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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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MethodSelf-organizing state aggregation for architecture design of Q-learning
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“Therefore, previous studies have suggested algorithms such as CMAC, ART, and FAST to replace the box decoder for clustering input vectors [1,5,11].”
MethodAdaptive EKF-CMAC-Based Multisensor Data Fusion for Maneuvering Target
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MethodNeural Networks in Feedback Control Systems
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“It has been shown that cerebellar model articulation controller (CMAC) [Albus 1975], plant control u(t) output y(t) NN controller NN system identifier estimated output )(ˆ ty identification error desired output )(tyd plant control u(t) output y(t) NN controller desired output )(tyd tracking…”
BackgroundApplication of CMAC neural network to the control of induction motor drives
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BackgroundAdaptive-resolution reinforcement learning with polynomial exploration in deterministic domains
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“We note that the well-known Cerebellar Model Articulation Controller (CMAC) (Albus 1975) fits into above framework.”
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“These include Q-learning [14], the Dyna architecture [11], Associative Memory [5], and neural-network-based techniques including Brain-Based Devices [6] and CMAC [1].”
MethodBio-inspired adaptive feedback error learning architecture for motor control
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“Inspired on this model, Albus (1975) proposed the CMAC controller capable of learning and retrieving the motor behaviour for joint control .”
MethodDynamic control of a robot arm using CMAC neural networks
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“In addition, the CMAC neural network (Cerebcllar Model Articulation Controller), originally developed by (Albus, 1975), has also been used in neural • "u:laptivc control schemes.”
MethodSelf-organizing CMAC neural networks and adaptive dynamic control
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A fuzzy cerebellar model articulation controller
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“The CMAC, first proposed by [32], is a kind of neural network based on a model of the mammalian cerebellum.”
BackgroundKernel CMAC With Improved Capability
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InfluentialA model of computation and representation in the brain
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“To overcome this obstacle, 1) a network of Gaussian radial basis functions was employed for direct adaptive control within [20]; 2) the cerebellar model articulation controller was exploited to compute triangular or spline fuzzy basis functions by [21]; and 3) a principle about parameter selections based on rigorous p…”
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“Important differences of fuzzy models from other modelling techniques, such as neural networks (Haykin, 1994), CMAC (Albus, 1975) or radial basis function networks (Chen, Cowan & Grant, 1991) are the following. f Fuzzy models integrate the logical processing of information with attractive…”
MethodUse of adaptive resolution for better CMAC learning
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MethodTask translation and integration specification in intelligent machines
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“The method is generally referredto as the CMAC approach, after the name given to it by Albus, theCerebellar Model Articulation Controller (Albus, 1975).”
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“CMAC is classified as a non-fully connected perceptron-like associative memory network with overlapping receptive fields (Albus 1975).”
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“The cerebellar model articulation controller (CMAC) [2,3], developed by Albus, is a simple network architecture which provides the advantages of fast learning and a high convergence rate.”
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“The learning approach expressed as cerebellar model articulation controllers in Albus (1975a, b) has the advantages of local generalization and rapid learning convergence (Wong and Sideris 1992, Lin and Chiang 1997). Although the speed of convergence to the conventional CMAC is much faster than BP, it is still not sui…”
InfluentialLe, T.-L. (2019) · IEEE Access · doi:10.1109/access.2019.2895636 · Cited by 27
“The cerebellarmodel articulation neural network (CMANN) was first proposed in 1975 [12].”
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Diagnosis of liver disease by using CMAC neural network approach
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“Albus [1,2] first introduced the CMAC NN based on the functions of the human cerebellum, which is responsible for muscle control and motor coordination.”
Transferring task models in Reinforcement Learning agents
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“We choose to use the Cerebral Model Articulation Controller (CMAC) [1], a well-known, tile-coding, function approximator [19] shown to have good performance in our experimental domains.”
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Adding Active Learning to LWR for Ping-Pong Playing Robot
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“As a controller that models the structure and function of the cerebellum, the cerebellar model articulation controller (CMAC) [10] has received much attention [11]–[14].”
BackgroundHierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence
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“The cerebellar model articulation controller (CMAC) [6] is a neural network (NN) inspired by the neurophysiological properties of the human cerebellum and is recognized for its localized generalization and rapid algorithmic computations.”
BackgroundOptimal and intelligent motion control scheme for an Ultrasonic-Motor-Driven X-Y stage
Liang, W., Ma, J., Ng, C., et al. (2019) · Mechatronics · doi:10.1016/j.mechatronics.2019.03.004 · Cited by 26
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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