Parameter-dependent actuator fault estimation for vehicle active suspension systems based on RBFNN

Xue, Wenping; Jin, Pan; Li, Kangji

doi:10.1177/0954407021993014

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…The vector control algorithm based on the PI controller is the basis for achieving PMSM servo system control, and it is also the purpose of studying PMSM identification, usually using double-closed vector control, as shown in Figure 1. Among them, the current ring and speed ring are respectively controlled by PI controller to achieve current and speed control, then a double closed-loop structure can be realized [8]. The function of the speed ring is to achieve precise control of speed and ensure that the speed of the PMSM is consistent with the instruction value.…”

Section: Pmsm Vector Control System Modelingmentioning

confidence: 99%

Mechanical parameter recognition of permanent magnet synchronous motor based on RBFNN algorithm

Feng,

Wu,

et al. 2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

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Aiming at the problem of mechanical parameters that affect the real-time changes of permanent magnet synchronous motors affecting the performance of the servo system, in this paper, we propose a permanent magnet synchronous motor mechanical parameter recognition solution based on the radial basis function neural network. Based on the designed radial neural network, we conduct parameter recognition and analysis of the three major mechanical parameters of rotation inertia, viscous damping, and load torque, and verify the superiority of this solution through a comparison experiment. The result proves that compared to the forgetting factor recursive least-squares, the parameter recognition scheme based on the radial basis function neural network designed in this article is reduced by 44.4%, the recognition accuracy has increased by 8%, and can recognize the friction coefficient and load torque, so it has certain practical application significance.

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Section: Pmsm Vector Control System Modelingmentioning

confidence: 99%

Mechanical parameter recognition of permanent magnet synchronous motor based on RBFNN algorithm

Feng,

Wu,

et al. 2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

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“…It has become the research focus of scholars. 7,8 As a result, numerous control methods have been applied to the system to improve the comprehensive suspension performance, such as model predictive control, 9,10 sliding model control, 11–13 adaptive control, 14–16 fault tolerant control, 17 and robust control. 18–22 In particular, the robust control, such as H 2 and H ∞ has been widely studied owing to its robustness.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy robust non-fragile control for nonlinear active suspension systems with time varying actuator delay

Xie

You

Wong

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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The active suspension systems have attracted increasing attentions because of its advantages in improving automotive dynamic performance. This paper focuses on the fuzzy robust non-fragile [Formula: see text] control problem for the nonlinear active suspension system with the time-varying actuator delay and control uncertainty. Firstly, a Takagi-Sugeno fuzzy suspension model is established to describe the highly nonlinear components in the suspension system. Then, a series of linear matrix inequalities are derived based on a novel parameter Lyapunov function to guarantee the asymptotic stability of the suspension systems. Also, a fuzzy [Formula: see text] controller is derived for the proposed fuzzy suspension model with the parallel distributed compensation method. Thirdly, a co-design criterion is proposed to obtain the fuzzy controller with the time varying delay and control uncertainty simultaneously. Finally, both simulations and experiments are carried out to demonstrate the effectiveness and robustness of the proposed controller.

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“…They used the finite-element neural network method to approximate the observer fault, and turned it into a linear matrix inequality problem. This method can obtain the results faster and more accurately, and can well adapt to the changing spring mass [27]. The above methods are very effective, but the actuator fault diagnosis and fault-tolerant control for the ECAS system are rarely used.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Fault Diagnosis of Actuators in Electronically Controlled Air Suspension System

Jiang

Shan

2022

WEVJ

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The air suspension adjusts the height of the vehicle body through charging and bleeding air to meet the high performance of the vehicle, which needs a reliable electronic control system. Through fault tree analysis of the electronically controlled air suspension (ECAS) system and considering the correlation between the duty cycle and flow rate of the air spring solenoid valve, the fault model of the solenoid valve is constructed, and the fault diagnosis design method of the ECAS system solenoid valve based on multiple extended Kalman filter banks (EKFs) is proposed. An adaptive threshold is used to realize fault diagnosis, and active fault-tolerant control is carried out based on an analytical model. The real controller based on d2p rapid prototyping technology and the vehicle model based on AMESim are further verified on the hardware-in-the-loop (HiL) simulation test platform and compared with the pure simulation results. The test results show that the fault diagnosis and fault-tolerant control algorithm can work normally in the actual controller, and can effectively realize the fault diagnosis and fault-tolerant control of the actuator in the vehicle ECAS system.

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Parameter-dependent actuator fault estimation for vehicle active suspension systems based on RBFNN

Cited by 4 publications

References 41 publications

Mechanical parameter recognition of permanent magnet synchronous motor based on RBFNN algorithm

Mechanical parameter recognition of permanent magnet synchronous motor based on RBFNN algorithm

Fuzzy robust non-fragile control for nonlinear active suspension systems with time varying actuator delay

Model-Based Fault Diagnosis of Actuators in Electronically Controlled Air Suspension System

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