Pressure control for pneumatic electric braking system of commercial vehicle based on model predictive control

Zhao, Yongtao; Yang, Yiyong

doi:10.1049/itr2.12118

Cited by 6 publications

(5 citation statements)

References 26 publications

(27 reference statements)

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“…In order to construct the state expression in the risk point of the braking system and judge the working state of the risk point, this paper consults the relevant literature on the risk point mentioned in this paper. some characteristics and indexes of related components are introduced in the braking system [26][27][28][29][30][31][32][33][34]. Based on these indicators, this paper constructs the risk point state expression.…”

Section: State Expression Of Key Risk Point State Of the Braking Systemmentioning

confidence: 99%

Failure Propagation Prediction of Complex Electromechanical Systems Based on Interdependence

et al. 2023

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Interdependence is an inherent feature of the cyber-physical system. Small damage to one component in the system may affect several other components, leading to a series of failures, thus collapsing the entire system. Therefore, the system failure is often caused by the failure of one or more components. In order to solve this problem, this paper focuses on a failure propagation probability prediction method for complex electromechanical systems, considering component states and dependencies between components. Firstly, the key component set in the system is determined based on the reliability measure. Considering the three coupling mechanisms of mechanical, electrical, and information, a topology network model of the system is constructed. Secondly, based on the topology network model and fault data, the calculation method of influence degree between components is proposed. Three state parameters are used to express the risk point state of each component in the system through mathematical representation, and the correlation coefficient between the risk point state parameters is calculated and measured based on the uncertainty evaluation. Then, the influence matrix between the system risk points is constructed, and the fault sequence is predicted by using the prediction function of an Artificial Neural Network (ANN) to obtain the fault propagation probability. Finally, the method is applied to the rail train braking system, which verifies that the proposed method is feasible and effective.

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Section: State Expression Of Key Risk Point State Of the Braking Systemmentioning

confidence: 99%

Failure Propagation Prediction of Complex Electromechanical Systems Based on Interdependence

et al. 2023

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“…Zhao and Braun et al [22,23] realized the observation of solenoid valve spool position and WCP control based on the sliding mode observer and controller. Zhao et al [24] established a pressure controller for commercial vehicles based on the MPC theory and carried out a simulation and experimental verification.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Control Scheme for Adaptive Cruise Control System Based on Model Predictive Control

Zhang

Mei

et al. 2023

Actuators

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An adaptive cruise control (ACC) system can improve safety and comfort during driving by taking over longitudinal control of the vehicle. It requires the coordination between the upper-layer controller and the lower-layer actuators. In this paper, a hierarchical anti-disturbance cruise control architecture based on electronic stability control (ESC) system is proposed. The upper-layer controller outputs the desired longitudinal acceleration or deceleration to the lower-layer actuators. In order to improve the accuracy of model prediction and achieve the coordinated control of multiple objectives, an upper-layer model prediction cruise controller is established based on feedback control and disturbance compensation. In addition, based on the hydraulic control unit (HCU) model and the vehicle longitudinal dynamics model, a lower-layer nonlinear model predictive deceleration controller is proposed in order to solve the problems of pressure fluctuations and the low accuracy of small decelerations when ESC is used as the actuator for the ACC system. Finally, the simulation and experimental tests were carried out. The results show that the proposed control architecture can improve the stability and comfort of the cruise control process. Moreover, compared with the traditional PID deceleration controller, it effectively improves the deceleration control accuracy.

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“…Due to parametric uncertainties, unknown non-linearities and dynamic external disturbances, it is challenging for the EHPS system to realize a precise path tracking control, so an integral sliding mode control is proposed to deal with this complex non-linear tracking control problem, and the comparisons and extensive simulation verify the scheme is superior [18]. Considering that the non-linearities and dynamics in the EHPS control could be obtained by theoretical analysis and experimental calibration, the model predictive control (MPC) methods could be applied to settle these issues [19,20]. Cai et al developed a trajectory tracking control system, which consists of an active safety unit and a MPC unit, and it was proven to be easy to realize with low cost [21].…”

Section: Introductionmentioning

confidence: 99%

Front wheel angle control for steering system of intelligent commercial vehicle based on model predictive control

Yang

2023

IET Intelligent Trans Sys

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Accurate control of front wheel angle is essential in intelligent vehicle electric‐hydraulic power steering (EHPS) system, because it has a significant impact on the safety, comfort, and reliability of vehicle steering. However, the accurate control of front wheel angle is a challenging problem due to the non‐linearity of hydraulic power in steering system and the uncertainty of steering resistance. In this paper, a model predictive control (MPC) method for front wheel angle based on steering resistance estimation is proposed. Firstly, through theoretical analysis and practical experiment of EHPS system model, the relationship between front wheel angle and steering wheel angle and the non‐linear characteristics of hydraulic power are obtained. Secondly, a sliding mode observer is established to estimate the front axle lateral tyre force and steering resistance, which is verified by MATLAB/TruckSim co‐simulation. Then a controller based on the MPC theory is designed and applied to the accurate control unit of front wheel angle in the EHPS system for real‐time control. Finally, the simulation and test platform results show that the control algorithm proposed in this paper can precisely control the vehicle's front wheel turning angle within a certain frequency range.

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Pressure control for pneumatic electric braking system of commercial vehicle based on model predictive control

Cited by 6 publications

References 26 publications

Failure Propagation Prediction of Complex Electromechanical Systems Based on Interdependence

Failure Propagation Prediction of Complex Electromechanical Systems Based on Interdependence

A Hierarchical Control Scheme for Adaptive Cruise Control System Based on Model Predictive Control

Front wheel angle control for steering system of intelligent commercial vehicle based on model predictive control

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