Active vibration control of a flexible beam using system identification and controller tuning by evolutionary algorithm

System identification is the process of developing a mathematical model of a dynamic system based on measured input-output data. The main purposes of developing mathematical models are for prediction of the behavior of a system and for controller design. This is important in order to improve the efficiency and the effectiveness of the system. There are four procedures that are involved in developing a model of a dynamical system based on observed input-output data. They are acquisition of data, selection of model presentation, parameter estimation, and model validation (Ljung, 1999). Leontaritis & Billings (1985) introduced the nonlinear auto-regressive moving average with exogenous input (NARMAX) model as a representation for a wide class of nonlinear systems. The essence of the NARMAX model is that past outputs are included in the expansions. This makes the model identification easier since fewer terms are required to represent a system. However, it also means that noise in the output must be taken into account when estimating the model coefficients (Billings, 2013). Recently, polynomial NARMAX model has been widely used in nonlinear system identifications such as in control systems (

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“…Flexible Beam system called FB system is based on the input-output data originating from Saad et al (2015). Fig.…”

Section: Case Study 3: Flexible Beam Systemmentioning

confidence: 99%

“…The first 700 samples was used for model identification and the remaining 300 samples was used for model validation. These data were collected by Saad et al (2015).…”

Section: Fig 3 -Schematic Diagram Of the Experimental Setup (Saad Etmentioning

confidence: 99%

NARMAX Model Identification Using Multi-Objective Optimization Differential Evolution

Zakaria

Mansor

Nor

et al. 2018

IJIE

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“…The tuning of PID parameters was based on Fuzzy Logic in [19] and [20]. In [21], [22], and [23] the authors used a PID controller based on genetic tuning. In [24][25][26][27][28][29] the Differential Evolution and Genetic Algorithm were used to optimize robust flight controllers.…”

Section: Incas Bulletin Volume 9 Issue 2/ 2017mentioning

confidence: 99%

New Methodology for Optimal Flight Control Using Differential Evolution Algorithms Applied on the Cessna Citation X Business Aircraft – Part 1. Design and Optimization

Boughari¹,

Ghazi²,

Mihaela³

et al. 2017

INCAS BULLETIN

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“…However, these approaches always require intensive mathematical derivations, which are usually labor intensive and time consuming. Meanwhile, these modeling techniques are sometimes hindered by factors such as the assumption of ideal boundary conditions, perfect bonding between the host and actuators/sensors, and so on [7].…”

Section: Introductionmentioning

confidence: 99%

Experimental Identification and Vibration Control of A Piezoelectric Flexible Manipulator Using Optimal Multi-Poles Placement Control

et al. 2017

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This paper presents experimental identification and vibration suppression of a flexible manipulator with piezoelectric actuators and strain sensors using optimal multi-poles placement control. To precisely identify the system model, a reduced order transfer function with relocated zeros is proposed, and a first-order inertia element is added to the model. Comparisons show the identified model match closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved. Based on the identified model, a full-state multi-poles placement controller is designed, and the optimal locations of the closed loop poles are determined where the move distance of the closed loop poles is the shortest. The feasibility of the proposed controller is validated by simulations. Moreover, the controller is tested for different locations of the closed loop poles, and an excellent performance of the optimal locations of the closed loop poles is shown. Finally, the effectiveness of the proposed controller is demonstrated by experiments. Results show that the vibrations of the expected modes are significantly diminished. Accordingly, multi-mode vibrations of the manipulator are well attenuated.

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Active vibration control of a flexible beam using system identification and controller tuning by evolutionary algorithm

Cited by 45 publications

References 29 publications

NARMAX Model Identification Using Multi-Objective Optimization Differential Evolution

NARMAX Model Identification Using Multi-Objective Optimization Differential Evolution

New Methodology for Optimal Flight Control Using Differential Evolution Algorithms Applied on the Cessna Citation X Business Aircraft – Part 1. Design and Optimization

Experimental Identification and Vibration Control of A Piezoelectric Flexible Manipulator Using Optimal Multi-Poles Placement Control

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