Adaptive neuro-fuzzy wheel slip control

Ćirović, Velimir; Aleksendrić, Dragan

doi:10.1016/j.eswa.2013.03.012

Cited by 27 publications

(11 citation statements)

References 30 publications

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“…Taking into consideration that very complex and highly non-linear phenomena are involved in the braking process, a suffi ciently accurate analytical model of brake performance is almost impossible to obtain [245][246][247][248][249]. The reason for that lies in the fact that the coeffi cient of friction of a brake friction pair has to be considered as a non-linear parameter.…”

Section: Neuro-genetic Optimization Of the Performance Of A Brake Frimentioning

confidence: 99%

Composite materials – modelling, prediction and optimization

Aleksendrić

Carlone

2015

Soft Computing in the Design and Manufacturing of Composite Materials

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Section: Neuro-genetic Optimization Of the Performance Of A Brake Frimentioning

confidence: 99%

Composite materials – modelling, prediction and optimization

Aleksendrić

Carlone

2015

Soft Computing in the Design and Manufacturing of Composite Materials

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“…Initialize the q-dimensional search space by setting its boundaries, the maximum number of iterations max μ , and the parameters in (12).…”

Section: Pso Algorithm-based Fuzzy Modeling Approachmentioning

confidence: 99%

“…A discussion on fuzzy models for ABSs is included in [6]. Fuzzy models are involved in intelligent ABS controllers in various control strategies such as type-2 fuzzy control [7], PI and PID fuzzy control [8], sliding mode fuzzy control [9], two-level fuzzy robust cooperative control [10], neuro-fuzzy control [11], [12], stable and optimal state feedback fuzzy control [4], [13].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization of fuzzy models for Anti-Lock Braking Systems

Precup

Sabau

Dragoş

et al. 2014

2014 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)

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This paper suggests a Particle Swarm Optimization (PSO) approach to the optimal tuning of fuzzy models for Antilock Braking Systems (ABSs). A set of ten local state-space models of the ABS is first obtained by the linearization of the nonlinear state-space model of the ABS process at ten operating points. The initial Takagi-Sugeno (T-S) fuzzy models are next obtained by the modal equivalence principle, namely by placing the local state-space models of the process in the rule consequents. The optimization problem targets the minimization of the objective function (OF) expressed as the mean squared modeling error, and the vector variable of the OF consists of the feet of the triangular input membership functions. A PSO algorithm solves the optimization problem and gives the optimal T-S fuzzy models. A set of real-time experimental results is included to validate the PSO approach and the optimal T-S fuzzy models for real-world ABS laboratory equipment.

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“…SRC tracks a desired slip reference by regulating the drive force (or torque). Various control approaches have been proposed to deal with the nonlinear and varying features of the wheel-ground contact conditions, including PID control [3], sliding mode control [6], fuzzy and neural control [7] [8].…”

Section: Introductionmentioning

confidence: 99%

A novel adhesion stability detection methodology and slip prevention control strategies for wheeled ground vehicles

Zheng

et al. 2014

2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014)

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Ensuring the motion stability and improving the mobility are crucial for wheeled ground vehicles (WGVs) to complete their missions, e.g., outdoor inspections and explorations. However, wheel slip may deteriorate the mobility performance and safety, particularly on unknown low-adhesive ground. In this research, a novel methodology to detect the adhesion stability using a force transmitting factor is proposed. The admissible maximum adhesion force (AMAF) and the optimal slip rate are determined via dynamically capturing the critical point from stable to unstable regions, based on which two controllers in prevention of wheel slip are proposed accordingly. In comparison to the existing methods, the novel detection only requires the measurements of wheel speed and wheel drive force. Additionally, it is adaptive to various uncertain wheel-ground conditions. Numerical simulation and experimental results indicate the effectiveness of the proposed methodology in adhesion stability detection, and maintaining the stability on uncertain slippery ground.

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Adaptive neuro-fuzzy wheel slip control

Cited by 27 publications

References 30 publications

Composite materials – modelling, prediction and optimization

Composite materials – modelling, prediction and optimization

Particle Swarm Optimization of fuzzy models for Anti-Lock Braking Systems

A novel adhesion stability detection methodology and slip prevention control strategies for wheeled ground vehicles

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