• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.5
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• pp.299-307
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• 2003

The local structure of CMAC neural networks (NN) results in better and faster controllers for nonlinear dynamical systems. In this paper, we propose a CMAC NN-based disturbance observer and its corresponding controller for a flexible joint robot. The CMAC NN-based disturbance observer compensates for the parametric uncertainties and the external disturbances throughout the entire mechanical system. Finally, a simulation result is given to demonstrate the effectiveness of proposed design method's robust tracking performance.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.3
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• pp.273-281
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• 2001

In this paper, we propose a CMAC(Cerebellar Model Articulation Controller) neural network for controlling a non-linear system. CMAC is a neural network that models the human cerebellum. CMAC uses a table look-up method to resolve the complex non-linear system instead of numerical calculation method. It is very fast learn compared with other neural networks. It does not need a calculation time to generate control signals. The simulation results show that the proposed CMAC controllers for a simple non-linear function and a DC Motor speed control reduce tracking errors and improve the stability of its learning controllers. The validity of the proposed CMAC controller is also proved by the real-time tension control.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.466-477
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• 2000

For developing a CMAC-based adaptive critic learning system to control the cart-pole system, various papers including neural network based learning control schemes as well as an adaptive critic learning algorithm with Adaptive Search Element are reviewed and the adaptive critic learning algorithm for the ASE is integrated into a CMAC controller. Also, quantization problems involved in integrating CMAC into ASE system are studied. By comparing the learning speed of the CMAC system with that of the ASE system and by considering the learning genemlization of the CMAC system with the adaptive critic learning, the applicability of the adaptive critic learning algorithm to CMAC is discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.279-285
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• 2007

The objective of the research is two fold. The first is to design and propose a stable and robust learning control algorithm. The controller is CMAC Learning Controller which consists of a model-based controller, such as LQR or PID, as a reference control and a CMAC. The second objective is to implement a reference control and CMAC at two different sampling rates. Generally, a conventional controller is designed based on a mathematical plant model. However, increasing complexity of the plant and accuracy requirement on mathematical models nearly prohibits the application of the conventional controller design approach. To avoid inherent complexity and unavoidable uncertainty in modeling, biology mimetic methods have been developed. One of such attempts is Cerebellar Model Articulation Computer(CMAC) developed by Albus. CMAC has two main disadvantages. The first disadvantage of CMAC is increasing memory requirement with increasing number of input variables and with increasing accuracy demand. The memory needs can be solved with cheap memories due to recent development of new memory technology. The second disadvantage is a demand for processing powers which could be an obstacle especially when CMAC should be implemented in real-time. To overcome the disadvantages of CMAC, we propose CMAC learning controller with multiple sampling rates. With this approach a conventional controller which is a reference to CMAC at high enough sampling rate but CMAC runs at the processor's unoccupied time. To show efficiency of the proposed method, an inverted pendulum controller is designed and implemented. We also demonstrate it's possibility as an industrial control solution and robustness against a modeling uncertainty.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.271-274
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• 1997

This paper presents the optimization technique for design of a CMAC network by using an evolution strategies(ES). The proposed technique is designed to find the optimal parameters of a CMAC network, which can minimize the learning error between the desired output and the CMAC network's as well as the number of memory used in the CMAC network. Computer simulations demonstrate the effectiveness of the proposed design method.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.349-356
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• 2006

A cartpole system is controlled by a control system consisting of two fuzzy controllers cooperating by a CMAC. Each controller uses 2 different input variables and yields the control force provided to the CMAC. The cooperation is due to training of the CMAC supervised by a judge which selects training information for the CMAC between two fuzzy controllers. The control scheme could be appreciated in terms of the tight structure of the controller, simple cooperating scheme due to the CMAC training, and accomplishing control goal that could not be attained by individual controllers.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.3-13
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• 1996

In orde to reduce huge memory requirements of multidimensional CMAC problems, building a CMAC system by problem decomposition is investigated. Decomposition is based on resolving a displacement vector in cartesian coordinates into unit vectors that define a few lower-dimensional CMACs in the CMAC system. A CMAC system for an an in verse kinematics problem for a planar manipulator was simulated and the performance of the system was evaluated in terms of training and output quality.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.488-493
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• 1989

In this paper, the structure of CMAC address mapping is first revisited, and the address hashing function and the random mapping is discussed in the conventional CMAC implementation. Then the effective size of CMAC memory is derived from the modulus property of the CMAC address vector, and a new hashing function for the effective memory mapping is proposed for a CMAC implementation with feasible memory size and no troublesome random mapping. Finally, the performance of the conventional CMAC learning algorithm and that of the proposed new CMAC scheme arc compared via simulations.

• Journal of Advanced Navigation Technology
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• v.16 no.2
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• pp.211-218
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• 2012

In this paper, we propose a fault injection attack on AES-CMAC, which is defined by IETF. The fault assumption used in this attack is based on that introduced at FDTC'05. This attack can recover the 128-bit secret key of AES-CMAC by using only small number of fault injections. This result is the first known key recovery attack result on AES-CMAC.

• Journal of Korean Society of Steel Construction
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• v.12 no.5 s.48
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• pp.605-615
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• 2000

Cerebellar model articulation controller (CMAC) is introduced and used for the identification of structural dynamic model. CMAC has fascinating features in learning speed. It can learn structural response within a few seconds. Therefore it is suitable for the real time identification structures. Real time identification is required in the control of structure which may be damaged or undergo severe change in mechanical properties due to shrinkage or relaxation etc. In numerical examples, it is shown that CMAC trained with the dynamic response of three-story building can predict responses under not trained earthquakes with allowable error. Finally, CMAC has great potential in structural and control engineering.