Article

Event-Triggered Asynchronous Fuzzy Filtering for Vehicle Sideslip Angle Estimation With Data Quantization and Dropouts

Wenfeng LiSchool of Mechanical and Automotive Engineering and the Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, South China University of Technology, Guangzhou, ChinaZhengchao XieSchool of Mechanical and Automotive Engineering and the Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, South China University of Technology, Guangzhou, ChinaPak Kin WongDepartment of Electromechanical Engineering, University of Macau, Macau, ChinaYunfeng HuState Key Laboratory of Automotive Simulation and Control, Department of Control Science and Engineering, Jilin University, Changchun, ChinaGe GuoState Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, ChinaJing ZhaoDepartment of Electromechanical Engineering, University of Macau, Macau, China

2021en

ABI

Abstract

This article investigates the event-triggered fuzzy filtering issue for vehicle sideslip angle estimation with consideration of data quantization and dropouts. First, an uncertain Takagi–Sugeno fuzzy model is developed to describe vehicle nonlinear dynamics resulted from nonlinear tire dynamics, varying velocity, uncertain mass, and yaw moment inertia. Then, an adaptive event-triggered scheme is introduced between the sensor and the filter for the decision of releasing sampled data to economize limited network resource. Moreover, the network-induced constraints, such as delay, data quantization, and dropouts, are taken into account to improve the robustness of the filtering method. Based on the Lyapunov stability theory, a new event-triggered asynchronous fuzzy filtering method is proposed by establishing an augmented Lyapunov–Krasovskii functional candidate and applying integral inequalities in the derivation. Finally, simulation results are presented to verify the advantages of the proposed method in comparison with the existing results.

Identifiers

DOI: 10.1109/tfuzz.2021.3075761

Citations and references

Cited by 20 references

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