Study on Multi-Mode Switching Control of Intelligent Suspension under Full Road Section

Qu, Zhipeng; Liu, Jianze; Li, Yang; Yang, Fuzi; Liu, Jiang

doi:10.3390/pr11061776

Cited by 4 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several other studies can also be found in the literature and include control approaches such as the linear quadratic regulator [21][22][23], adaptive sliding control [24], H∞ control [25,26], advanced fuzzy control [27,28], optimal control [15,29] and neural-networkbased methods for tuning [30,31].…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation of a Feedback Controller for an Active Suspension System: A Simplified Approach

Provatas,

Ipsakis

2023

Processes

View full text Add to dashboard Cite

The concept of controlling vehicle comfort is a common problem that is faced in most under- and postgraduate courses in Engineering Schools. The aim of this study is to provide a simplified approach for the feedback control design and simulation of active suspension systems, which are applied in vehicles. Firstly, the mathematical model of an active suspension system (a quarter model of a car) which consists of a passive spring, a passive damper and an actuator is provided. In this study, we chose to design and compare the following controllers: (a) conventional P, PI and PID controllers that were tuned through two conventional methodologies (Ziegler–Nichols and Tyreus–Luyben); (b) an optimal PID controller that was tuned with a genetic algorithm (GA) optimization framework in terms of the minimization of certain performance criteria and (c) an internal model controller (IMC) based on the process transfer function. The controllers’ performance was assessed in a series of realistic scenarios that included set-point tracking with and without disturbances. In all cases, the IMC controller and the optimal PID showed superior performance. On the other hand, the P and PI controllers showed a rather insufficient behavior that involved persistent errors, overshoots and eventually, uncomfortable ride oscillations. Clearly, a step-by-step approach such as this, that includes modeling, control design and simulation scenarios can be applied to numerous other engineering examples, which we envisage to lead more students into the area of automatic control.

show abstract

Section: Introductionmentioning

confidence: 99%

Design and Simulation of a Feedback Controller for an Active Suspension System: A Simplified Approach

Provatas,

Ipsakis

2023

Processes

View full text Add to dashboard Cite

show abstract

Design and control of a new omnidirectional levelling system for hilly crawler work machines

Jiang,

Sun,

Wang

et al. 2024

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

Adaptive suspension state estimation based on IMMAKF on variable vehicle speed, road roughness grade and sprung mass condition

Wu,

Shi,

Zhang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Vehicle speed, road roughness grade and sprung mass are the three main factors to influence suspension control and state estimation. Aiming at the problem that fixed state observer cannot guarantee the estimation accuracy of suspension with driving scenario changes, a suspension state observer based on interactive multiple model adaptive Kalman filter (IMMAKF) is established. Firstly, an adaptive control suspension is proposed based on LQR algorithm and multi-objective optimization algorithm, which can automatically adjust the controller parameters according to the vehicle speed, road roughness grade and sprung acceleration parameters, so as to keep the optimal control effect of the suspension. Secondly, the theoretical model of IMMAKF is derived, and two kinds of IMMAKF suspension state observers and controllers are established. Finally, a simulation condition with the vehicle speed, road roughness grade and sprung mass changing simultaneously is established. The simulation results shows that: compared with ordinary IMMKF, AKF and KF observers, the estimation accuracy of IMMAKF5 is improved. Except for state observation, IMMAKF can be used to identify the road roughness grade and estimate the suspension sprung mass.

show abstract

Study on Multi-Mode Switching Control of Intelligent Suspension under Full Road Section

Cited by 4 publications

References 22 publications

Design and Simulation of a Feedback Controller for an Active Suspension System: A Simplified Approach

Design and Simulation of a Feedback Controller for an Active Suspension System: A Simplified Approach

Design and control of a new omnidirectional levelling system for hilly crawler work machines

Adaptive suspension state estimation based on IMMAKF on variable vehicle speed, road roughness grade and sprung mass condition

Contact Info

Product

Resources

About