Direct adaptive neural control of antilock braking systems incorporated with passive suspension dynamics

Pedro, Jimoh O.; Dahunsi, Olurotimi Akintunde; Nyandoro, Otis T.

doi:10.1007/s12206-012-0878-5

Cited by 13 publications

(15 citation statements)

References 35 publications

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“…In addition to this, the SMC outperforms the NN-based design in terms of braking distance. Several additional studies on NN applied to ABS can be found, for example, in [55] and [56].…”

Section: Neural Network Controllers (Nnc)mentioning

confidence: 99%

Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems

et al. 2020

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Since their introduction, anti-lock braking systems (ABS) have mostly relied on heuristic, rule-based control strategies. ABS performance, however, can be significantly improved thanks to many recent technological developments. This work presents an extensive review of the state of the art to verify such a statement and quantify the benefits of a new generation of wheel slip control (WSC) systems. Motivated by the state of the art, as a case study, a nonlinear model predictive control (NMPC) design based on a new load-sensing technology was developed. The proposed ABS was tested on Toyota's high-end vehicle simulator and was benchmarked against currently applied industrial controller. Additionally, a comprehensive set of manoeuvres were deployed to assess the performance and robustness of the proposed NMPC design. The analysis showed substantial reduction of the braking distance and better steerability with the proposed approach. Furthermore, the proposed design showed comparable robustness against external factors to the industrial benchmark. INDEX TERMS Road vehicles, vehicle safety, antilock braking system, wheel slip control, model predictive control.

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“…In addition to this, the SMC outperforms the NN-based design in terms of braking distance. Several additional studies on NN applied to ABS can be found, for example, in [55] and [56].…”

Section: Neural Network Controllers (Nnc)mentioning

confidence: 99%

Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems

et al. 2020

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“…The various plots that are slip ratio vs. time, stopping distance vs. time and vehicle & wheel velocity vs. time are shown in Figs. [8][9][10]. The stopping distance obtained by implementing Proportional control is 46.9494 meters, and the stopping time is 3.1229 seconds.…”

Section: Control Systemmentioning

confidence: 99%

“…For the configuration of ABS controllers, various methodologies have been proposed. A prescient methodology to design a nondirect model-based controller for the wheel slips is put forward by [4][5][6][7][8][9]. A static-state feedback control calculation for Anti-lock braking system is proposed by [10].…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of Controller for Antilock Braking System in Matlab/Simulation

Rohilla¹,

Jitender²,

Amit³

et al. 2016

IJERT

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Antilock braking mechanism enhances the vehicle steadiness and steering ability to stop a vehicle wheel without locking and minimizing stopping distance. A scientific model of electronically monitored slowing mechanism (ABS) of quarter auto model has been produced. The different sorts of controllers such as P, PI, PD and PID have been executed and results are analyzed in Matlab/Simulink environment. The controller's parameters are streamlined to control wheel slip and stopping distance. A Genetic algorithmic optimization technique has been used to obtain gain parameters of controllers. The simulated results of an ABS system are compared with and without controller and further between the distinctive sorts of controllers. The output response of PID controller is better as compared to different controllers. Keywords-Antilock Braking System (ABS), Proportional controller (P), Proportional Integrative (PI), Proportional Derivative (PD), PID, modeling quarter car model, genetic algorithm. Slip ratio, steering ability.I.

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“…where K is a matrix of controller gains and R is the reference input. Substituting equation (34) into (33) cancels out the nonlinear dynamics of the system,  …”

Section: Nnfbl Suspension Travel Control Loop Designmentioning

confidence: 99%

NARMA-L2 Control of a Nonlinear Half-Car Servo-Hydraulic Vehicle Suspension System

2013

APH

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In this paper, the performance of a nonlinear, 4 degrees-of-freedom, servo-hydraulic half-car active vehicle suspension system is compared with that of a passive vehicle suspension system with similar model parameters. The active vehicle suspension system is controlled by an indirect adaptive Neural Network-based Feedback Linearization controller (NARMA-L2). Hydraulic actuator force tracking is guaranteed by an inner Proportional+Integral+Derivative-based force feedback control loop. The output responses of the vehicles are presented and analyzed in the frequency and time domains, in the presence of model uncertainties in the form of variation in vehicle sprung mass loading. The results show that the NARMA-L2-based active vehicle suspension system performed better than the passive vehicle suspension system within the constraints.

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Direct adaptive neural control of antilock braking systems incorporated with passive suspension dynamics

Cited by 13 publications

References 35 publications

Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems

Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems

Design and Analysis of Controller for Antilock Braking System in Matlab/Simulation

NARMA-L2 Control of a Nonlinear Half-Car Servo-Hydraulic Vehicle Suspension System

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