Unwanted noise and vibration control using finite element analysis and artificial intelligence

Zolfagharian, Ali; Noshadi, Amin; Khosravani, Mohammad Reza; Zain, Mohd

doi:10.1016/j.apm.2013.10.039

Cited by 36 publications

(14 citation statements)

References 22 publications

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“…GA has been extensively used in the past and current research works for optimization of single-and multi-objective problems [29][30][31][32]. GA starts the optimization process with a set of randomly generated solutions (population).…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Optimal PID-type fuzzy logic controller for a multi-input multi-output active magnetic bearing system

Noshadi

Shi

Lee

et al. 2015

Neural Comput & Applic

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The performance of the fuzzy controllers depends highly on the proper selection of some design parameters which is usually tuned iteratively via a trial and error process based primarily on engineering intuition. With the recent developments in the area of global optimization, it has been made possible to obtain the optimal values of the design parameters systematically. Nevertheless, it is well known that unless a priori knowledge is available about the optimization search-domain, most of the available time-domain objective functions may result in undesirable solutions. It is consequently important to provide guidelines on how these parameters affect the closedloop behavior. As a result, some alternative objective functions are presented for the time-domain optimization of the fuzzy controllers, and the design parameters of a PID-type fuzzy controller are tuned by using the proposed time-domain objective functions. Finally, the real-time application of the optimal PID-type fuzzy controller is investigated on the robust stabilization of a laboratory active magnetic bearing system. The experimental results show that the designed PID-type fuzzy controllers provide much superior performances than the linear on-board controllers while retaining lower profiles of control signals.

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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Optimal PID-type fuzzy logic controller for a multi-input multi-output active magnetic bearing system

Noshadi

Shi

Lee

et al. 2015

Neural Comput & Applic

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“…Using adaptive-fuzzy control algorithm, Zolfagharian et al [19] presented a mechatronic approach integrating both passive and active controllers to deal with unwanted noise and vibration produced in an automobile wiper system operation, brought a bi-level adaptive-fuzzy controller in whose parameters were tuned simultaneously by a multiobjective genetic algorithm (MOGA) to deal with the conflict interests in wiper control problem.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Flexible Plate Vibration Control by Using Two-Loop Sliding Mode Control Strategy

Yang

Lin

Liu

et al. 2016

J. Inst. Eng. India Ser. C

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It is well known that intelligent control theory has been used in many research fields, novel modeling method (DROMM) is used for flexible rectangular active vibration control, and then the validity of new model is confirmed by comparing finite element model with new model. In this paper, taking advantage of the dynamics of flexible rectangular plate, a two-loop sliding mode (TSM) MIMO approach is introduced for designing multiple-input multiple-output continuous vibration control system, which can overcome uncertainties, disturbances or unstable dynamics. An illustrative example is given in order to show the feasibility of the method. Numerical simulations and experiment confirm the effectiveness of the proposed TSM MIMO controller.

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“…We aim to find a model of the system that is as simple as possible, and yet capable of capturing all the important characteristics of the plant. To overcome this problem, Genetic Algorithm (GA) [35]- [38] is employed to find the required frequency weightings (fictitious noise components) automatically, and perform an iterative re-weighted least squares algorithm. In this approach, GA generates a random vector of w i > 0 to be the diagonal elements of the weight matrix W. Then, GA alters the weights by changing the range and scaling factors in the vector w i .…”

Section: Amb System Description and Model Identificationmentioning

confidence: 99%

System Identification and Robust Control of Multi-Input Multi-Output Active Magnetic Bearing Systems

Noshadi

Shi

Lee

et al. 2016

IEEE Trans. Contr. Syst. Technol.

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Abstract-This paper studies the system identification and robust control of a multi-input multi-output (MIMO) active magnetic bearing (AMB) system. The AMB system under study is open-loop unstable, and the presence of right-half plane zeros and the rotor flexible modes bring additional degrees of difficulty to the control design of such a system. Firstly, a closed-loop system identification is performed by using frequency-domain response data of the system. Genetic Algorithm-based Weighted Least Squares method is employed to obtain the best frequencyweighted model of the system. As the cross-coupling channels have negligible gains in the low-frequency region, it is assumed that the system can be diagonalized. This allows the analysis of the system as a family of low-order single-input single-output (SISO) subsystems. On the other hand, the effects caused by the coupling channels become more significant at higher frequencies. Therefore, a similar method is used to obtain a high-order MIMO model of the system by including the cross-coupling effects. Next, SISO H∞ controllers and lead-lag type compensators are designed on the basis of the SISO models of the systems. To strive for a better performance, MIMO H2 and H∞ optimal controllers are synthesized on the basis of the MIMO model of the system. Extensive experimental studies are conducted on the performance of the designed SISO and MIMO controllers in real-time by taking into consideration both constant disturbances while the rotor is stationary, as well as sinusoidal disturbances caused by the centrifugal forces and the rotor mass-imbalance while the rotor is in rotation. Unlike the recently published works, it is shown that the accurate modeling of the system being controlled is the key to successful design of high-performance stable controllers that not only guarantee the internal stability of the system-controller interconnection, but also no further modifications are required before the real-time implementation of the designed controllers.

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Unwanted noise and vibration control using finite element analysis and artificial intelligence

Cited by 36 publications

References 22 publications

Optimal PID-type fuzzy logic controller for a multi-input multi-output active magnetic bearing system

Optimal PID-type fuzzy logic controller for a multi-input multi-output active magnetic bearing system

Experimental Study of Flexible Plate Vibration Control by Using Two-Loop Sliding Mode Control Strategy

System Identification and Robust Control of Multi-Input Multi-Output Active Magnetic Bearing Systems

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