Vibration suppression of a flexible piezoelectric beam using BP neural network controller

Qiu, Zhi-cheng; Zhang, Xiangtong; Ye, Chunde

doi:10.1016/s0894-9166(12)60037-8

Cited by 32 publications

(20 citation statements)

References 12 publications

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“…In the aspect of artificial intelligence algorithms, controllers for vibration suppression of smart structures are developed using online self-organizing fuzzy logic control [43], multiobjective differential evolution algorithm optimized fuzzy logic control [44], and hybrid algorithm combining fuzzy logic and proportional-integral control [45]. Moreover, neural network control was applied to smart structures numerically [46][47][48] and experimentally [49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Generalized‐Disturbance Rejection Control for Vibration Suppression of Piezoelectric Laminated Flexible Structures

Zhang

Wang

Zhang

et al. 2018

Shock and Vibration

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In the framework of disturbance rejection (DR) control, the paper proposes a generalized-disturbance rejection (GDR) control with proportional-integral (PI) observer for vibration suppression of smart structures under any unknown continuous disturbances. In the proposed GDR-PI control, a refined state space model is first constructed, and a generalized disturbance including the disturbance influence matrices, unknown physical disturbances, and state variables is defined. In the closed loop of GDR-PI control, physical disturbances can be counteracted indirectly by feeding back estimated generalized disturbances. By this means, the GDR-PI control remedies most of the defects in conventional DR control and has excellent performances especially in the following situations: (i) the disturbances are completely unknown; (ii) the number of sensor signals is less than the number of disturbances; (iii) the unknown disturbances vary fast. Finally, the GDR-PI control is validated and compared with ∞ state feedback control and conventional DR control available in the literature for vibration suppression of smart beams.

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Section: Introductionmentioning

confidence: 99%

Generalized‐Disturbance Rejection Control for Vibration Suppression of Piezoelectric Laminated Flexible Structures

Zhang

Wang

Zhang

et al. 2018

Shock and Vibration

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“…A tuning procedure based on reducing the resonant peak on the frequency magnitude plot is successfully presented in [4]. Apart from classical tuning procedures and optimization techniques, using neural networks is also a viable approach [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Discrete-Time Implementation and Experimental Validation of a Fractional Order PD Controller for Vibration Suppression in Airplane Wings

2017

APH

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“…Valoor et al [12] applied recurrent neural networks (RNNs) for vibration control of smart composite beams. Qiu et al [13] used a BP neural network controller for vibration suppression of a flexible piezoelectric beam.…”

Section: Introductionmentioning

confidence: 99%

“…In these figures, SRWNNI provides adequate tracking performance. Figures 12,13,14,15,16 and 17 show the time evolution of the ALRs for SRWNNC under different conditions. As indicated, the learning rates adapt Meccanica quickly using the ALRs algorithm in response to changes in the system dynamics.…”

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confidence: 99%

Self-adaptive vibration control of simply supported beam under a moving mass using self-recurrent wavelet neural networks via adaptive learning rates

2015

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A self-recurrent wavelet neural network (SRWNN) is used to control suppression of vibration of an Euler-Bernoulli beam under excitation of a moving mass traveling along a vibrating path. The proposed control structure uses one SRWNN as an identifier and one as a controller. The SRWNN identifier is trained to model the dynamic behavior of the process and provide the SRWNN controller with information about system sensitivity. The SRWNN controller uses the sensitivity information provided by the SRWNN identifier to update weights and produce a signal that controls beam vibration. The gradient descent method and adaptive learning rates (ALRs) are used to update all SRWNN weights. The ALRs are obtained using the discrete Lyapunov stability theorem which guarantees the convergence of the proposed control structure. The performance and robustness of the proposed controller are evaluated at different mass ratios of moving mass to beam and for dimensionless velocity of a moving mass. The simulations verify the effectiveness and robustness of the controller.

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Vibration suppression of a flexible piezoelectric beam using BP neural network controller

Cited by 32 publications

References 12 publications

Generalized‐Disturbance Rejection Control for Vibration Suppression of Piezoelectric Laminated Flexible Structures

Generalized‐Disturbance Rejection Control for Vibration Suppression of Piezoelectric Laminated Flexible Structures

Discrete-Time Implementation and Experimental Validation of a Fractional Order PD Controller for Vibration Suppression in Airplane Wings

Self-adaptive vibration control of simply supported beam under a moving mass using self-recurrent wavelet neural networks via adaptive learning rates

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