Networked Motion Control for Smart EV With Multiple-Package Transmissions and Time-Varying Network-Induced Delays

Cao, Wanke; Liu, Zhiwei; Li, Jianwei; He, Haibo

doi:10.1109/tie.2021.3070499

Cited by 13 publications

(13 citation statements)

References 38 publications

(58 reference statements)

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“…As shown in the research [25], [33], a typical expression of the reference state model is adopted here as shown in Eq.2, where the desired/reference sideslip angle is set to zero, and the desired/reference yaw rate is usually defined by steering angle, vehicle speed and structural parameters. The reference state model selects the yaw rate and the side slip angle of the CG:…”

Section: B Reference State Modelmentioning

confidence: 99%

“…As shown in the study [25], the application of in-vehicle network will lead to the new problem, e.g. inevitable networkinduced delays as shown in Fig.…”

Section: B Uncertainty In Data Communication Processmentioning

confidence: 99%

“…Where τ loop k is the delay of the entire control loop, τ sc k is the delay of the feedback channel from the sensor to the controller, τ ca k is delay the forward channel from the controller to the actuator. The details of multiple-package transmissions and time-varying network-induced delays are shown in [25].…”

Section: B Uncertainty In Data Communication Processmentioning

confidence: 99%

“…dynamic parameters perturbation caused by network-induced delay, which will make the controller design a challenge problem and even make the system unstable [7], [23], [24]. There have been some researches focusing on lateral motion control considering uncertainty caused by network-induced delay [7], [23], [25]- [27]. Klehmet et al and Herpel et al proposed a delay analysis method based on network calculus theory for calculating the worst-case response time of each message sent on CAN in automotive applications [26], [27].…”

Section: Introductionmentioning

confidence: 99%

“…Zhu et al [23] proposed a delay analysis based on two Markov chains theory to model the CAN-induced delay in feedback and forward channels, and designed a robust LQR-based H ∞ controller to deal with the oscillation of the time-delay vehicle lateral motion control with less conservation. Liu et al [25] presented a delay analysis on network-induced delays with considering multiple-package transmissions, and designed a hybrid schedule-control framework to deal with the uncertainty caused by time-varying network-induced delays, and ensure the stability of the time-delay vehicle lateral motion control.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Motion Control With Cyber-Physical Uncertainty Tolerance for Distributed Drive Electric Vehicle

et al. 2022

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A lateral motion control scheme for a distributed drive electric vehicle is presented in this paper, which takes into account both in-car network and movement-parameter uncertainty in a synthetic manner. Distributed drive vehicles have obvious advantages in terms of safety and comfort at high speeds due to the well-known E/E architecture, which includes an in-vehicle network, advanced vehicle motion control, and Advanced Driver Assistance System (ADAS) technologies. This is a fundamentally cyber-physical system. However, on the other hand, the application/insertion of in-vehicle network and the dynamic of wide-range varying speeds introduce additional system uncertainties, such as time-varying network induced delays and inevitable system perturbation, making controller design a difficult problem and even making the system unstable. This paper develops a cyber-physical control scheme and under which a two-process perturbation analysis is proposed to illustrate the system uncertainties. A hierarchical control strategy is also devised, with an upper-level gain-scheduling controller dealing with speed perturbation uncertainties and a lowerlevel H ∞ -LQR controller dealing with in-vehicle network uncertainty. Using real-time hardware in loop testing, the suggested control technique was found to be effective in dealing with both in-vehicle network and system perturbation problems while also ensuring reliable vehicle stability in all three scenarios.INDEX TERMS Distributed drive electric vehicle, cyber -physical, direct yaw-moment control (DYC), H ∞ -based linear quadratic regulator (H ∞ -LQR), gain-scheduling, two-process perturbation analysis.

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Section: B Reference State Modelmentioning

confidence: 99%

“…As shown in the study [25], the application of in-vehicle network will lead to the new problem, e.g. inevitable networkinduced delays as shown in Fig.…”

Section: B Uncertainty In Data Communication Processmentioning

confidence: 99%

Section: B Uncertainty In Data Communication Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Motion Control With Cyber-Physical Uncertainty Tolerance for Distributed Drive Electric Vehicle

et al. 2022

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Scheduling strategy for networked control system based on variable sampling period and deadband feedback

Lian

2023

Int J Communication

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A scheduling strategy of variable sampling period combined with deadband feedback for networked control system is proposed. Variable sampling period algorithm can allocate a reasonable sampling period to each controlled loop according to the network utilization and packet transmission time. Deadband feedback algorithm can alleviate network congestion by appropriately adjusting the packets in the network when the networked control system cannot be scheduled. According to the actual overload and utilization of the network, the designed scheduling strategy dynamically adjusts the sampling period and priority, and improves the performance of the system combined with deadband feedback. Based on the TrueTime platform, the proposed scheduling strategy is verified on a three controlled loops networked control system with interference nodes and limited network resources. The simulation results demonstrate that the designed scheduling strategy can overcome the uncertainty of the upper bound of network resources, improve output control performance, reduce integral absolute error value of the controlled loop, and shorten the packet transmission time. The overall control performance of the system is improved. The designed scheduling strategy is effective.

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Robust lateral motion control of distributed drive vehicle considering long input delays

Xie

et al. 2022

Intl J Robust & Nonlinear

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This article presents a comparative study of lateral motion controller design for distributed drive vehicle. Aiming at handling long input delays, two types of robust controllers are specifically investigated. First, a Lyapunov-Krasovskii functional based robust controller design is proposed. Considering input delay, the design conditions of the state-feedback controller are derived in terms of linear matrix inequalities (LMIs), which can be effectively solved using LMI toolbox. Second, based on the same control law, a robust predictive controller design is further presented. However, different from the Lyapunov-Krasovskii functional based robust controller, the control gain of the proposed robust predictive controller is calculated without considering the input delay. An input delay estimator and a predictor are developed instead to reconstruct the delay-dependent state, which is further used to generate a new feedback error. Moreover, the feasibility of this control scheme is theoretically proved. Finally, extensive co-simulations with Carsim and Matlab/Simulink are conducted to compare the performance between these two controllers. Compared with the Lyapunov-Krasovskii functional based robust controller, longer input delay can be handled by using the proposed robust predictive controller.

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Networked Motion Control for Smart EV With Multiple-Package Transmissions and Time-Varying Network-Induced Delays

Abstract: This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Cited by 13 publications

References 38 publications

An Improved Motion Control With Cyber-Physical Uncertainty Tolerance for Distributed Drive Electric Vehicle

An Improved Motion Control With Cyber-Physical Uncertainty Tolerance for Distributed Drive Electric Vehicle

Scheduling strategy for networked control system based on variable sampling period and deadband feedback

Robust lateral motion control of distributed drive vehicle considering long input delays

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