Adaptive fuzzy controller for vehicle active suspension system based on traffic conditions

Soleymani, Mehdi; Montazeri-Gh, Morteza; Amiryan, Reza

doi:10.1016/j.scient.2012.03.002

Cited by 27 publications

(13 citation statements)

References 13 publications

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“…A predictive model will compute the future changes of the dependent variables using the current states of the dynamical process (Soleymani et al, 2012). In the problem of the suspension system with changes in the mass of the vehicle’s body, the predictive model estimates and predicts the optimal constant gains ( f c i 1 i 2 j and g c i 1 i 2 j ) of the adaptive fuzzy controller at any instance to reach the best performance.…”

Section: Predictive Modelsmentioning

confidence: 99%

“…Aldair and Wang (2010) considered a strategic set for constructing an adaptive fuzzy inference system using neural networks for an eight degrees of freedom vehicle with the active-builder hydraulics. Soleymani et al (2012) implemented an adaptive fuzzy controller into an active eight degrees of freedom model of a suspension system. Further, two discrete adaptive fuzzy controllers were used for the front and rear tires.…”

Section: Introductionmentioning

confidence: 99%

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Combination of predictive models with an optimal adaptive fuzzy controller for active suspension systems having control force constraints on front and rear tires

Mahmoodabadi

Javanbakht

2020

Transactions of the Institute of Measurement and Control

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This paper presents optimal adaptive fuzzy approaches combined by the predictive models for five degrees of freedom vehicle systems having a control force constraint of 1000 N on both front and rear suspensions in order to minimize the road disturbances. First, two separate adaptive fuzzy controllers are designed for the rear and front tires using the singleton fuzzifier, center average defuzzifier and product inference engine. The constructed fuzzy systems implement the adaptation laws based on the Lyapunov theory to guarantee the stability of the system. Afterward, a gravitational search optimization algorithm is applied to calculate the optimal values of the controller’s gains. The weighted summation of four objectives, as the relative displacement between the sprung mass and the front tire, the relative displacement between the sprung mass and the rear tire, the acceleration of the body and the acceleration of the seats, are regarded in the optimization process. Two different predictive models are employed to find the optimal design variables for the circumstances where the stability of the system is under variation. The first model is a fuzzy predictive system while the second one is based on the moving least squares interpolation. Eventually, the resultant online models are compared with the offline systems when the vehicle mass varies. These simulations obviously illustrate the efficiency and ability of the suggested strategy to remove the effect of the road disturbances on the ride comfort.

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Section: Predictive Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combination of predictive models with an optimal adaptive fuzzy controller for active suspension systems having control force constraints on front and rear tires

Mahmoodabadi

Javanbakht

2020

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…A number of in-depth studies have proposed control methods for active suspension control to meet the ride comfort and handling stability requirements [1][2][3][4][5][6][7][8]. For example, [4] considered the influence of different vehicle speeds and road conditions and designed two separate fuzzy controllers for front and rear suspensions. Reference [5] developed a saturated adaptive robust control system for active suspension in response to uncertainties and the possible actuator saturation in systems; [6] provided an enhanced active suspension, established a fuzzy model, and used the fuzzy adaptive sliding mode control to verify the effectiveness of the model.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Predictive Sliding Mode Control for Active Suspension System

Wang

Zhao

Gong

et al. 2018

Shock and Vibration

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An active suspension system is important in meeting the requirements of the ride comfort and handling stability for vehicles. In this work, a nonlinear model of active suspension system and a corresponding nonlinear robust predictive sliding mode control are established for the control problem of active suspension. Firstly, a seven-degree-of-freedom active suspension model is established considering the nonlinear effects of springs and dampers; and secondly, the dynamic model is expanded in the time domain, and the corresponding predictive sliding mode control is established. The uncertainties in the controller are approximated by the fuzzy logic system, and the adaptive controller reduces the approximation error to increase the robustness of the control system. Finally, the simulation results show that the ride comfort and handling stability performance of the active suspension system is better than that of the passive suspension system and the Skyhook active suspension. Thus, the system can obviously improve the shock absorption performance of vehicles.

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“…Fuzzy logic control has been widely used in different industries [5][6][7]. In recent decades, some researchers introduced fuzzy logic control in developing EMS for HEV [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Energy Management Optimization for a Parallel Hybrid Electric Vehicle using Chaotic Non-dominated sorting Genetic Algorithm

Liang

Zhang

et al. 2015

Automatika

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Original scientific paperThis paper presented a parallel hybrid electric vehicle (HEV) equipped with a hybrid energy storage system. To handle complex energy flow in the powertrain system of this HEV, a fuzzy-based energy management strategy was established. A chaotic multi-objective genetic algorithm, which optimizes the parameters of fuzzy membership functions, was also proposed to improve fuel economy and HC, CO, and NOx emissions. The main target of this algorithm is to escape from local optima and obtain high quality trade-off solutions. Chaotic initialization operator, chaotic crossover and mutation operators, chaotic disturbance operator, and chaotic local search operator were integrated into non-dominated sorting genetic algorithm II (NSGA-II) to form this new algorithm named chaotic NSGA-II (C-NSGA-II). Simulation results and comparisons demonstrated that chaotic operators can enhance searching ability for optimal solutions. In conclusion, C-NSGA-II is suitable for solving HEV energy management optimization problem.Key words: Chaotic operator, Fuzzy logic, Hybrid electric vehicle, Multi-objective optimization, NSGA-II Neizrazita strategija optimizacije potrošnje energije za paralelno hibridno električno vozilo korištenjem kaotičnog nedominirajućeg genetskog algoritma sortiranja. Ovaj rad prikazuje paralelno hibridno električno vozilo (HEV) opremljeno hibridnim spremnikom energije. Kako bi se omogućila funkcionalnost pogonskog sklopa ovakvog HEV-a korištena je strategija raspolaganja energijom zasnovana na neizrazitoj logici. Takoer, prikazan je više kriterijski genetski algoritam kaosa za optimiranje parametara neizrazite funkcije povezanih s ekonomskim pokazateljem te pokazateljima emisije HC-a, CO-a i NOx-a. Osnovni cilj algoritma je omogućiti izlazak iz lokalnih optimuma i uspostavljanjem kompromisa omogućiti dosezanje boljih rješenja. Kaotični inicijalizacijski operator, kaotično križanje i operator mutacije, kaotični operator poremećaja i kaotični operator lokalnog pretraživanje uključeni su u nedominirajući genetski algoritam sortiranja II (NSGA-II) u svrhu formulacije novog problema nazvanog kaotični NSGA-II (C-NSGA-II). Simulacijski rezultati i usporedbe prikazuju kako kaotični operator može povećati uspješnost traženja optimalnog rješenja. Zaključno, C-NSGA-II je primjeren za rješavanje problema raspolaganja energijom u HEV-u.Ključne riječi: operator kaosa, neizrazita logika, hibridna električna vozila, više kriterijska optimizacija, NSGA-II

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Adaptive fuzzy controller for vehicle active suspension system based on traffic conditions

Cited by 27 publications

References 13 publications

Combination of predictive models with an optimal adaptive fuzzy controller for active suspension systems having control force constraints on front and rear tires

Combination of predictive models with an optimal adaptive fuzzy controller for active suspension systems having control force constraints on front and rear tires

Nonlinear Predictive Sliding Mode Control for Active Suspension System

Fuzzy Energy Management Optimization for a Parallel Hybrid Electric Vehicle using Chaotic Non-dominated sorting Genetic Algorithm

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