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A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme
2017
Engineering applications of artificial intelligence
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This paper represents a novel online self-learning disturbance observer (SLDO) by benefiting from the combination of a type-2 neuro-fuzzy structure (T2NFS), feedback-error learning scheme and sliding mode ...
The SLDO is developed within a framework of feedback-error learning scheme in which a conventional estimation law and a T2NFS work in parallel. ...
Acknowledgement The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000598. ...
doi:10.1016/j.engappai.2017.04.013
fatcat:3okhj73yo5bnlbtwzfitfzzzfa
Feedback Linearization Control for Systems with Mismatched Uncertainties via Disturbance Observers
2018
Asian journal of control
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2021 pre-print arXiv:2103.11292v1 fatcat:k37g2n4z7zcanir323okalqknm
This paper focuses on a novel feedback linearization control (FLC) law based on a self-learning disturbance observer (SLDO) to counteract mismatched uncertainties. ...
In the estimation scheme for the SLDO, the BNDO is used to provide a conventional estimation law, which is used as being the learning error for the type-2 neuro-fuzzy system (T2NFS), and T2NFS learns mismatched ...
observer (DO)
Fig. 2 . 2 The diagram of the self-learning disturbance observer (SLDO)III. ...
doi:10.1002/asjc.1802
fatcat:ptz76tnh2bhcbmw6bubilw5zxi
Robust Course Keeping Control of a Fully Submerged Hydrofoil Vessel without Velocity Measurement: An Iterative Learning Approach
2017
Mathematical Problems in Engineering
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Based on a sampled-data iterative learning strategy, an iterative learning observer is established for the estimation of system states and the generalized disturbances. ...
With the state observer, a feedback linearized iterative sliding mode controller is designed for the stabilization of the lateral dynamics of the fully submerged hydrofoil vessel. ...
State observer based control schemes have been developed for many species of nonlinear systems such as nonlinear time-delay systems [34, 35] , Lipschitz nonlinear systems [24, 26, 36] , and other structured ...
doi:10.1155/2017/7979438
fatcat:odluit3bfnapfhuboh2mmduacm
Adaptive Position Tracking System and Force Control Strategy for Mobile Robot Manipulators Using Fuzzy Wavelet Neural Networks
2014
Journal of Intelligent and Robotic Systems
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In this paper, we propose an adaptive position tracking system and a force control strategy for nonholonomic mobile robot manipulators, which incorporate the merits of Fuzzy Wavelet Neural Networks (FWNNs ...
In general, it is not easy to adopt a model-based method to achieve this control object due to the uncertainties of mobile robot manipulators control system, such as unknown dynamics, disturbances and ...
In addition, adaptive control schemes for nonlinear systems that incorporate the FNNs have also grown rapidly. ...
doi:10.1007/s10846-013-0006-5
fatcat:wsmmq35k5bblfjkzgneoxwkgwu
Internal models in sensorimotor integration: perspectives from adaptive control theory
2005
Journal of Neural Engineering
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The important role played by vestibular system identification in postural control suggests an indirect adaptive control scheme whereby system states or parameters are explicitly estimated prior to the ...
Internal models and adaptive controls are empirical and mathematical paradigms that have evolved separately to describe learning control processes in brain systems and engineering systems, respectively ...
Acknowledgments We thank E Bizzi, N Hogan, J-J Slotine, R Ajemian and D M Wiberg for valuable comments on the manuscript. ...
doi:10.1088/1741-2560/2/3/s01
pmid:16135881
pmcid:PMC2263077
fatcat:nrseuyhlszcdbdm53zrqtb3waq
AUV Trajectory Tracking Models and Control Strategies: A Review
2021
Journal of Marine Science and Engineering
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Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. ...
For each category, a brief description of the control-oriented modeling strategies is given. ...
Conflicts of Interest: The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results ...
doi:10.3390/jmse9091020
fatcat:sbjchl6l3vdg5casa6sf4xy4u4
Optimal control and learning for cyber‐physical systems
2021
International Journal of Robust and Nonlinear Control
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However, the traditional optimal control theory cannot be directly used because it was developed for systems that do not have the complexity level of modern systems we observe today. ...
A successful management of these systems requires enhanced performance in terms of robustness, safety, resiliency, scalability, and usability. ...
The article authored by Guo et al. studies state feedback and output feedback Q-learning of a two-wheeled self-balancing robot (TWSBR). ...
doi:10.1002/rnc.5442
fatcat:2sqn5j3urrgcrbjxfx6vsgvnci
Dual-high-order periodic adaptive learning compensation for state-dependant periodic disturbance
2008
2008 47th IEEE Conference on Decision and Control
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For simplicity, in the sequel, we denote the above statedependent periodic disturbance as a(x). ...
of the system with the DHO-PALC; 2) Experimental study of the DBO-PALC for state-dependent periodic disturbance on a dynamometer position control system; 3) Experimental demonstration of the advantages ...
This lab system can be used as a research platform to test various nonlinear control schemes [16] . 1) Architecture of the Dynamometer: The architecture of the dynamometer control system is shown in ...
doi:10.1109/cdc.2008.4739331
dblp:conf/cdc/LuoCA08
fatcat:ses7ll6rqrcxdhyvcxvzhbc2vi
Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation
2017
Complexity
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Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping ...
This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN) and disturbance observer ...
In [57] , a saturated output feedback tracking control was studied for robot manipulators via fuzzy self-tuning. ...
doi:10.1155/2017/7413642
fatcat:qboauuw5ubgd7hhwgtcib2fbiu
2020 Index IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 50
2020
IEEE Transactions on Systems, Man & Cybernetics. Systems
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
-that appeared in this periodical during 2020, and items from previous years that were commented upon or corrected in 2020. ...
., +, TSMC Feb. 2020 590-599 Adaptive Neural Network Control for a Class of Nonlinear Systems With Error-Driven Nonlinear Feedback Design for Fuzzy Adaptive Dynamic Surface Control of Nonlinear Systems ...
doi:10.1109/tsmc.2021.3054492
fatcat:zartzom6xvdpbbnkcw7xnsbeqy
Robust Adaptive Neural-Network Backstepping Control Design for High-Speed Permanent-Magnet Synchronous Motor Drives: Theory and Experiments
2019
IEEE Access
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This paper presents a robust adaptive backstepping control (RABC) for high-speed permanent-magnet synchronous motor (HSPMSM) drive system. ...
To mitigate the need for the lumped parameter uncertainties within the backstepping controller, an online adaptive observer based on RRBFNN is designed to estimate the nonlinear parameter uncertainties ...
change in the self-feedback loops, respectively. ...
doi:10.1109/access.2019.2930237
fatcat:z3y23yj5kzbuzcuoripisrbmsq
Robust self-learning fuzzy controller design for a class of nonlinear MIMO systems
2000
Fuzzy sets and systems (Print)
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This paradigm is adopted in the design of a new robust self-learning fuzzy controller for a class of nonlinear MIMO systems. ...
Such a phenomenon may be observed in other types of direct fuzzy logic-based learning controllers that utilize an adaptation scheme in which the locational information of the current error state vector ...
In Section 3, we design a robust self-learning fuzzy controller for a class of nonlinear MIMO systems based on the proposed learning paradigm. ...
doi:10.1016/s0165-0114(98)00042-6
fatcat:u4a3dek6dvbjhc63tk6iw755t4
Robust EMRAN-aided Coupled Controller for Autonomous Vehicles
[article]
2022
arXiv
pre-print
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Combined with a self-regulating learning scheme for improving generalization performance, the proposed EMRAN-aided control architecture aids a basic PID cruise and Stanley path-tracking controllers in ...
Using a feedback error learning mechanism, an inverse vehicle dynamics learning scheme utilizing an adaptive Radial Basis Function (RBF) neural network, referred to as the Extended Minimal Resource Allocating ...
A feedback error learning mechanism was employed for learning the inverse dynamics of the vehicle and eliminating the effects of external disturbances and uncertainties. ...
arXiv:2106.11716v3
fatcat:xozcoajf5baqbddk344ai44xhm
Evolution of adaptive learning for nonlinear dynamic systems: a systematic survey
2022
Intelligence & Robotics
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AI in nonlinear dynamical systems and particularly in robotics. ...
In the 1990s, the field of Artificial Neural Networks was hugely investigated in general, and for control of dynamical systems in particular. ...
In 1991, Lin and Kim integrated the CMAC into the self-learning control scheme that was based on the work of Lin and Kim [88] . ...
doi:10.20517/ir.2021.19
fatcat:xwp7dc3j6rdrraumuc5xigzici
Fractional Horsepower Dynamometer - A General Purpose Hardware-In-The-Loop Real-Time Simulation Platform for Nonlinear Control Research and Education
2006
Proceedings of the 45th IEEE Conference on Decision and Control
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This lab system can be used as a research platform to test various nonlinear control schemes. ...
A fractional horsepower dynamometer was developed as a general purpose hardware-in-the-loop real-time simulation platform to emulate mechanical nonlinearities such as frictions, state-dependent disturbances ...
ACKNOWLEDGEMENT We would like to thank the financial support from CSOIS for building the original fractional horsepower dynamometer as a senior design project under the supervision of Dr. Carl Wood. ...
doi:10.1109/cdc.2006.377021
dblp:conf/cdc/TarteC0006
fatcat:5og4zfcurrg2pehby5mjedvbma
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