Research on electric power steering fuzzy PI control strategy based on phase compensation

Zheng, Zhu‘An; Wei, JinCheng

doi:10.1007/s40435-022-01077-2

Cited by 10 publications

(7 citation statements)

References 6 publications

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Section: Introductionmentioning

confidence: 96%

“…These conditions must satisfy the optimal requirements of the algorithm. When the system is affected by other conditions, the optimal state will not be guaranteed.Using intelligent algorithms, such as fuzzy (Zheng and Wei, 2022; Hassan et al ., 2012b) or BPNN (Li et al ., 2020), to flexibly adjust the controller’s parameters is a highly effective solution. This helps the system adapt well to external influences.…”

Section: Introductionmentioning

confidence: 99%

“…Using intelligent algorithms, such as fuzzy (Zheng and Wei, 2022; Hassan et al ., 2012b) or BPNN (Li et al ., 2020), to flexibly adjust the controller’s parameters is a highly effective solution. This helps the system adapt well to external influences.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, we can use fuzzy algorithms to dynamically tune the controller parameters to respond to specific conditions instead of just finding an optimal value. This is highlighted by Zheng and Wei (2022). In Hassan et al (2012b), claimed that a fuzzy-PID controller can help to reduce the energy consumption of EPS systems better than traditional PID.…”

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confidence: 99%

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Improving stability and adaptability of automotive electric steering systems based on a novel optimal integrated algorithm

Nguyen,

Iqbal

2024

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PurposeDesign a novel optimal integrated control algorithm for the automotive electric steering system to improve the stability and adaptation of the system.Design/methodology/approachSimulation and calculation.FindingsThe output signals follow the reference signal with high accuracy.Originality/valueThe optimal integrated algorithm is established based on the combination of PID and SMC. The parameters of the PID controller are adjusted using a fuzzy algorithm. The optimal range of adjustment values is determined using a genetic algorithm.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Improving stability and adaptability of automotive electric steering systems based on a novel optimal integrated algorithm

Nguyen,

Iqbal

2024

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“…An EPS control strategy for steering feel control based on closed-loop control of steering motion is presented in [48]. The control strategy for AEPS based on active disturbance rejection algorithm is presented in [49]. The interval optimal impedance control method for electric-human steering system with reliability constraints is proposed in [50].…”

mentioning

confidence: 99%

Automotive Electric Power Steering Control With Robust Observer Based Neuroadaptive Type-2 Radial Basis Function Methodology

Amirkhani,

Shirzadeh,

Heydari

2024

IEEE Open J. Veh. Technol.

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In this paper, a simplified type-2 (ST2) radial basis function (RBF) based neuroadaptive technique for controlling an automotive electric power steering (AEPS) system is designed. The dynamics of the AEPS are assumed to be unknown and the system is subjected to certain disturbances. A ST2-RBF system is proposed for approximating the unknown nonlinear functions. The ST2-RBF parameters are tuned online based on the adaptation laws obtained via Lyapunov stability analysis. A robust observer is also used in this process. The effects of uncertainties as well as approximation and estimation errors are compensated by means of an adaptive component. The parameters of the robust observer-based neuroadaptive ST2-RBF network are optimally determined by applying the Coronavirus disease optimization algorithm (COVIDOA), which mimics the replication mechanism of Coronaviruses taking over the human cells. The results indicate that the COVIDOA can reduce the cost function for neuroadaptive ST2-RBF controller compared to other strategies. Comparison of numerical results is presented to show the efficacy of the suggested technique. Interestingly, based on implementation results, the designed methodology is able to control the AEPS system successfully.

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Genetic algorithm inspired optimal integrated nonlinear control technique for an electric power steering system

Nguyen,

Iqbal

2024

J Braz. Soc. Mech. Sci. Eng.

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Research on electric power steering fuzzy PI control strategy based on phase compensation

Cited by 10 publications

References 6 publications

Improving stability and adaptability of automotive electric steering systems based on a novel optimal integrated algorithm

Improving stability and adaptability of automotive electric steering systems based on a novel optimal integrated algorithm

Automotive Electric Power Steering Control With Robust Observer Based Neuroadaptive Type-2 Radial Basis Function Methodology

Genetic algorithm inspired optimal integrated nonlinear control technique for an electric power steering system

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