FPGA Based Adaptive Neuro Fuzzy Inference Controller for Full Vehicle Nonlinear Active Suspension Systems

Aldair, Ammar A.; Wang, W. J.

doi:10.5121/ijaia.2010.1401

Cited by 13 publications

(5 citation statements)

References 27 publications

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“…The rapid evaluation of silicon technologies has helped to reduce the size of FPGA integrated circuits and cost; therefore, the FPGAs can be used as final solutions to implemented many systems. The hardware implementation of the NF controller using the FPGA is explained by the authors (Aldair and Wang, 2011).…”

Section: Design Of the Neurofuzzy Controllermentioning

confidence: 99%

A neurofuzzy controller for full vehicle active suspension systems

Aldair

Wang

2011

Journal of Vibration and Control

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The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PI λ D µ (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.

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Section: Design Of the Neurofuzzy Controllermentioning

confidence: 99%

A neurofuzzy controller for full vehicle active suspension systems

Aldair

Wang

2011

Journal of Vibration and Control

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“…In order to overcome the limitations of passive suspensions in harsh road conditions, active suspensions with electro-hydraulic actuators have been used to preserve comfort and road-holding conditions, while using a low voltage control signal as input. Research works on active suspensions using this kind of actuators have presented for quarter-car [1][2][3][4], halfcar [5][6][7] and full-car models [8][9][10]. While quarter-car models can be used to demonstrate the effect of the active suspension in the improvement of comfort and road-holding, other effects like pitch and jaw movements caused by a disturbance in one of the corners of a car can only be modeled by half and full-car models.…”

Section: Introductionmentioning

confidence: 99%

An MPC-LQR-LPV Controller with Quadratic Stability Conditions for a Nonlinear Half-Car Active Suspension System with Electro-Hydraulic Actuators

et al. 2022

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The active suspension system of a vehicle manipulated using electro-hydraulic actuators is a challenging nonlinear control problem. In this research work, a novel Linear Parameter Varying (LPV) State-Space (SS) model with a fictional input is proposed to represent a nonlinear half-car active suspension system. Four different scheduling parameters are used to embed the nonlinearities of both the suspension and the electro hydraulic actuators to represent its nonlinear behavior. A recursive least squares (RLS) algorithm is used to predict the future behavior of the scheduling parameters along the prediction horizon. A Model Predictive Control-Linear Quadratic Regulator (MPC-LQR) is implemented as the control strategy and, to ensure stability, Quadratic Stability conditions are imposed as Linear Matrix Inequalities (LMI) constraints. Furthermore, the inclusion of attraction sets to overcome the conservative performance imposed by the Quadratic Stability conditions is included, as well as a terminal set were the switching between the MPC and the LQR controller is made. Simulations results for the half-car active suspension model over a typical road disturbance are tested to show the effectiveness of the proposed MPC-LQR-LPV controller with quadratic stability conditions in terms of comfort and road-holding.

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“…Cao et al (2008) studied a control method for a two degrees of freedom suspension system via the type-II fuzzy logic in association with adaptive laws, active and inactive stabilizers. 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.…”

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

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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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FPGA Based Adaptive Neuro Fuzzy Inference Controller for Full Vehicle Nonlinear Active Suspension Systems

Cited by 13 publications

References 27 publications

A neurofuzzy controller for full vehicle active suspension systems

A neurofuzzy controller for full vehicle active suspension systems

An MPC-LQR-LPV Controller with Quadratic Stability Conditions for a Nonlinear Half-Car Active Suspension System with Electro-Hydraulic Actuators

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

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