Model Predictive Control for Speed-Dependent Active Suspension System with Road Preview Information

Li, Qiangqiang; Chen, Zhiyong; Song, Haisheng; Dong, Yahui

doi:10.3390/s24072255

Sensors

2024

DOI: 10.3390/s24072255

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Model Predictive Control for Speed-Dependent Active Suspension System with Road Preview Information

Qiangqiang Li,

Zhiyong Chen,

Haisheng Song

et al.

Abstract: This paper proposes a model predictive control (MPC) scheme based on linear parameter variation to enhance the damping control of speed-dependent active suspensions. The controller is developed by introducing a speed-dependent term, specifically front- and rear-wheel time delays, to the half-car model using the Padé approximation. Subsequently, the model is augmented with time-varying parameter dependence. An adaptive Kalman filter based on variance matching is employed to estimate system states affected by im… Show more

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2024

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Cited by 3 publications

References 40 publications

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A simulation study on visual preview control of vehicle magnetorheological suspension

Xie,

Wang,

Zhou

et al. 2024

Smart Mater. Struct.

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At present, the boom in intelligent vehicles, especially automotives, has brought magnetorheological (MR hereinafter) suspensions into further focus. Generally, traditional MR suspensions work with acceleration sensors to respond with a non-negligible time delay issue due to the inductive property of MR damper (MRD hereinafter), which hinders their full performance especially at high-speed driving with a powerless “blind zone”. To address this issue, this study proposes a simulation study on visual preview control for vehicle MR suspension based on MPC (Model Predictive Control), since the road preview is able to compensate the time delay due to the future road information perceived in advance. First, a camera with a computer vision algorithm is employed to preview road information of target type, target size and distance to wheels. Besides, the error correction of radial and tangential distortions of camera lens are investigated, and the trajectory of wheels are predicted by a cycloid model because MR suspension only needs to respond to the road targets that the wheels will pass over. Further, the area in front of the vehicle is divided into a recognition zone and action zone, and the road targets within action zone (<1.5 m) are calculated more precisely for a prepared immediate response from the MR suspension. Then, the performance of a simulating vehicle MR suspension is investigated through a joint simulation by the automotive simulation software CarSim and the model-solving Simulink. The evident results show that the MR suspension with visual preview control performs the best in ride comfort (mean square values of sprung mass acceleration is 82% and 52% lower compared to a passive suspension and a MR suspension with skyhook control, respectively). Therefore, the visual preview control strategy of MR suspension is able to enhance the performance on the discrete road conditions.

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A simulation study on visual preview control of vehicle magnetorheological suspension

Xie,

Wang,

Zhou

et al. 2024

Smart Mater. Struct.

View full text Add to dashboard Cite

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H∞ Differential Game of Nonlinear Half-Car Active Suspension via Off-Policy Reinforcement Learning

Wang,

Deng,

Zhou

et al. 2024

Mathematics

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This paper investigates a parameter-free H∞ differential game approach for nonlinear active vehicle suspensions. The study accounts for the geometric nonlinearity of the half-car active suspension and the cubic nonlinearity of the damping elements. The nonlinear H∞ control problem is reformulated as a zero-sum game between two players, leading to the establishment of the Hamilton–Jacobi–Isaacs (HJI) equation with a Nash equilibrium solution. To minimize reliance on model parameters during the solution process, an actor–critic framework employing neural networks is utilized to approximate the control policy and value function. An off-policy reinforcement learning method is implemented to iteratively solve the HJI equation. In this approach, the disturbance policy is derived directly from the value function, requiring only a limited amount of driving data to approximate the HJI equation’s solution. The primary innovation of this method lies in its capacity to effectively address system nonlinearities without the need for model parameters, making it particularly advantageous for practical engineering applications. Numerical simulations confirm the method’s effectiveness and applicable range. The off-policy reinforcement learning approach ensures the safety of the design process. For low-frequency road disturbances, the designed H∞ control policy enhances both ride comfort and stability.

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Reinforcement Learning-Based Vibration Control for Half-Car Active Suspension Considering Unknown Dynamics and Preset Convergence Rate

Wang,

Deng,

Zhou

et al. 2024

Processes

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Electromagnetic actuators, characterized by their lack of pneumatic or hydraulic circuits, rapid response, and ease of control, have the potential to significantly enhance the dynamic performance of automotive active suspensions. However, the complexity associated with their models and the calibration of control parameters hampers the efficiency of control design. To address this issue, this paper proposes a reinforcement learning vibration control strategy for electromagnetic active suspension. Firstly, a half-car dynamic model with electromagnetic active suspension is established. Considering the unknown dynamics of the actuator and its preset convergence performance, an optimal control method based on reinforcement learning is investigated. Secondly, a heuristic PI adaptive dynamic programming algorithm is presented. This method can update to the optimal control solution without requiring model parameters or initial design parameters. Finally, the energy consumption and dynamic performance of this method are analyzed through rapid prototyping control simulation. The results show that the ride comfort of the vehicle suspension can be improved with the given preset convergence rate.

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Model Predictive Control for Speed-Dependent Active Suspension System with Road Preview Information

Cited by 3 publications

References 40 publications

A simulation study on visual preview control of vehicle magnetorheological suspension

A simulation study on visual preview control of vehicle magnetorheological suspension

H∞ Differential Game of Nonlinear Half-Car Active Suspension via Off-Policy Reinforcement Learning

Reinforcement Learning-Based Vibration Control for Half-Car Active Suspension Considering Unknown Dynamics and Preset Convergence Rate

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