Cloud-based adaptive semi-active suspension control for improving driving comfort and road holding

Başargan, Hakan; Mihály, András; Gáspár, Péter; Sename, Olivier

doi:10.1016/j.ifacol.2022.07.588

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…When optimized acceleration is still high, we could recommend additionally a lower speed value. Thus, combining information about speed, masses and their locations from car sensors, optimal damping, RMS of vertical acceleration stored in a microcomputer or the cloud, and road information, we could control damping and recommend or reduce the driving speed [40,74].…”

Section: Resultsmentioning

confidence: 99%

“…The second is more comprehensive usage of preview information from camera, Global Positioning System (GPS), and electronic horizon such as vehicle-to-vehicle communication, vehicle-to-infrastructure communication, vehicle localization and real-time accessing of cloud information, and crowd sourcing leading to up-to-date road profile maps". So, there are still many areas for investigation [23,[39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Damping in a Semi-Active Car Suspension System with Various Locations of Masses

et al. 2023

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The key request for a vehicle suspension system is vibration control and decreasing the actual inertia forces. This ensures ride comfort for the crew and influences the fatigue level of the driver and overall driving safety. Implementing semi-active damping control in the vehicle suspension allows for adjusting the damping process in the vehicle for minimum acceleration applied to the seats, driver, and passengers. In order to implement theoretical analysis, we used a mathematical full-car model in Simulink/MATLAB. As the load, we added simulations of various artificially generated road profiles. The damping coefficient of the semi-active suspension system was optimized for maximum comfort level for a driver only. Results from the full-car simulation process deliver a graph of the output accelerations showing kinematic excitation from road deformities under various locations of vehicle load positions.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Damping in a Semi-Active Car Suspension System with Various Locations of Masses

et al. 2023

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“…In the future, a new upper logic control algorithm for vehicle ride and handling control [28] can be developed by considering the active suspension actuator system performance with data-driven and model-free approaches [29], [30].…”

Section: Discussionmentioning

confidence: 99%

“…The experimental results of the hydraulic dynamics varied depending on the damper dimensions and the operating conditions. The electrical characteristics are dependent on the solenoid resistance and inductance characteristics and are set to a 1 ms time response [27], [28]. Fig.…”

Section: A Inverse Force Map Based Controlmentioning

confidence: 99%

New Decentralized Actuator System Design and Control for Cost-Effective Active Suspension

Lee¹,

2022

IEEE Access

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A new active suspension control system that replaces the existing complex hydraulic systems was proposed in this paper by reviewing recent research and development trends. We studied the actuator system characteristics, force, and damping control by conducting studies on actuator dynamics, which are yet to be studied in the literature. Moreover, many damping and active force mechanism concept studies have been conducted based on several proposed hydraulic circuits. Based on the conducted studies, a new decentralized actuator system was designed for compact and efficient vehicle control. For the piecewise control, a model-based actuator force control algorithm was proposed with consideration of the individual main component non-linearity for force application scalability. Based on a semi-active system applied to the existing commercialization, the on-demand electric pump at each wheel is integrated into the system circuit to propose a realistic, cost-effective solution. Additionally, from the vehicle control point of view, an integrated control algorithm for active suspension was developed using a model-based control method and conventional map-based inverse control methods, considering nonlinear actuator characteristics and road input disturbance. Finally, the performance of the proposed control system was evaluated using a simulation technique and an actual test platform.

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“…Ref. [4] developed a cloud-based parameter-adjustable adaptive semi-active suspension control method, while AMESim software was used by [5] to model the Continuous Damping Control damper adjustable damping shock absorber, analyze the dynamics of a 2-DOF semi-active suspension, and propose a genetic algorithm to improve the fuzzy rule. The control effect of the semi-active suspension controller depends largely on the parameters in the controller; for example, the control effect of the LQR controller depends on the weighting coefficient of various performance indicators, the performance of the PID controller mainly depends on K p , K i and K d parameters, and the control effect of the fuzzy controller depends on the fuzzy rules formulated, so the key to the design of the controller is the tuning of the controller parameters.…”

Section: Introductionmentioning

confidence: 99%

Tuning Parameters of the Fractional Order PID-LQR Controller for Semi-Active Suspension

Gao,

2023

Electronics

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In order to further improve the control effect of proportion integral differential (PID) control and linear quadratic regulator (LQC) control, and improve vehicle ride comfort and enhance body stability, the 7 DOF semi-active suspension model was established, and the fractional order PIλDμ-LQR controller was designed by combining fractional order PIλDμ control theory and LQR control theory. The semi-active suspension model in this paper is more complex, and there are many parameters in the controller. The optimal weighting coefficient of 12 vehicle smoothness evaluation indicators and parameters Kp, Ki, Kd, λ and μ in the controller were founded by NSGA-II algorithm. After optimization, the optimized parameters were brought into the controller for random pavement simulation. Compared to the traditional passive suspension, fractional order PIλDμ individual control and LQR separate control, the simulation results show that the effect of fractional order PIλDμ-LQR control is very significant. The evaluation index of vehicle smoothness has been significantly improved, and the use of fractional order PIλDμ-LQR control has significantly improved the working performance of the suspension and improved the smoothness of the vehicle. At the same time, the adjusting force output of the actuator is very balanced, which inhibits the roll of the body and improves the anti-roll performance. After simulation, the excellent performance of the designed fractional PIλDμ-LQR controller was verified, and the introduced NSGA-II algorithm played an important role in the controller parameter tuning work, which shows that the fractional order PIλDμ-LQR controller and NSGA-II algorithm cooperate with each other to achieve good control effects.

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Cloud-based adaptive semi-active suspension control for improving driving comfort and road holding

Cited by 6 publications

References 12 publications

Optimization of Damping in a Semi-Active Car Suspension System with Various Locations of Masses

Optimization of Damping in a Semi-Active Car Suspension System with Various Locations of Masses

New Decentralized Actuator System Design and Control for Cost-Effective Active Suspension

Tuning Parameters of the Fractional Order PID-LQR Controller for Semi-Active Suspension

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