Tube-based Model Reference Adaptive Control for Vibration Suppression of Active Suspension Systems

Mousavi, Yashar; Alfi, Alireza; Küçükdemiral, İbrahim Beklan; Fekih, Afef

doi:10.1109/jas.2022.105470

Cited by 10 publications

(2 citation statements)

References 10 publications

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“…Many research results have effectively solved various vibration reduction problems of multicopters during flight. Among them, input shaping, as a classic and widely-used vibration reduction technique, has been applied to a variety of practical plants, such as rolling bearing systems [7], flexible spacecraft [8] and active suspension system [9]. Based on the input shaping technique, a differential flatness scheme is proposed to solve the vibration problem of microelectromechanical systems, and the related simulation results show that this scheme can save energy significantly [10].…”

Section: Introductionmentioning

confidence: 99%

Quadcopter attitude control with vibration reduction by additive-state-decomposition dynamic inversion design with a notch filter

Chen

Zhang²,

Quan³

2023

Nonlinear Dyn

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In practice, the unbalanced mass of the propeller is the leading cause of vibration in a quadcopter. First, a mechanical model of the unbalanced masses is established. Next, consider the vibration reduction problem for quadcopter attitude control. A notch filter is incorporated into to an additive-state-decomposition dynamic inversion controller to obtain a new controller. Stability analysis shows that the proposed control method guarantees that all attitude signals are globally uniformly ultimately bounded. In particular, the notch filter can effectively reduce the vibration having a specific frequency. Finally, the proposed controller is performed on a real quadcopter to verify its vibration reduction performance.

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Section: Introductionmentioning

confidence: 99%

Quadcopter attitude control with vibration reduction by additive-state-decomposition dynamic inversion design with a notch filter

Chen

Zhang²,

Quan³

2023

Nonlinear Dyn

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“…This configuration has the capability to either replace or function concurrently with the conventional damper-spring setup. In the last decade, several control methodologies have emerged for the enhancement of active suspension systems, including robust control [1][2][3][4][5][6], previewbased active suspension [7][8][9], optimal control [9][10][11][12], and adaptive control [4,[13][14][15][16]. Since neural networks and fuzzy controls are the key branches of intelligent control, numerous studies have been conducted on using adaptive control methods, neural networks, and fuzzy control theory to control active suspension systems [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Control of Active Vehicle Suspension Based on H2 and H∞ Synthesis

Esmaeili,

Akbari,

Farnam

et al. 2023

Machines

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This paper addresses the issue of a road-type-adaptive control strategy aimed at enhancing suspension performance. H2 synthesis is employed for modeling road irregularities as impulses or white noise, minimizing the root mean square (RMS) of performance outputs for these specific road types. It should be noted, however, that this approach may lead to suboptimal performance when applied to other road profiles. In contrast, the H∞ controller is employed to minimize the RMS of performance outputs under worst-case road irregularities, taking a conservative stance that ensures robustness across all road profiles. To leverage the advantages of both controllers and achieve overall improved suspension performance, automatic switching between these controllers is recommended based on the identified road type. To implement this adaptive switching mechanism, manual switching is performed, gathering input–output data from the controllers. These data are subsequently employed for training an Adaptive Neuro-Fuzzy Inference System (ANFIS) network. This elegant approach contributes significantly to the optimization of suspension performance. The simulation results employing this novel ANFIS-based controller demonstrate substantial performance enhancements compared to both the H2 and H∞ controllers. Notably, the ANFIS-based controller exhibits a remarkable 62% improvement in vehicle body comfort and a significant 57% enhancement in ride safety compared to passive suspension, highlighting its potential for superior suspension performance across diverse road conditions.

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Research on Transition Process Control of STOVL Aircraft Based on Moment Allocation and MRAC Method

Shilong

Tian

He-qi

et al. 2023

Lecture Notes in Electrical Engineering

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Tube-based Model Reference Adaptive Control for Vibration Suppression of Active Suspension Systems

Cited by 10 publications

References 10 publications

Quadcopter attitude control with vibration reduction by additive-state-decomposition dynamic inversion design with a notch filter

Quadcopter attitude control with vibration reduction by additive-state-decomposition dynamic inversion design with a notch filter

Adaptive Neuro-Fuzzy Control of Active Vehicle Suspension Based on H2 and H∞ Synthesis

Research on Transition Process Control of STOVL Aircraft Based on Moment Allocation and MRAC Method

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