Explicit model predictive control of semi-active suspension systems with magneto-rheological dampers subject to input constraints

Ngoc, Van; Yoon, Dal‐Seong; Choi, Seung‐Bok; Kim, Gi‐Woo

doi:10.1177/1045389x20914404

Cited by 27 publications

(10 citation statements)

References 35 publications

(41 reference statements)

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“…Nguyen et al (2016) demonstrated the predictive semi-active control of vehicle suspension by introducing constraints on the damper input and the system state along with a disturbance model from the road surface. Mai et al (2020) utilized an explicit MPC for semi-active vibration control. Piecewise optimal control input trajectories are calculated in advance and stored in a lookup table.…”

Section: Model Predictive Semi-active Vibration Controlmentioning

confidence: 99%

Semi-active switching vibration control with tree-based prediction and optimization strategy

Abe

Hara

Otsuka

et al. 2022

Journal of Intelligent Material Systems and Structures

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A novel control strategy that combines model predictive control (MPC) with semi-active vibration control that uses a highly effective, energy-efficient, and stable piezoelectric transducer is proposed in this paper. Incorporating MPC into semi-active vibration control enables significant improvements in control performance and robustness. However, it is challenging to directly predict and optimize the input trajectory because the semi-active input has a state-dependent discontinuous nature. To realize effective optimal control, we need a strategy that can predict the discontinuous semi-active input trajectory in a reasonable manner and is computationally cost-efficient. The proposed method employs a prediction algorithm based on a tree data structure. The proposed algorithm achieves flexible prediction and optimization of a semi-active input trajectory with a simple tree traversal. In addition, the proposed method employs a switching criterion to minimize the computational cost and implement fast prediction and optimization. The proposed method is called predictive switching vibration control with tree-based formulation and optimization, or the PSTFO method. The simulation proved that the proposed PSTFO method can predict discontinuous semi-active input and realizes optimal vibration control performance and high robustness. In addition, the high control performance and robustness of the proposed method were experimentally validated.

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Section: Model Predictive Semi-active Vibration Controlmentioning

confidence: 99%

Semi-active switching vibration control with tree-based prediction and optimization strategy

Abe

Hara

Otsuka

et al. 2022

Journal of Intelligent Material Systems and Structures

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“…When the piston head moves, the MR fluid flows from one chamber to the other and a damping force is generated. The damping force of the MR damper can be expressed as follows (Bai et al, 2013(Bai et al, , 2019Mai et al, 2020):…”

Section: Dynamic Characteristics Of Mr Dampermentioning

confidence: 99%

Dynamic analysis of semi-active MR suspension system considering response time and damping force curve

Jeon

Kim

et al. 2021

Journal of Intelligent Material Systems and Structures

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To obtain a good ride quality and smooth car body motion, many studies have focused on the damper. Passive dampers are unable to respond actively to changes in the environment; hence many studies are being conducted on semi-active dampers that use magnetorheological (MR) fluids. As the damping characteristics of the MR damper, such as the response time, control gain, and gradient of the damping force curve, can significantly affect the control performance of the vehicle, the damping characteristics of the MR damper should be carefully considered. Accordingly, in this study, we developed a method for selecting the damping characteristics of an MR damper for specific vehicle types and analyze the relation between the dynamic characteristics of MR damper and driving performances. To achieve this objective, we demonstrated that the damping characteristics can be tuned according to the additional flow path, groove, and core material. To confirm the enhancement of the ride quality, vibration control performance, and steering stability, the skyhook controller was considered. Since we obtained the suitable damping characteristics that met the requirements under random road profiles and bumpy roads, the realization of these characteristics will enable us to satisfy the driving requirements.

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“…At present, the more common control methods to improve suspension performance include optimal control [ 6 ], neural network control [ 7 ], adaptive control [ 8 ], sliding mode control [ 9 , 10 ], fuzzy control [ 11 ], and model predictive control [ 12 , 13 , 14 ]. Ding et al [ 6 ] proposed the optimal selection strategy of anti-interference coefficients in the time-delay-dependent H-infinity/H-2 controller, and the effectiveness of the proposed method is verified by simulation.…”

Section: Introductionmentioning

confidence: 99%

Fast Distributed Model Predictive Control Method for Active Suspension Systems

Zhang

Yang

et al. 2023

Sensors

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In order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then, for engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, comfort, and handling stability of the vehicle at the same time.

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Explicit model predictive control of semi-active suspension systems with magneto-rheological dampers subject to input constraints

Cited by 27 publications

References 35 publications

Semi-active switching vibration control with tree-based prediction and optimization strategy

Semi-active switching vibration control with tree-based prediction and optimization strategy

Dynamic analysis of semi-active MR suspension system considering response time and damping force curve

Fast Distributed Model Predictive Control Method for Active Suspension Systems

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