Fuzzy logic in control: truck backer-upper problem revisited

Riid, Andri; Rustern, E.

doi:10.1109/fuzz.2001.1007361

Cited by 32 publications

(23 citation statements)

References 5 publications

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“…Backward movement control of computer simulated truck-trailers using various types of intelligent control e.g. fuzzy control, neural control and neurofuzzy or genetic algorithm-based control (Yang et al 2006, Kiyuma et al 2004, Riid andRustern 2001), has been reported. The backing-up control of a trucktrailer is considered in (Park et al 2007) as a system with time delay.…”

Section: Introductionmentioning

confidence: 99%

Backing-up Fuzzy Control of a Truck-trailer Equipped with a Kingpin Sliding Mechanism

Siamantas

Manesis

2009

IFIP Advances in Information and Communication Technology

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For articulated vehicles met in robotics and transportation fields, even for an experienced operator, backing-up leads usually to jack-knifing. This paper presents a fuzzy logic controller for back-driving a truck-trailer vehicle into a predefined parking task. The truck-trailer link system is equipped with a kingpin sliding mechanism acting as an anti-jackknife excitation input. By applying fuzzy logic control techniques, precise system model is not required. The developed controller with thirty four rules works well as the presented simulation results demonstrate the avoidance of jack-knife and the accuracy of the backing-up technique.

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Section: Introductionmentioning

confidence: 99%

Backing-up Fuzzy Control of a Truck-trailer Equipped with a Kingpin Sliding Mechanism

Siamantas

Manesis

2009

IFIP Advances in Information and Communication Technology

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“…That is, a combined fuzzy rule base is generated by joining the previously generated rules (data-driven) and linguistic rules (expert knowledge-driven). More recently, a supervisory control system was proposed with fewer number of state variables required due to its capability to the decomposition of the control task, thus relieving the curse of dimensionality [86].…”

Section: Truck Backer-upper Controlmentioning

confidence: 99%

Fuzzy Interpolation Systems and Applications

Yang¹,

Zuo²,

Chao³

et al. 2017

Modern Fuzzy Control Systems and Its Applications

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Fuzzy inference systems provide a simple yet effective solution to complex non-linear problems, which have been applied to numerous real-world applications with great success. However, conventional fuzzy inference systems may suffer from either too sparse, too complex or imbalanced rule bases, given that the data may be unevenly distributed in the problem space regardless of its volume. Fuzzy interpolation addresses this. It enables fuzzy inferences with sparse rule bases when the sparse rule base does not cover a given input, and it simplifies very dense rule bases by approximating certain rules with their neighbouring ones. This chapter systematically reviews different types of fuzzy interpolation approaches and their variations, in terms of both the interpolation mechanism (inference engine) and sparse rule base generation. Representative applications of fuzzy interpolation in the field of control are also revisited in this chapter, which not only validate fuzzy interpolation approaches but also demonstrate its efficacy and potential for wider applications.

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“…The other advantages of Fuzzy controllers are that their design is simple, fast, inexpensive, and easily maintained because the rules can be linguistically interpreted by the human experts. Riid & Rustern (Riid & Rustern, 2001) presented a fuzzy supervisory control system over the PID controller to reduce the complexity of the control problem and enhance the control performance. Riid & Rustern in (Riid & Rustern, 2002) demonstrate that problem decomposition leads to more effective knowledge acquisition and improved control performance in fuzzy control.…”

Section: Introductionmentioning

confidence: 99%