A Preview Type-2 Fuzzy Controller Design for the Semi-active Suspension to Improve Adhesion Characteristics during Braking and Handling

Gad, Ahmed S.

doi:10.4271/2021-01-5069

Cited by 3 publications

References 27 publications

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Damping Magnetorheological Systems Based on Optimal Neural Networks Preview Control Integrated with New Hybrid Fuzzy Controller to Improve Ride Comfort

Shehata Gad,

Darakhshan Jabeen,

Galal Ata

2023

SAE Int. J. Veh. Dyn., Stab., and NVH

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<div>Adaptive neural networks (ANNs) have become famous for modeling and controlling dynamic systems. However, because of their failure to precisely reflect the intricate dynamics of the system, these have limited use in practical applications and perform poorly during training and testing. This research explores novel approaches to this issue, including modifying the simple neuron unit and developing a generalized neuron (GN). The revised version of the neuron unit helps to develop the system controller, which is responsible for providing the desired control signal based on the inputs received from the dynamic responses of the vehicle suspension system. The controller is then tested and evaluated based on the performance of the magnetorheological (MR) damper for the main suspension system. These results of the tests show that the optimal preview controller designed using the GN both ∑-Π-ANN and Π-∑-ANN can accurately capture the complex dynamics of the MR damper and improve their damping characteristics compared with other methods. The seat and main suspension systems work together to provide more support and comfort for the driver and passengers. The short stroke of the MR damper is used in seat suspension as it allows for more precise control over the suspension and can provide a smoother ride. The new hybrid fuzzy type-2 (T-2) control is designed to accurately estimate the desired damping force for the seat MR damper. This system also allows for the damping force to be adjusted to meet the desired requirements of the seat MR damper. This integration of damping systems allows better control and stability of the vehicle and provides a smoother ride for drivers and passengers. Furthermore, integrating the damping systems increases the overall performance of the vehicle, making it better able to handle various road conditions.</div>

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Damping Magnetorheological Systems Based on Optimal Neural Networks Preview Control Integrated with New Hybrid Fuzzy Controller to Improve Ride Comfort

Shehata Gad,

Darakhshan Jabeen,

Galal Ata

2023

SAE Int. J. Veh. Dyn., Stab., and NVH

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Interval Type-2 Fuzzy Control System Integrated with Neural Network-Predictive Control for Air Suspension System to Improve Both Ride and Braking Characteristics

Gad¹

2022

SAE Technical Paper Series

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Interval Lower Singleton Fuzzy Optimal Controller Design of Magnetorheological Seat Suspension Integrated with Semi-Active Vehicle Suspension System

Shehata Gad

2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">In this paper, semi-active MR main suspension system based on system controller design to minimize pitch motion linked with MR-controlled seat suspension by considering driver’s biodynamics is investigated. According to a fixed footprint tire model, the transmitted tire force is determined. The linear-quadratic Gaussian (LQG) system controller is able to enhance ride comfort by adjusting damping forces based on an evaluation of body vibration from the dynamic responses. The controlled damping forces are tracked by the signum function controllers to evaluate the supply voltages for the front and rear MR dampers. Based on the sprung mass acceleration level and its derivative as the inputs, the optimal type-2 (T-2) fuzzy seat system controller is designed to regulate the controlled seat MR damper force. The best rate for each linguistic variable is acquired by modifying the range between upper and lower membership functions (MFs), which enables accurate tracking of the seat-damping force. The parameters of the LQG main system controller and the ideal scaling lower ranges of the T-2 fuzzy seat system controller are both explored by a genetic algorithm (GA). The performance of LQG regulated for MR dampers is compared with that of linear-quadratic regulator (LQR) controlled for MR dampers and passive systems to measure the suspension efficacy under bump and random road disturbance. To verify the efficiency of the recommended integrated models on both the main and seat systems, the performance of the proposed ideal T-2 fuzzy-controlled MR semi-active seat suspension is compared with the passive seat suspension. The simulation results show that the LQG controlled connected with the T-2 fuzzy controlled can greatly improve both ride comfort and vehicle stability, among all examined systems.</div></div>

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A Preview Type-2 Fuzzy Controller Design for the Semi-active Suspension to Improve Adhesion Characteristics during Braking and Handling

Cited by 3 publications

References 27 publications

Damping Magnetorheological Systems Based on Optimal Neural Networks Preview Control Integrated with New Hybrid Fuzzy Controller to Improve Ride Comfort

Damping Magnetorheological Systems Based on Optimal Neural Networks Preview Control Integrated with New Hybrid Fuzzy Controller to Improve Ride Comfort

Interval Type-2 Fuzzy Control System Integrated with Neural Network-Predictive Control for Air Suspension System to Improve Both Ride and Braking Characteristics

Interval Lower Singleton Fuzzy Optimal Controller Design of Magnetorheological Seat Suspension Integrated with Semi-Active Vehicle Suspension System

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