatio-temporal behaviors when computing controls, and then online, we perform  ...  However, seamlessly incorporating such rules into a robot control policy remains challenging especially for real-time applications.  ...  Deep neural networks provide a computationally tractable way to synthesize STL controllers for complex systems.  ... 
arXiv:2202.01997v1 fatcat:h5y47yhpm5dtnmmtyn5iyv6a5y

2020 Index IEEE Transactions on Circuits and Systems II: Express Briefs Vol. 67

2020 IEEE Transactions on Circuits and Systems - II - Express Briefs  
for Nonlinear Semi-Markov Switching Systems; TCSII Nov. 2020 2622-2626 Qi, X., see Liu, W., 1249-1253 Qian, G., see Dong, F., TCSII Dec. 2020 3587-3591 Qian, G., Ning, X., and Wang, S., Recursive  ...  Constrained Maximum Correntropy Criterion Algorithm for Adaptive Filtering; TCSII Oct. 2020 2229-2233 Qian, J., Lu, M., and Huang, N., Radar and Communication Co-Existence Design Based on Mutual Information  ...  ., +, TCSII Oct. 2020 1954-1958 Global Nested PID Control of Strict-Feedback Nonlinear Systems With Prescribed Output and Virtual Tracking Performance.  ... 
doi:10.1109/tcsii.2020.3047305 fatcat:ifjzekeyczfrbp5b7wrzandm7e
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