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A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)
Abstract
CM AC is an adaptive system by which control functions for many degrees of freedom operating simultaneously can be computed by referring to a table rather than by mathematical solution of simultaneous equations. CM AC combines input commands and feedback variables into an input vector which is used to address a memory where the appropriate output variables are stored. Each address consists of a set of physical memory locations, the arithmetic sum of whose contents is the value of the stored variable. The CM AC memory addressing algorithm takes advantage of the continuous nature of the control function in a way which promises to make it possible to store the necessary data in a physical memory of practical size.
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“In 1975, Albus proposed CMAC, a new type of NN as an alternative to MLP [11].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [11]
In the citing paper
“These include the Backpropagation (BP) algorithm, the Cerebellar Model Articulation Controller (CMAC) NN model [11], the Expectation Maximization (EM) algorithm [115], the to-be-referred-to as Temporal Difference (TD) learning [478], and the Iterative Dichotomiser 3 (ID3) algorithm [373].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [11]
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Items 701–800 of 999
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Apostolos, M. K., Littman, M. G., Lane, S. H., et al. (1996) · Computers & Mathematics with Applications · doi:10.1016/0898-1221(96)00081-8 · Cited by 11
Robust Intelligent Backstepping Longitudinal Control of Vehicle Platoons with H Tracking Performance
Peng, Y.-F., Hsu, C., Lin, C., et al. (2006) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2006.385037 · Cited by 11
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BackgroundAutomated misplaced component inspection for printed circuit boards
Enke, D. L., & Dağli, C. H. (1997) · Computers & Industrial Engineering · doi:10.1016/s0360-8352(97)00115-0 · Cited by 11
REINFORCEMENT LEARNING FOR DECISION-MAKING IN A BUSINESS SIMULATOR
García, J., Borrajo, F., & Fernández, F. (2012) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622012500277 · Cited by 10
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Zhang, L., Cao, Q., Lee, J., et al. (2004) · Neural Processing Letters · doi:10.1023/b:nepl.0000039430.04088.78 · Cited by 11
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Župerl, U., & Čuš, F. (2011) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.1277 · Cited by 11
“[ 15] 4.”
BackgroundInverse dynamics of speech motor control
Hirayama, M., Vatikiotis‐Bateson, E., & Kawato, M. (1994) · Cited by 11
Zhao, J., Lin, L.-Y., & Lin, C. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/8073279 · Cited by 12
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Adeli, M., Zarabadipour, H., & Shoorehdeli, M. A. (2011) · International Conference on Control, Instrumentation, and Automation · doi:10.1109/icciautom.2011.6356691 · Cited by 11
Chen, J. (2005) · Proceedings of ... IEEE International Conference on Fuzzy Systems · doi:10.1109/fuzz.2001.1009094 · Cited by 11
Neural Networks for Function Approximation in Nonlinear Control
Linse, D. J., & Stengel, R. F. (1990) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.1990.4790819 · Cited by 11
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Wang, F., & Saridis, G. N. (2003) · IEEE International Symposium on Intelligent Control · doi:10.1109/isic.1988.65400 · Cited by 11
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Takeda, M., Nakamura, T., Imai, M., et al. (2000) · Advanced Robotics · doi:10.1163/156855300741852 · Cited by 11
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Macnab, C. J. B., & D’Eleuterio, G. M. T. (2000) · The International Journal of Robotics Research · doi:10.1177/02783640022067003 · Cited by 11
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Tsai, C.-R., & Lee, T. (1998) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.2801308 · Cited by 11
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Song, B., & Koivo, A. J. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1998.677085 · Cited by 11
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Huang, K.-L., Hsieh, S. H., & Fu, H.-C. (2002) · doi:10.1109/icnn.1997.611626 · Cited by 11
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Chan, L., & Asokanthan, S. F. (2001) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2001.945687 · Cited by 11
Macnab, C. J. B., & D’Eleuterio, G. M. T. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1998.677025 · Cited by 11
Learning Control of Robotic Manipulators Based on Neurological Model CMAC
Kano, H., & Takayama, K. (1990) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)51743-7 · Cited by 11
Computational significance of the cellular mechanisms for synaptic plasticity in Purkinje cells
Houk, J. C., & Alford, S. T. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081735 · Cited by 11
Selection of network and learning parameters for an adaptive neural robotic control scheme
Sundararajan, N., Chin, L., & San, Y. K. (1993) · Mechatronics · doi:10.1016/0957-4158(93)90061-6 · Cited by 11
Miller, W. T., Box, B., Whitney, E. C., et al. (1990) · neural information processing systems · Cited by 11
ATM scheduling with queuing delay predictions
Schwartz, D. B. (1993) · doi:10.1145/166237.166257 · Cited by 11
A Theory for Memory-Based Learning
Lin, J.-H., & Vitter, J. S. (1994) · Machine Learning · doi:10.1023/a:1022667616941 · Cited by 10
Chen, H., & Gu, F. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.12.044 · Cited by 10
“The CMAC was first proposed by Albus in 1975 (Albus, 1975).”
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Harib, M., Chaoui, H., & Miah, S. (2022) · Intelligence & Robotics · doi:10.20517/ir.2021.19 · Cited by 16
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He, H., & Li, Y. (2008) · Journal of Iron and Steel Research International · doi:10.1016/s1006-706x(08)60245-9 · Cited by 10
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Kurozumi, R., & Yamamoto, T. (2005) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.125.607 · Cited by 10
Wang, S.-Y., Tseng, C.-L., Lin, S.-C., et al. (2015) · Sensors · doi:10.3390/s150407323 · Cited by 10
“In 1975, Albus [9] presented a subclass of NNs—the CMAC—to address these problems.”
BackgroundDiscrete-time weight updates in neural-adaptive control
Richert, D., Masaud, K., & Macnab, C. J. B. (2012) · Soft Computing · doi:10.1007/s00500-012-0918-1 · Cited by 10
“The CMAC basis functions have hypercube domains, offering significantly easier implementation than other neural networks in real-time control systems (Albus 1975a, b).”
BackgroundRFCMAC: A novel reduced localized neuro-fuzzy system approach to knowledge extraction
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“The concept of a cerebellar-model articulation controller (CMAC) was first proposed by Albus (1975).”
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“For the low-level layer, a fuzzy cerebellar model articulation controller (CMAC) [29]–[31]-based adaptive controller is designed.”
BackgroundTan, N., Li, C., Yu, P., et al. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109267 · Cited by 10
Yan, C., Zhang, H., & Wu, L. (2009) · Energy and Power Engineering · doi:10.4236/epe.2009.11002 · Cited by 10
“Albus presented the CMAC neural network and applied it to the robotic manipulator control in 1975 [14].”
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Engineering design of a multirate nonlinear controller for robot manipulators
Machado, J. A. T., & Carvalho, J. L. D. M. (1989) · Journal of Robotic Systems · doi:10.1002/rob.4620060102 · Cited by 10
Tikhonov training of the CMAC neural network
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“I. INTRODUCTION THE cerebellar model articulation controller (CMAC) is anonlinear adaptive system proposed by Albus in the mid1970s [1] with built-in simple computation, local generalization and fast learning properties [2].”
MethodTaniguchi, T., & Sawaragi, T. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.10.007 · Cited by 10
“There are standard methods in function approximation, e.g. Gaussian functions, normalized Gaussian functions (Sato & Ishii, 2000), polynomial functions (Okada, Tatani, & Nakamura, 2002), CMAC (Albus et al., 1975), and other basis functions can be used as b F mi and b Σ mi .”
SYNCHRONIZATION OF CHAOTIC GYRO SYSTEMS USING RECURRENT WAVELET CMAC
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“A cerebellar model articulation controller (CMAC) is classified as a non-fully connected perceptron-like associative memory network with over-lapping receptive fields (Albus 1975).”
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Shi, X., Xiong, H., Wang, C., et al. (2006) · doi:10.1109/icma.2005.1626901 · Cited by 10
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Inderbitzin, M., Herreros-Alonso, I., & Verschure, P. (2010) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn.2010.5596874 · Cited by 11
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Liao, Y., Koiwai, K., & Yamamoto, T. (2018) · Asian Journal of Control · doi:10.1002/asjc.1806 · Cited by 10
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Austin, J., & Lees, K. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00309-x · Cited by 10
“We have evaluated CMAC encoding [1] in a time-series predictor [14], and have developed adaptive 66 J.”
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Bai, Y., & Zhuang, H. (2005) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2004.1401025 · Cited by 10
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Mukaibo, Y., Park, S., & Maéno, T. (2004) · Cited by 10
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Chen, C. (2004) · doi:10.1109/wi.2003.1241172 · Cited by 10
“Based on the previous analyses, Albus’s [9], [10] CMAC (Cerebellar Model Arithmetic Computer) neural network model has a high potential in development of effective data mining techniques owing to its fast learning property, good local generalization capability, native parallel and distributed processing, and ease of i…”
ResultOn-line learning control of manipulators based on artificial neural network models
Cılız, M. K., & Işık, C. (1997) · Robotica · doi:10.1017/s0263574797000337 · Cited by 10
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Hung, C.-P., & Wang, M.-H. (2003) · Springer eBooks · doi:10.1007/978-1-4471-3744-3_1 · Cited by 10
“Brief description of digital CMAC neural network Figure 1 schematically depicts the digital CMAC networks [9,11], which like the models of the human memory to perform a reflexive processing.”
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Hsu, Y.-P., Hwang, K., & Wang, J. (2002) · doi:10.6688/jise.2002.18.2.1 · Cited by 10
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Lin, C.-S., & Li, C.-K. (2002) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1996.571298 · Cited by 10
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Schaal, S. (2000) · Studies in cognitive systems · doi:10.1007/978-94-010-0870-9_67 · Cited by 10
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Lin, J.-H., & Vitter, J. S. (1994) · Machine Learning · doi:10.1007/bf00993469 · Cited by 10
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Hilmi, G. (2003) · Cited by 10
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Song, Y. D. (1998) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.1998.707299 · Cited by 10
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Peterson, T., Owens, N. E., & Carroll, J. L. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2001.932602 · Cited by 10
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“Cerebellar Model Articulation Controller (CMAC) [11, 12] neural network is used to generate joint angles for the proper balancing of a biped walking robot.”
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Cheng, K.-H. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.08.011 · Cited by 9
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Wright, J., & Jordanov, I. (2012) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252537 · Cited by 9
“A Cerebellar Model Articulation Controller or CMAC network is a type of associative memory network based on the cerebellum [41].”
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Li, T., Ogihara, M., & Zhu, S. (2003) · Intelligent Data Analysis · doi:10.3233/ida-2003-7304 · Cited by 9
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Wakitani, S., Nawachi, T., Martins, G. R., et al. (2013) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2013.p0690 · Cited by 9
Wang, S.-Y., Lin, C.-M., & Li, C.-H. (2021) · Applied Sciences · doi:10.3390/app11041567 · Cited by 9
Research on Walking Gait of Biped Robot Based on a Modified CPG Model
Lu, Q., & Tian, J. (2015) · Mathematical Problems in Engineering · doi:10.1155/2015/793208 · Cited by 9
“Albus [21] developed CMAC as a control method based on the principles of the cerebellum’s behavior.”
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Heider, D., Piovoso, M. J., & Gillespie, J. W. (1998) · Journal of Thermoplastic Composite Materials · doi:10.1177/089270579801100604 · Cited by 9
Shen, Z., Guo, C. G., & Zhang, N. (2010) · Neurocomputing · doi:10.1016/j.neucom.2009.03.021 · Cited by 9
Chao, K., Liao, B.-J., & Hung, C.-P. (2013) · International Journal of Photoenergy · doi:10.1155/2013/839621 · Cited by 9
“The CMAC theory was proposed by Albus in the 1970s [13] to imitate the neurological structure of the human cerebellum for achieving rapid learning and reactions.”
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Macnab, C. J. B. (2010) · Neural Processing Letters · doi:10.1007/s11063-010-9154-9 · Cited by 9
“Details on the implementation of the CMAC can be found in [9].”
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Jiang, J., & Kamel, M. S. (2007) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/ijcnn.2007.4370928 · Cited by 9
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Nguyen, M. N., Shi, D., & Fu, J. (2008) · Neurocomputing · doi:10.1016/j.neucom.2007.11.016 · Cited by 9
“The Cerebellar Model Articulation Controller (CMAC) [13, 14] was first proposed by Albus.”
BackgroundAircraft landing control in wind shear condition
Lee, C.-L., & Juang, J. (2011) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2011.6016885 · Cited by 9
“CMAC is a type of artificial neural network proposed in the literatures [16].”
BackgroundTeddy, S. D., Quek, C. H., Lai, E. M.-K., & Çınar, A. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2009.2036726 · Cited by 9
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Thompson, R. F. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081887 · Cited by 9
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“CMACs [1] usefilters mapping sensor-based inputs to a set of activated cells.”
Fundamentals of Intelligent Control
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Lin, X., Duan, P., Zheng, Y., Cai, W., & Zhang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2959667 · Cited by 11
“Available: https://arxiv.org/ abs/1712.00960 [28] J. S. Albus, ‘‘A new approach to manipulator control: The cerebellar model articulation controller (CMAC),’’ J. Dyn.”
MethodInfluentialKong, Z., Zhang, Y., Wang, X., et al. (2020) · Journal of Environmental Engineering and Landscape Management · doi:10.3846/jeelm.2020.12098 · Cited by 9
“Modelling the structure and function of the cerebellum, CMAC was put forward by Albus for manipulator control problems (Albus, 1975a).”
BackgroundMethodInfluentialApplications of Cerebellar Model Articulation Controllers to Intelligent Landing System
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“CMAC is a type of artificial neural network proposed in the literatures [Albus, 1975].”
BackgroundMethodLee, Z.-J. (2007) · Applied Intelligence · doi:10.1007/s10489-007-0080-0 · Cited by 9
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Fischer, T., Rapela, D., & Woern, H. (1999) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim.1999.803206 · Cited by 9
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Brooks, M. (1990) · Springer eBooks · doi:10.1007/978-3-642-84051-7_3 · Cited by 9
“Biological theories of motor learning were developed in the late 1960's by Marr [14] and Albus [2].”
BackgroundInfluentialPerhaps it's time to completely rethink cerebellar function
Bower, J. M. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081553 · Cited by 9
Dynamic control of a parallel link manipulator using a CMAC neural network
Geng, Z., & Haynes, L. S. (1993) · Computers & Electrical Engineering · doi:10.1016/0045-7906(93)90049-w · Cited by 9
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Continuous CMAC-QRLS and Its Systolic Array
Qin, T. G., Zhang, H., Chen, Z., et al. (2005) · Neural Processing Letters · doi:10.1007/s11063-004-2694-0 · Cited by 9
“Three decades ago, the cerebellar model articulation controller (CMAC) was first proposed by Albus [1,2].”
Background
Metadata
- Authors
- Albus, J. S.
- Affiliations
- National Institute of Standards and Technology (US)
- Corresponding author
- Albus, J. S.
- Venue
- Journal of Dynamic Systems, Measurement, and Control
- Publisher
- ASME International
- Published
- 1975-09-01
- Volume
- 97
- Issue
- 3
- Pages
- 220-227
- ISSN
- 0022-0434 (ISSN-L)
- 1528-9028
- OpenAlex
- W1993740947
- Cited by
- 2,177
- References
- 0
- Citations by year
- 2026: 9 · 2025: 18 · 2024: 18 · 2023: 19 · 2022: 21 · 2021: 19 · 2020: 30 · 2019: 39 · 2018: 44 · 2017: 59 · 2016: 33 · 2015: 38 · 2014: 55 · 2013: 58 · 2012: 63
- FWCI
- 1.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Dynamic Systems Measurement and Control (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
- Keywords
- cerebellar model articulation controller, CMAC, adaptive control, robot manipulator control, multivariable control, memory addressing, look-up table, feedback control, neural network control
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