Fuzzy-hybrid modelling of an Ackerman steered electric vehicle

Economou, J.T.; Colyer, R.E.

doi:10.1016/j.ijar.2005.08.002

Cited by 13 publications

(11 citation statements)

References 17 publications

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“…Takagi-Sugeno fuzzy model [137] Fuzzy linear regression [9] Fuzzy preview control [138] Model reference adaptive fuzzy control [44] Neuro-fuzzy method [123] Hierarchical fuzzy integral [24] Fuzzy clustering [139] Type-II fuzzy system [140] Sliding mode control [141] Grey predictor [142] PI and PID control [91,143] Genetic algorithm [71,144] Optimization gradient method [145] Multi-objective optimization [108] Variable structure control [146] Reinforcement learning [94] Bayesian methods [147] in automotive engineering applications. The following positions can be especially mentioned in this context:…”

Section: Conventional Fuzzy Methods Methods / Tools Integrated With Fmentioning

confidence: 99%

A review of fuzzy methods in automotive engineering applications

Ivanov

2015

Eur. Transp. Res. Rev.

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Purpose The paper addresses evolution of fuzzy systems for core applications of automotive engineering. Methods The presented study is based on the analysis of bibliography dedicated to fuzzy sets and fuzzy control for ground vehicles. A special attention is given to fuzzy approaches used in the following domains of automotive engineering: vehicle dynamics control systems, driver and driving environment identification, ride comfort control, and energy management of electric vehicles. Results The bibliographical analysis, supplemented with statistics of relevant research publications, has allowed to allocate the most important fuzzy application cases for each domain. In particular, it concerns driver identification, humanmachine interface, recognition of road conditions, and controllers of vehicle chassis and powertrain systems. It is found out that fuzzy methods have the primary use most of all for tasks requiring identification and forecasting procedures, especially in conditions of limited informational space. Additional observation that can be also derived from the presented survey points to reasonable integration of fuzzy technique with other control engineering methods to improve the performance of automotive control systems. Conclusions In the aggregate the performed review indicates that fuzzy computing can be considered as a versatile tool for automotive engineering applications of different nature.

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Section: Conventional Fuzzy Methods Methods / Tools Integrated With Fmentioning

confidence: 99%

A review of fuzzy methods in automotive engineering applications

Ivanov

2015

Eur. Transp. Res. Rev.

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“…In a review of the literature (Sharp et al 1995) it was noted that the use of terrain preview , (Kiyotsugu 2003) together with an active suspension system would need to be used on "special test surfaces" for example; very rough (off-road) to be of any benefit. In this work a three axle military vehicle, Figure 1, is used to investigate the possible benefits of a Sugeno (Feng 2006), (Tagaki et al 1985) based Fuzzy controller (using subtractive clustering) (Economou et al 2006) to improve its performance at isolating the crew from the terrain. This work extends that done by (Mehrishi 2007), (Berrouila 2008), (Bo 2010), and (Purdy et al 1997).…”

Section: Introductionmentioning

confidence: 99%

Intelligent based terrain preview controller for a 3-axle vehicle

Economou¹,

Purdy²,

Wall³

et al. 2016

Advanced Vehicle Control AVEC’16

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The paper presents a six-wheel half longitudinal model and the design of a dual level control architecture. The first (top) level is designed using a Sugeno fuzzy inference feedforward architecture with and without preview. The second level of controllers are locally managing each wheel for each axle. As the vehicle is moving forward the front wheels and suspension units will have less time to respond when compared to the middle and rear units, hence a preview sensor is used to compensate. The paper shows that the local active suspensions together with the Sugeno Fuzzy, (locally optimised using subtractive clustering), Feedforward control strategy is more effective and this architecture has resulted in reducing the sprung mass vertical acceleration and pitch accelerations.

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“…In particular in this chapter, the electrical thruster is modelled as an ordinary differential equation which can operate in either motoring or generator mode depending on the operational shaft angular velocity and the motor torque. The theoretical parts present the Sugeno fuzzy inference in association to the fuzzy-hybrid concept developed by Economou & Colyer (2005). The latter is demonstrated from the simulated behaviour of the PMDC thruster.…”

mentioning

confidence: 99%

“…The latter is demonstrated from the simulated behaviour of the PMDC thruster. Part of the electrical thruster, based on Economou & Colyer (2005) can be presented in an ordinary differential equation representation while when the UAV altitude is included then the model exerts partially a Sugeno type fuzzy behaviour. The collection of these behaviours is shown in this chapter.…”

mentioning

confidence: 99%

“…In effect part of the thruster is modelled utilising physical system modelling methods while the remaining part of the system is modelled using an intelligent based method (fuzzy logic). Overall the thruster is a fuzzy-hybrid system as per Economou & Colyer (2005). In particular, the fuzzy inference system utilised in this chapter is a Sugeno system Sugeno (1999).…”

mentioning

confidence: 99%

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