Negative Stiffness Control of Quasi-Zero Stiffness Air Suspension via Data-Driven Approach with Adaptive Fuzzy Neural Network Method

Ma, Zhixiang; Xu, Xing; Xie, Ju; Jiang, Xiao; Wang, Rui

doi:10.1007/s40815-022-01357-1

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…It is difficult for the existing research to be competent in this kind of control work. (3) In the existing research of the fuzzy self-organizing control framework, the initial rules and structure of fuzzy neural network have a great influence on the control effect [34,35]. The fuzzy neural network usually is self-organized in the control process after setting one or several good initial rules.…”

Section: Introductionmentioning

confidence: 99%

Self-Organizing Interval Type-2 Fuzzy Neural Network Compensation Control Based on Real-Time Data Information Entropy and Its Application in n-DOF Manipulator

Sun

Zhao

Liu

2023

Entropy

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In order to solve the high-precision motion control problem of the n-degree-of-freedom (n-DOF) manipulator driven by large amount of real-time data, a motion control algorithm based on self-organizing interval type-2 fuzzy neural network error compensation (SOT2-FNNEC) is proposed. The proposed control framework can effectively suppress various types of interference such as base jitter, signal interference, time delay, etc., during the movement of the manipulator. The fuzzy neural network structure and self-organization method are used to realize the online self-organization of fuzzy rules based on control data. The stability of the closed-loop control systems are proved by Lyapunov stability theory. Simulations show that the algorithm is superior to a self-organizing fuzzy error compensation network and conventional sliding mode variable structure control methods in control performance.

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Section: Introductionmentioning

confidence: 99%

Self-Organizing Interval Type-2 Fuzzy Neural Network Compensation Control Based on Real-Time Data Information Entropy and Its Application in n-DOF Manipulator

Sun

Zhao

Liu

2023

Entropy

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“…For the design and optimization of semi-active suspension controllers, researchers have proposed the optimal control, sliding mode control, PID control, neural network control, fuzzy control [3][4][5][6] . For example, Attia et al 7 designed a linear quadratic regulator (LQR) based on the optimal control theory to improve the smoothness of vehicles and maintain the stability of roads, but its robustness is poor.…”

mentioning

confidence: 99%

Research on variable universe fuzzy PID control for semi-active suspension with CDC dampers based on dynamic adjustment functions

Ji,

Zhang,

Cai

et al. 2024

Sci Rep

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The vehicle suspension system is a complex system with multiple variables, nonlinearity and time-varying characteristics, and the traditional variable universe fuzzy PID control algorithm has the problems of over-reliance on expert experience and non-adaptive adjustment of the contracting-expanding factor parameters, which make it difficult to achieve a better control effect. In this paper, the system error e(t) and its change rate ec(t) are introduced into the contracting-expanding factor as dynamic parameters to realize the adaptive adjustment of the contracting-expanding factor parameters, and propose a variable universe fuzzy PID control based on dynamic adjustment functions (VUFP-DAF), which uses the real-time contracting-expanding factor to realize the adaptive adjustment of the fuzzy universe, so as to improve the ride comfort of vehicles. The research results show that the proposed VUFP-DAF has strong adaptability and can effectively improve the ride comfort and handling stability of vehicles under different speeds and road excitations, providing a certain technical basis for the development of the semi-active suspension system.

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Optimization of a Pseudo-Active Suspension Network

Qiu,

Bai,

Qian

2024

Lecture Notes in Mechanical Engineering

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Negative Stiffness Control of Quasi-Zero Stiffness Air Suspension via Data-Driven Approach with Adaptive Fuzzy Neural Network Method

Cited by 9 publications

References 29 publications

Self-Organizing Interval Type-2 Fuzzy Neural Network Compensation Control Based on Real-Time Data Information Entropy and Its Application in n-DOF Manipulator

Self-Organizing Interval Type-2 Fuzzy Neural Network Compensation Control Based on Real-Time Data Information Entropy and Its Application in n-DOF Manipulator

Research on variable universe fuzzy PID control for semi-active suspension with CDC dampers based on dynamic adjustment functions

Optimization of a Pseudo-Active Suspension Network

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