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This correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are...
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... schematic diagram of an AVC structure is shown in Fig. 2. An unwanted (primary disturbance) point source emits broadband distur- bance into the structure. This is detected by a detector, processed by a controller of suitable transfer characteristics, and fed to a canceling (secondary) point actuator. The secondary (control) signal thus gener- ated interferes with the disturbance to achieve a ...
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... allow the development of an AVC algorithm, consider the sys- tem in Fig. 2 with the detected signal U M as input, and the observed signal Y O as output. For complete cancellation of the disturbance to be achieved at the observation point, the signal Y O = 0 must be forced to become zero. This is equivalent to the minimum variance design criterion in a stochastic environment. This requires the primary and ...
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Traditional approach to vibration control uses passive techniques, which are relatively large, costly and ineffective at low frequencies. Active Vibration Control (AVC) is used to overcome these problems and in AVC additional sources (secondary) are used to cancel vibration from primary source based on the principle of superposition theorem Since t...
ANFIS controller for the active vibration control (AVC) of a flexible plate with single input and single output fashion is designed tuned with LQR controller. ANFIS controller is one of the best controllers to control a specific action. MATLAB simulation software is exercised to design it effectively and with highly accurate results. Finite element...
This paper presents an investigation into the comparative performance of intelligent system identification and control algorithms within the framework of an active vibration control (AVC) system. Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where...
Yes This correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms...
Citations
... To suppress unwanted vibrations, researchers have come up with many vibration control techniques such as passive, semi-active and active [3][4][5]. Passive vibration control (PVC) was extensively by many researchers to suppress the excessive vibration on the structure. However, it does experience some limitations due to its heavy structure. ...
Robotic manipulator with a multi-link structure has a significant role in the majority of today's industries. Controlling the motion of a multi-link manipulator, on the other hand, has proven to be a difficult task, especially when a flexible structure is used. This is due to the complexity of their structures, which causes excessive vibration, resulting in system failure. The goal of this research is to eliminate excessive vibrations produced in order to maintain its efficiency and extend the life of the system. This can be accomplished by developing an intelligent controller for a double-link flexible robotics manipulator (DLFRM) using a proportional integral derivative (PID)
controller approach and bacterial foraging optimization algorithm (BFOA) to tune the PID control parameters. BFOA is a new swarm intelligence optimization algorithm based on E.Coli’s foraging behavior. The BFOA is utilised to tune the PID controller parameters via simulation to achieve the optimum angle for both links present in the DLFRM using MATLAB/Simulink software. Not only that, the BFOA performance is analyzed and compared with conventional method (Ziegler-Nichols) in optimizing the PID controller used to control the hub angle of the manipulator. It is noticed that the PID controller tuned by BFOA exhibited reduced settling time, overshoot, raising time, and steady state error for both hub angles of the manipulator at link 1 and link 2. Then, the system robustness was tested using different types of sinusoidal disturbance and its durability is demonstrated by the fact that it can effectively reduce vibration when the drawback is moved under different disturbances.
... In our research, we¯rst used SA to¯nd the optimal sparse array elements distribution for a spe-ci¯c imaging target, then we can get the photoacoustic signals collected from the optimized sparse array elements. [24][25][26][27][28] Next, we used the sparse reconstruction algorithm based on CS to obtain a high-quality photoacoustic image. This can e®ectively suppress artifacts caused by undersampling, reduce equipment costs, and increase imaging speed. ...
Photoacoustic imaging (PAI) has been developed, and photoacoustic computed tomography (PACT) is widely used for in vivo tissue and mouse imaging. Simulated annealing (SA) algorithm solves optimization problems, and compressed sensing (CS) recovers sparse signals from undersampled measurements. We aim to develop an advanced sparse imaging framework for PACT, which invloves the use of SA to find an optimal sparse array element distribution and CS to perform sparse imaging. PACT reconstructions were performed using a dummy and porcine liver phantoms. Compared to traditional sparse reconstruction algorithms, the proposed method recovers signals using few ultrasonic transducer elements, enabling high-speed, low-cost PACT for practical application.
... Several studies have been done on the systems that combine ANN with other DM methods such as Bayesian ANN [52,53], and fuzzy networks [54]. Recently, applications of ANN such as neural system identification networks (NSINs) [55], Bayesian neural network [56], fuzzy wavelet neural networks [57], parametric ANN [58], wavelet neural network (WNN) [59,60], ANN-based damage classification [61], auto-associative neural network (AANN) [62][63][64], displacement-based neural network emulator (DNNE) [65], multilayer-functional-link neural network (MFLNN) [66], ANN-based response surface technique [67], replicator neural networks (RANN) [68], inverse neural network (INN) [69], nonlinear parameterized neural network (NPNN) [70], improved artificial bee colony (IABC) [71], radial basis function neural network (RBFNN) [72], ANN predictive control algorithm (ANNPCA) [73], and general regression neural network (GRNN) [74] have been widely used to solve civil engineering problems. ...
To date, data mining (DM) techniques, i.e. artificial intelligence, machine learning, and statistical methods have been utilized in a remarkable number of structural health monitoring (SHM) applications. Nevertheless, there is no classification of these approaches to know the most used techniques in SHM. For this purpose, an intensive review is carried out to classify the aforementioned techniques. In doing so, a brief background, models, functions, and classification of DM techniques are presented. To this end, wide range of researches are collected in order to demonstrate the development of DM techniques, detect the most popular DM techniques, and compare the applicability of existing DM techniques in SHM. Eventually, it is concluded that the application of artificial intelligence has the highest demand rate in SHM while the most popular algorithms including artificial neural network, genetic algorithm, fuzzy logic, and principal component analysis are utilized for damage detection of civil structures.
... Since then, plenty of interest has been shown by other researchers to utilize this technique for various applications. [4][5][6][7][8] AVC can be defined as cancelling the vibration at a desired location using superposition of waves by generating a secondary vibration to destruct interferes of unwanted vibration source. 9 In the past decades, many researchers implemented AVC in various applications for industrial processes. ...
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.
... Balancing the offshore structures in the ocean or sea against the forces created by seismic movements and waves is extremely important for the serviceability and the safety of the structure. In order to achieve this, various approaches are currently being considered [1][2][3][4]. Region-specific parameters should be recorded and used when needed to assist these approaches. In addition, the use of artificial intelligence methods, which have been in demand recently, to reduce the oscillation in buildings, allows more effective results in the application that can be done in this field [5][6][7]. ...
... Reducing the vibration parameters such as displacements that may occur as a result of both the earthquake or sea waves, especially in offshore structures, is a situation that needs extra attention. Due to this fact, many studies have been carried out to reduce the vibration that may occur in offshore structures [1][2][3][4]. However, approaches developed for vibration control seem to have some advantages and disadvantages [1][2][3][4]. ...
... Due to this fact, many studies have been carried out to reduce the vibration that may occur in offshore structures [1][2][3][4]. However, approaches developed for vibration control seem to have some advantages and disadvantages [1][2][3][4]. Therefore, it is crucial to choose the optimal approach that will suit the system, considering the characteristics and environmental conditions. ...
Balancing the offshore structures in the ocean or sea against the forces created by seismic movements and waves is extremely important for the serviceability and the safety of the structure. In order to achieve this, various approaches are currently being considered [1-4]. Region-specific parameters should be recorded and used when needed to assist these approaches. In addition, the use of artificial intelligence methods, which have been in demand recently, to reduce the oscillation in buildings, allows more effective results in the application that can be done in this field [5-7]. Using the long-short-time memory (LSTM) algorithm [5], one of the deep learning methods, time series prediction can be performed. As a result of the prediction, better approaches can be developed for the future. This study shows that the vibration control of offshore platforms can be achieved against various types of loadings by the deep learning techniques, which is a branch of artificial intelligence. For this purpose, a long pile is analyzed by solving the equation of motion under forcing described by the Morison equation [8-9]. Thus, a realistic wave load is applied to analyze system behavior in a more realistic setting. The applied wave loads are predicted using the LSTM deep learning network and applied to the system as negative feedback. It is shown that a significant reduction in the vibration amplitudes can be achieved by this approach. Our findings and their possible applications are also discussed.
... Using DEAs, Hosoya et al. constructed a hemispherical breathing mode speaker [25] and a balloon speaker [26] inflated by air, which radiated sound waves over a wide spatial range. Furthermore, active vibration control techniques [27][28][29][30][31][32][33][34][35][36] using DEAs have been exploited [37][38][39][40][41] . Sarban et al. used a tubular DEA with a weight on it to experimentally verify vibration isolation [39] . ...
A vibration suppression technique for lightweight and flexible membrane structures is proposed using smart structure technology with a dielectric elastomer actuator (DEA). DEA, which is a lightweight flexible polynomial material with a large deformation and high response time, can easily be applied to membrane structures for vibration suppression over a wide frequency range. Herein, active vibration control for membrane structures with DEA is examined. A non-contact excitation system with a high-power Nd:YAG pulsed laser precisely measures the vibration responses of membrane structures. A laminated DEA is fabricated and attached to the membrane surface, generating a highly reproducible ideal impulse excitation. Then a digital control system is constructed with the laser excitation system. To demonstrate the effectiveness of the proposed method, vibration control experiments are conducted using a controller designed by H∞ control theory with the identified membrane model in both atmospheric and vacuum environments.
... From the previous study, it can be concluded that despite many developments, the patients are finding difficulties in wearing the devices due to many factors like the weight of the device, range of the frequency-controlled, amplitude factors and actuators used [7][8][9][10][11][12][13][14][15]. To have an efficient system with actuators and higher frequencies to be controlled, a system design is developed with a simple class PID controller for tuning purposes which gives signals to the actuators that in turn control the vibrations of the hand. ...
Parkinson's disease, also known as tremors is one of the leading disability that is prevailing globally. Many people are affected by this disease. Generally, men are 1.5times more prone to suffer from tremors than women. According to scientists, this disease is partially curable by medications and also by complementary support therapies. But these have not resulted to be efficient in controlling tremors. Therefore scientists have developed many models to control the effects of tremors. Based on the previous studies, it was found that many inventions have been done to reduce the effects of vibrations but had few limitations. So, this paper exhibits the system that attains low cost and efficient device by using classic PID controller. This PID controller is tuned in such a way that it gives in best optimum result. The system design is done in Simulink software. This simulation study can be further used to develop a device.
... From the previous study, it can be concluded that despite many developments, the patients are finding difficulties in wearing the devices due to many factors like the weight of the device, range of the frequency-controlled, amplitude factors and actuators used [7][8][9][10][11][12][13][14][15]. To have an efficient system with actuators and higher frequencies to be controlled, a system design is developed with a simple class PID controller for tuning purposes which gives signals to the actuators that in turn control the vibrations of the hand. ...
... In the second case an adaptation algorithm is required to adjust GRNN parameters. The second approach is shown in Some of the applications of GRNN in control systems include dead-zone estimation and compensation in motion control of a traveling wave ultrasonic motor [15], fault diagnosis of power system [16], intelligent battery charger [17], microgrid hybrid power systems control [18], bipedal standing stabilization [19], air conditioning control [20], wind generation system [21], helicopter motion control [22], active vibration control [23], active noise cancellation [24], rat-like robot control [25], pipe climbing robot control [26], tracking-control for an optomechatronical Image derotator [27], tracking in marine navigational radars [28], factory monitoring [29], and flapping wing micro aerial vehicle control [3]. ...
... In the second case an adaptation algorithm is required to adjust GRNN parameters. The second approach is shown in Some of the applications of GRNN in control systems include dead-zone estimation and compensation in motion control of a traveling wave ultrasonic motor [15], fault diagnosis of power system [16], intelligent battery charger [17], microgrid hybrid power systems control [18], bipedal standing stabilization [19], air conditioning control [20], wind generation system [21], helicopter motion control [22], active vibration control [23], active noise cancellation [24], rat-like robot control [25], pipe climbing robot control [26], tracking-control for an optomechatronical Image derotator [27], tracking in marine navigational radars [28], factory monitoring [29], and flapping wing micro aerial vehicle control [3]. ...
This paper depicts a brief revision of Generalized Regression Neural Networks (GRNN) applications in system identification and control of dynamic systems. In addition, a comparison study between the performance of back-propagation neural networks and GRNN is presented for system identification problems. The results of the comparison confirm that GRNN has shorter training time and higher accuracy than the counterpart back-propagation neural networks.
