Supervisory recurrent fuzzy neural network control for vehicle collision avoidance system design

Mon, Yi-Jen; Lin, Chih‐Min

doi:10.1007/s00521-012-1098-8

Cited by 21 publications

(19 citation statements)

References 19 publications

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“…The parameter tuning laws ˛i and ˇi according to the gradient descent method are given by [31][32][33] …”

Section: On-line Parameter Learningmentioning

confidence: 99%

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On-line constructive fuzzy sliding-mode control for voice coil motors

Hsu

Wong

2016

Applied Soft Computing

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“…The parameter tuning laws ˛i and ˇi according to the gradient descent method are given by [31][32][33] …”

Section: On-line Parameter Learningmentioning

confidence: 99%

“…For improving the generalization capability of fuzzy observer, the full-tuned parameter learning laws l i , r i and c i can be obtained as [31][32][33] …”

Section: On-line Parameter Learningmentioning

confidence: 99%

On-line constructive fuzzy sliding-mode control for voice coil motors

Hsu

Wong

2016

Applied Soft Computing

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“…Though the INTSMC system can achieve favorable control performance, the approximation error introduced by the used neural networks may cause instability of the control system. Thus, an extra compensator, such as the switching compensator (Mon and Lin, 2012) and the supervisory compensator (Chen and Hsu, 2010), should be used. These compensators result in the chattering phenomena so as to cause damage to actuators or plants.…”

Section: Introductionmentioning

confidence: 99%

Intelligent nonsingular terminal sliding-mode control via perturbed fuzzy neural network

Hsu

Lee

Tanaka

2015

Engineering Applications of Artificial Intelligence

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“…To attack this problem, a recurrent fuzzy neural network (RFNN) has been attracting great interest [24][25][26][27]. Unlike a FNN, the RFNN, which involves dynamic elements in the form of feedback connections used as internal memories, performs dynamic mapping.…”

Section: Introductionmentioning

confidence: 99%

Intelligent exponential sliding-mode control with uncertainty estimator for antilock braking systems

Hsu

2015

Neural Comput & Applic

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The purpose of the antilock braking system (ABS) is to regulate the wheel longitudinal slip at its optimum point in order to generate the maximum braking force; however, the vehicle braking dynamic is highly nonlinear. To relax the requirement of detailed system dynamics, this paper proposes an intelligent exponential sliding-mode control (IESMC) system for an ABS. A functional recurrent fuzzy neural network (FRFNN) uncertainty estimator is designed to approximate the unknown nonlinear term of ABS dynamics, and the parameter adaptation laws are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the stable control performance. Since the outputs of the functional expansion unit are used as the output weights of the FRFNN uncertainty estimator, the FRFNN can effectively capture the input-output dynamic mapping. In addition, a nonlinear reaching law, which contains an exponential term of sliding surface to smoothly adapt the variations of sliding surface, is designed to reduce the level of the chattering phenomenon. Finally, the simulation results demonstrate that the proposed IESMC system can achieve robustness slip tracking performance in different road conditions.

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Supervisory recurrent fuzzy neural network control for vehicle collision avoidance system design

Cited by 21 publications

References 19 publications

On-line constructive fuzzy sliding-mode control for voice coil motors

On-line constructive fuzzy sliding-mode control for voice coil motors

Intelligent nonsingular terminal sliding-mode control via perturbed fuzzy neural network

Intelligent exponential sliding-mode control with uncertainty estimator for antilock braking systems

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