Adaptive design of a fuzzy cerebellar model arithmetic controller neural network

Chen, J.-Y.; Tsai, Pu-Sheng; Wong, Ching‐Chang

doi:10.1049/ip-cta:20041117

Cited by 47 publications

(19 citation statements)

References 10 publications

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“…The learning laws in (18), (20), (21) and (22) call for a proper choice of the learning rates b w , b m , b v and b r . For a small value of the learning rates, the convergence is easy to guarantee; however, the learning speed is slow.…”

Section: Convergence Analysesmentioning

confidence: 99%

“…This network has already been validated for approximating a nonlinear function over a domain of interest to any desired accuracy. The advantages of using CMAC over conventional NN in many practical applications have been presented in recent studies [16][17][18]. The conventional CMAC uses constant binary or triangular receptive-field basis functions.…”

Section: Introductionmentioning

confidence: 99%

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Self-tuning output recurrent cerebellar model articulation controller for a wheeled inverted pendulum control

Chiu

2010

Neural Comput & Applic

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In this study, a model-free self-tuning output recurrent cerebellar model articulation controller (SORC-MAC) is investigated to control a wheeled inverted pendulum (WIP). Since the proposed SORCMAC captures the system dynamics, it has superior capability compared to the conventional cerebellar model articulation controller in terms of an efficient learning mechanism and dynamic response. The dynamic gradient descent method is also adopted to adjust the SORCMAC parameters online. Moreover, an analytical method based on a Lyapunov function is proposed to determine the learning rates of the SORCMAC so that the convergence of the system can be guaranteed. Finally, the effectiveness of the proposed control system is verified by simulations of the WIP control. Simulation results show that the WIP can move forward and backward stably with uncertainty disturbance by using the proposed SORCMAC.

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Section: Convergence Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-tuning output recurrent cerebellar model articulation controller for a wheeled inverted pendulum control

Chiu

2010

Neural Comput & Applic

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“…This network has been already validated that it can approximate a nonlinear function over a domain of interest to any desired accuracy. The advantages of using CMAC over conventional NN in many practical applications have been presented in recent literatures (Gonzalez-Serrano et al 1998;Jan and Hung 2001;Chen et al 2005;Su et al 2006;Wu et al 2006;Peng et al 2005;Li and Leong 2004;Zheng et al 2006;Peng and Chiu 2008). The conventional CMAC uses constant binary or triangular receptive-field basis functions.…”

Section: Introductionmentioning

confidence: 97%

Adaptive output recurrent cerebellar model articulation controller for nonlinear system control

Chiu

2009

Soft Comput

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In this study, an adaptive output recurrent cerebellar model articulation controller (AORCMAC) is investigated for a nonlinear system. The proposed AORCMAC has superior capability to the conventional cerebellar model articulation controller in efficient learning mechanism and dynamic response. The dynamic gradient descent method is adopted to online adjust the AORCMAC parameters. Moreover, the analytical method based on a Lyapunov function is proposed to determine the learningrates of AORCMAC so that the stability of the system can be guaranteed. Furthermore, the variable optimal learningrates are derived to achieve the best convergence of tracking error. Finally, the effectiveness of the proposed control system is verified by the several simulation and experimental results. Those results show that the favorable performance can be obtained by using the proposed AORCMAC.

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“…Based on this property, the neural-network-based controllers have been developed to compensate for the effects of nonlinearities and system uncertainties, so that the stability, convergence and robustness of the control system can be improved [9]. Recently, the cerebellar model articulation control (CMAC) neural network have been adopted widely for the control of complex dynamical systems owing to its fast learning property, good generalization capability, and simple computation compared with the neural network [15][16][17][18][19]. The CMAC neural network is classified as a non-fully connected perception-like associative memory network with overlapping receptive-fields [15].…”

Section: Introductionmentioning

confidence: 99%

Design of intelligent power controller for DC–DC converters using CMAC neural network

Hsu

2007

Neural Comput & Applic

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DC-DC converters are the devices which can convert a certain electrical voltage to another level of electrical voltage. They are very popularly used because of the high efficiency and small size. This paper proposes an intelligent power controller for the DC-DC converters via cerebella model articulation controller (CMAC) neural network approach. The proposed intelligent power controller is composed of a CMAC neural controller and a robust controller. The CMAC neural controller uses a CMAC neural network to online mimic an ideal controller, and the robust controller is designed to achieve L 2 tracking performance with desired attenuation level. Finally, a comparison among a PI control, adaptive neural control and the proposed intelligent power control is made. The experimental results are provided to demonstrate the proposed intelligent power controller can cope with the input voltage and load resistance variations to ensure the stability while providing fast transient response and simple computation.

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Adaptive design of a fuzzy cerebellar model arithmetic controller neural network

Cited by 47 publications

References 10 publications

Self-tuning output recurrent cerebellar model articulation controller for a wheeled inverted pendulum control

Self-tuning output recurrent cerebellar model articulation controller for a wheeled inverted pendulum control

Adaptive output recurrent cerebellar model articulation controller for nonlinear system control

Design of intelligent power controller for DC–DC converters using CMAC neural network

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