Active vibration control of a horizontal flexible plate structure using intelligent proportional–integral–derivative controller tuned by fuzzy logic and artificial bee colony algorithm

Hadi, Muhamad Sukri; Darus, Intan Zaurah Mat; Tokhi, M. O.; Jamid, M. F.

doi:10.1177/1461348419852454

Cited by 25 publications

(14 citation statements)

References 26 publications

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“…These include fuzzy logic control (FLC), 2,3 neural network control [4][5][6] and hybrid proportional-derivative FLC. 7,8 The main advantage of an intelligent-based control over a conventional-based control is that it does not require an accurate mathematical model of the system. Intelligent-based control can handle nonlinear and complex problems more utilised certain BFA strategies and incorporated into SDA such that the accuracy performance of SDA is improved.…”

Section: Introductionmentioning

confidence: 99%

Hybrid spiral-bacterial foraging algorithm for a fuzzy control design of a flexible manipulator

Nasir

Ashraf

Tokhi

2021

Journal of Low Frequency Noise, Vibration and Active Control

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A novel hybrid strategy combining a spiral dynamic algorithm (SDA) and a bacterial foraging algorithm (BFA) is presented in this article. A spiral model is incorporated into the chemotaxis of the BFA algorithm to enhance the capability of exploration and exploitation phases of both SDA and BFA with the aim to improve the fitness accuracy for the SDA and the convergence speed as well as the fitness accuracy for BFA. The proposed algorithm is tested with the Congress on Evolutionary Computation 2013 (CEC2013) benchmark functions, and its performance in terms of accuracy is compared with its predecessor algorithms. Consequently, for solving a complex engineering problem, the proposed algorithm is employed to obtain and optimise the fuzzy logic control parameters for the hub angle tracking of a flexible manipulator system. Analysis of the performance test with the benchmark functions shows that the proposed algorithm outperforms its predecessor algorithms with significant improvements and has a competitive performance compared to other well-known algorithms. In the context of solving a real-world problem, it is shown that the proposed algorithm achieves a faster convergence speed and a more accurate solution. Moreover, the time-domain response of the hub angle shows that the controller optimised by the proposed algorithm tracks the desired system response very well.

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Section: Introductionmentioning

confidence: 99%

Hybrid spiral-bacterial foraging algorithm for a fuzzy control design of a flexible manipulator

Nasir

Ashraf

Tokhi

2021

Journal of Low Frequency Noise, Vibration and Active Control

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“…Hence, active vibration control (AVC) is proposed in this research study to overcome low frequency vibrations faced by flexible plate systems. 2 Lueg is a pioneer who introduced the fundamentals of AVC technique for noise cancellation in the early 1930s. 3 He proposed the superposition theory where unwanted sound signal may be cancelled out by introducing a secondary sound signal 180 out of the phase.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, active vibration control (AVC) is proposed in this research study to overcome low frequency vibrations faced by flexible plate systems. 2…”

Section: Introductionmentioning

confidence: 99%

Vibration suppression of the horizontal flexible plate using proportional– integral–derivative controller tuned by particle swarm optimization

Hadi

Darus

Talib

et al. 2021

Journal of Low Frequency Noise, Vibration and Active Control

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This paper presents the development of an active vibration control for vibration suppression of the horizontal flexible plate structure using proportional–integral–derivative controller tuned by a conventional method via Ziegler–Nichols and an intelligent method known as particle swarm optimization algorithm. Initially, the experimental rig was designed and fabricated with all edges clamped at the horizontal position of the flexible plate. Data acquisition and instrumentation systems were designed and integrated into the experimental rig to collect input–output vibration data of the flexible plate. The vibration data obtained through experimental study was used to model the system using system identification technique based on auto-regressive with exogenous input structure. The plate system was modeled using particle swarm optimization algorithm and validated using mean squared error, one-step ahead prediction, and correlation tests. The stability of the model was assessed using pole zero diagram stability. The fitness function of particle swarm optimization algorithm is defined as the mean squared error between the measured and estimated output of the horizontal flexible plate system. Next, the developed model was used in the development of an active vibration control for vibration suppression on the horizontal flexible plate system using a proportional–integral–derivative controller. The proportional–integral–derivative gains are optimally determined using two different ways, the conventional method tuned by Ziegler–Nichols tuning rules and the intelligent method tuned by particle swarm optimization algorithm. The performances of developed controllers were assessed and validated. Proportional–integral–derivative-particle swarm optimization controller achieved the highest attenuation value for first mode of vibration by achieving 47.28 dB attenuation as compared to proportional–integral–derivative-Ziegler–Nichols controller which only achieved 34.21 dB attenuation.

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“…It plays a significant role in a diverse range of fields, such as construction automation, environmental applications, space engineering and many more. However, because of its flexible structure system, it is dealing with high amount of vibration that affects the efficiency and performance as vibration will cause fatigue to the machine [3]. Thus, to resolve this problem, efforts and alternatives are introduced by researchers as it is an alarming issue in the industry.…”

Section: Introductionmentioning

confidence: 99%

Modelling of Flexible Manipulator System via Ant Colony Optimization for Endpoint Acceleration

Nazri

Hadi

Yatim

et al. 2021

J. Phys.: Conf. Ser.

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The application of flexible manipulators has increased in recent years especially in the fourth industrial revolution. It plays a significant role in a diverse range of fields, such as construction automation, environmental applications, space engineering and many more. Due to the lightweight, lower inertia and high flexibility of flexible manipulators, undesired vibration may occur and affect the precision of operation. Therefore, development of an accurate model of the flexible manipulator was presented prior to establishing active vibration control to suppress the vibration and increase efficiency of the system. In this study, flexible manipulator system was modelled using the input and output experimental data of the endpoint acceleration. The model was developed by utilizing intelligence algorithm via ant colony optimization (ACO), commonly known as a population-based trail-following behaviour of real ants based on autoregressive with exogenous (ARX) model structure. The performance of the algorithm was validated based on three robustness methods known as lowest mean square error (MSE), correlation test within 95% confidence level and pole zero stability. The simulation results indicated that ACO accomplished superior performance by achieving lowest MSE of 2.5171×10−7 for endpoint acceleration. In addition, ACO portrayed correlation tests within 95% confidence level and great pole-zero stability.

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Active vibration control of a horizontal flexible plate structure using intelligent proportional–integral–derivative controller tuned by fuzzy logic and artificial bee colony algorithm

Cited by 25 publications

References 26 publications

Hybrid spiral-bacterial foraging algorithm for a fuzzy control design of a flexible manipulator

Hybrid spiral-bacterial foraging algorithm for a fuzzy control design of a flexible manipulator

Vibration suppression of the horizontal flexible plate using proportional– integral–derivative controller tuned by particle swarm optimization

Modelling of Flexible Manipulator System via Ant Colony Optimization for Endpoint Acceleration

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