Dynamic Modelling of a Single-Link Flexible Manipulator System: A Particle Swarm Optimisation Approach

Alam, M. S.; Tokhi, M. O.

doi:10.1260/026309207781487466

Cited by 17 publications

(15 citation statements)

References 37 publications

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“…8 shows that the three mostly adopted tools are Neural Networks [434]- [442], Evolutionary Computing [443], including genetic algorithms [444]- [451], and fuzzy logic [452]- [460], while less consideration has been received by Particle Swarm Optimization [461]- [465]. Related to Vibrations, Structural Health Monitoring has been performed by using NN [466]- [468], GAs [469]- [471], bio-inspired algorithms [472] and fuzzy logic [473].…”

Section: G Applications Of Computational Intelligence To Vibration Mmentioning

confidence: 99%

Applications of computational intelligence to mechanical engineering

Belfiore

Rudas

2014

2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI)

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Most of the methods and tools of Computational Intelligence (CI), such as Artificial Neural Networks, Evolutionary Computation, Fuzzy logic, Computational Swarm Intelligence and Artificial Immune Systems, have been developed for decades. However, their applications are not disseminated as exhaustively as they could be, for many reasons. In this work, a survey on some applications of Computational Intelligence to Mechanical Engineering will be presented. This review has been based on statistical analysis of a large amount of data extracted from major bibliographic databases. This analysis provides a quantitative map of the variety of CI methods and tools and their applications to some fields of Mechanical Engineering. The results will suggest to researchers some important topics of Mechanical Engineering where CI has great potential and may improve its impact

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Section: G Applications Of Computational Intelligence To Vibration Mmentioning

confidence: 99%

Applications of computational intelligence to mechanical engineering

Belfiore

Rudas

2014

2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI)

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“…Several estimations using parametric approaches have been utilized for modeling the flexible manipulators such as the conventional RL [13], RELS [14] and RLS and least mean square (LMS) [15]. Apart from that, parametric approaches using intelligent methods have attracted many researchers to model the system such as, Genetic Algorithm [16] particle swarm optimization (PSO) [17], Bacteria Foraging Algorithm [18], Differential evolutionary (DE) algorithm [19] and Cuckoo Search Algorithm [20]. A number of research works considered parametric model structure because of its simplicity.…”

Section: Introductionmentioning

confidence: 99%

Intelligent modeling of double link flexible robotic manipulator using artificial neural network

Jamali¹,

Darus²,

Samin³

et al. 2018

J. vibroeng.

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The paper investigates the application of the Artificial Neural Network (ANN) in modeling of double-link flexible robotic manipulator (DLFRM). The system was categorized under multi-input multi-output. In this research, the dynamic models of DLFRM were separated into single-input single-output in the modeling stage. Thus, the characteristics of DLFRM were defined separately in each model and the coupling effect was assumed to be minimized. There are four discrete SISO model of double link flexible manipulator were developed from torque input to the hub angle and from torque input to the end point accelerations of each link. An experimental work was established to collect the input-output data pairs and used in developing the system model. Since the system is highly nonlinear, NARX model was chosen as the model structure because of its simplicity. The nonlinear characteristic of the system was estimated using the ANN whereby multi-layer perceptron (MLP) and ELMAN neural network (ENN) structure were utilized. The implementation of the ANN and its' effectiveness in developing the model of DLFRM was emphasized. The performance of the MLP was compared to ENN based on the validation of the mean-squared error (MSE) and correlation tests of the developed models. The results indicated that the identification of the DLFRM system using the MLP outperformed the ENN with lower mean squared prediction error and unbiased results for all the models. Thus, the MLP provides a good approximation of the DLFRM dynamic model compared to the ENN.

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“…Optimization approaches to formulating models of a single flexible link and a two dimensional flexible plate are respectively discussed by Alam et al [1] and Darus et al [8]. While these optimization approaches are shown to work well, they would still require experimental data to which the model can be fitted to.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control of Vibration in a Two Flexible Link Manipulator — Part 2

Elliott

Dubay

Mohany

et al. 2014

Journal of Low Frequency Noise, Vibration and Active Control

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A finite element based model predictive controller (FEMPC) is developed and practically implemented for attenuating in-plane vibration of a two flexible link planar manipulator. This FEMPC structure is based on that used in dynamic matrix control (DMC), with the exception that a finite element (FE) model replaces how the predictions are formulated. A linear FE model is developed for each individual link, which is used with the current measured strain and control actions, to predict the response of each link. These predictions are carried out at each time step to address the geometric non-linearities associated with the orientation of the second link and those associated with friction, backlash and compliance of the geared motors. Furthermore, the use of FE modelling enables the control structure to be formulated based on known properties of the system, eliminating the need for open loop testing. The resulting FEMPC scheme is shown to outperform DMC and is capable of providing substantial attenuation of vibration, reducing the mean amplitude of dominant vibration by 92.5% and 15.6%, for the first and second links, respectively. INTRODUCTIONWith lighter linkages and smaller spatial profiles, flexible link manipulators have advantages over their rigid link counterpart, when the payload being moved is relatively lightweight. These advantages include faster accelerations and more efficient operations since less energy is expended to move these lighter flexible linkages. However, the fundamental drawback of these manipulators is their susceptibility to deflection during motion, which leads to inaccurate positioning of the end effector due to the resulting vibration of the linkages. To address this issue active vibration control has been shown to be a potential solution, where actuators, such as piezoelectric plates, are used to counteract the vibration of these linkages, resulting in a more accurate positioning of the end effector.In part 1 of this work model predictive control (MPC) in the form of dynamic matrix control (DMC) was presented as viable control scheme for attenuating vibration of a planar two flexible link manipulator. While shown to work well with short prediction horizons, the selection of short prediction horizons in MPC tends to result in a less stable control as the scheme becomes more sensitive to noise. However, by increasing the prediction horizon the effectiveness of the controller to attenuate vibration is observed to decline. Since DMC control is ideally intended for first order systems, this behavior is viewed as a result of inadequate representation of the dynamics of the linkages when using the dynamic matrix to predict the response of the links. As such, this part shall incorporate an alternative means of modelling the vibration of the linkages into the DMC scheme, this alternative being finite element (FE).

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Dynamic Modelling of a Single-Link Flexible Manipulator System: A Particle Swarm Optimisation Approach

Cited by 17 publications

References 37 publications

Applications of computational intelligence to mechanical engineering

Applications of computational intelligence to mechanical engineering

Intelligent modeling of double link flexible robotic manipulator using artificial neural network

Model Predictive Control of Vibration in a Two Flexible Link Manipulator — Part 2

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