Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm

Topalov, Andon V.; Oniz, Yesim; Kayacan, Erdal; Kaynak, Okyay

doi:10.1016/j.neucom.2010.07.035

Cited by 64 publications

(32 citation statements)

References 16 publications

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“…Hence the wheel velocity becomes lesser than the car velocity, thereby changes the slip ( ). The expression for slip in this case can be written as; (4) The dependence of wheel slip with road adhesion coefficient ( -curve) is shown in Figure 2 ( Topalov, et al, 2011). …”

Section: Laboratory Antilock Braking System (Abs) Descriptionmentioning

confidence: 99%

“…Therefore, intelligent controllers should be developed to deal with all these uncertainties. Many control strategies such as Sliding mode control (Harifi et al, 2005;Unsal, & Kachroo, 1999;Choi et al, 2002;Oniz, 2007;Oniz, et al, 2009), intelligent techniques using Fuzzy Logic (Mauer, 1995;Radac et al, 2008), Artificial Neural Networks (Layne et al, 1993;Lin & Hsu, 2003), and Neuro-fuzzy control (Topalov, et al, 2011) are reported earlier in literature. Genetic Algorithm is used in finding optimum values of fuzzy component (Yonggon & Stanislaw, 2002).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Firefly algorithm tuned fuzzy set-point weighted PID controller for antilock braking systems

Boopathi¹,

Abudhahir²

2015

J Engin Res

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Section: Laboratory Antilock Braking System (Abs) Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Firefly algorithm tuned fuzzy set-point weighted PID controller for antilock braking systems

Boopathi¹,

Abudhahir²

2015

J Engin Res

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“…These systems have evolved from their origin, using increasingly sophisticated algorithms and complex control architectures. Fuzzy logic [5,6], sliding control [7][8][9], control by artificial neural networks [10,11] and nonlinear control [12,13] are examples of the most used control methods. These systems try to optimize the longitudinal and lateral force in the tire, obtaining the maximum available force in the wheel-road contact during braking and traction processes.…”

Section: Introductionmentioning

confidence: 99%

Regenerative Intelligent Brake Control for Electric Motorcycles

et al. 2017

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Vehicle models whose propulsion system is based on electric motors are increasing in number within the automobile industry. They will soon become a reliable alternative to vehicles with conventional propulsion systems. The main advantages of this type of vehicles are the non-emission of polluting gases and noise and the effectiveness of electric motors compared to combustion engines. Some of the disadvantages that electric vehicle manufacturers still have to solve are their low autonomy due to inefficient energy storage systems, vehicle cost, which is still too high, and reducing the recharging time. Current regenerative systems in motorcycles are designed with a low fixed maximum regeneration rate in order not to cause the rear wheel to slip when braking with the regenerative brake no matter what the road condition is. These types of systems do not make use of all the available regeneration power, since more importance is placed on safety when braking. An optimized regenerative braking strategy for two-wheeled vehicles is described is this work. This system is designed to recover the maximum energy in braking processes while maintaining the vehicle's stability. In order to develop the previously described regenerative control, tyre forces, vehicle speed and road adhesion are obtained by means of an estimation algorithm. A based-on-fuzzy-logic algorithm is programmed to carry out an optimized control with this information. This system recuperates maximum braking power without compromising the rear wheel slip and safety. Simulations show that the system optimizes energy regeneration on every surface compared to a constant regeneration strategy.

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“…Though the controller is robust to uncertainties from actuators and road, it is not adaptive for various road conditions. Further, many intelligent ABS control techniques via fuzzy system and neural network approaches have been proposed [8][9][10][11][12][13][14]. In [8], a fuzzy controller provides human logical thinking capabilities to tackle unknown environmental parameters; however, the large amount of the fuzzy control rules makes the analysis complex and it is not adaptive.…”

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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Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm

Cited by 64 publications

References 16 publications

Firefly algorithm tuned fuzzy set-point weighted PID controller for antilock braking systems

Firefly algorithm tuned fuzzy set-point weighted PID controller for antilock braking systems

Regenerative Intelligent Brake Control for Electric Motorcycles

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

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