System identification and control of robot manipulator based on fuzzy adaptive differential evolution algorithm

Al-Dabbagh, Rawaa Dawoud; Kinsheel, Azeddien; Mekhilef, Saad; Baba, Mohd Sapiyan; Shamshirband, Shahaboddin

doi:10.1016/j.advengsoft.2014.08.009

Cited by 40 publications

(8 citation statements)

References 18 publications

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“…In [18] the authors use system identification procedures in order to estimate inertial and flexibility parameters of an industrial robot. The barycentric parameters of a robot are estimated through computational intelligence techniques in [19]. In [20] the importance of experimental data driven modeling and parameter estimation in robotics are highlighted.…”

Section: Introductionmentioning

confidence: 99%

Modeling of a 2-DOF Piezoelectric Micromanipulator at High Frequency Rates through Nonlinear Black-box System Identification

Ayala

Rakotondrahe

Coelho

2018

2018 Annual American Control Conference (ACC)

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In the present paper we proceed the data-driven modeling of a two degrees of freedom (2-DOF) piezoelectric micromanipulator through models with the Nonlinear Au-toRegressive with eXogenous inputs (NARX) structure and real acquired data. We show the results when the system is excited at high frequencies, aiming towards rapid and precise micropositioning. The order of NARX the models are increased until they satisfy the statistical tests based on higher-order correlations and the multiple correlation coefficients, which are close to unity for both measured outputs. The results herein presented encourage the use of data-driven methods for modeling piezoelectric micromanipulators.

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Section: Introductionmentioning

confidence: 99%

Modeling of a 2-DOF Piezoelectric Micromanipulator at High Frequency Rates through Nonlinear Black-box System Identification

Ayala

Rakotondrahe

Coelho

2018

2018 Annual American Control Conference (ACC)

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“…Jafari, Safavi & Fadaei (2007) use a GA to minimise manufacturing costs of 3-DOF serial manipulator at the design stage, rather than estimating the unknown physical parameters of an existing manipulator as in the present article. Al-Dabbagh et al (2014) use GA optimisation to estimate the friction parameters of a single link 4-DOF surgery robot, while Bingül & Karahan (2011) use PSO to estimate the dynamical model parameters of the first three links of a Staubli RX-60 Robot. The latter two articles both make the assumption that the dynamic model of the robot is linear in the parameters.…”

Section: Introductionmentioning

confidence: 99%

Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

Montazeri

West

Monk

et al. 2016

International Journal of Control

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This article concerns the problem of dynamic modeling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model orientated research using the same machine, the article develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimize the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivization of an output error single performance index. The developed algorithm utilises a multi-objective Genetic Algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of 'true' parameters) and experimental data. Both simulation and experimental results show that multi-objectivization has improved convergence of the estimated parameters compared to the single objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem.

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“…On the other hand, the design of the SMC scheme requires the knowledge of uncertainties and external disturbances bound, which might be unavailable in real time. The adaptive concept provides an effective method to deal with these unknown external disturbances and uncertainties by estimating the upper bound of them [4]. Adaptive NTSMC (ANTSMC) method has been proposed by integration of the concept of adaptive control method and NTSMC method.…”

Section: Introductionmentioning

confidence: 99%

Two Novel Approaches of NTSMC and ANTSMC Synchronization for Smart Grid Chaotic Systems

Abadi

Hosseinabadi

Mekhilef

2018

Technol Econ Smart Grids Sustain Energy

Self Cite

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The presence of uncertainties and external disturbances is one of the unavoidable problems with various practical systems which might be unavailable in real-time. Sliding Mode Control (SMC) is one of the effective robust control methods to deal with these uncertainties and external disturbances. In this paper, two novel controllers are designed by using Nonsingular Terminal SMC (NTSMC) and Adaptive Nonsingular Terminal SMC (ANTSMC) methods for synchronization of dual smart grid chaotic systems with various uncertainties and external disturbances. Indeed, both adaptive and non-adaptive controllers based on NTSMC are proposed to provide two alternatives which can adjust by changing operating conditions and dynamics. The concept of SMC method guarantees controller robustness against various uncertainties and external disturbances. Elimination of the undesirable chattering phenomenon is addressed in this study which is one of the common deficiencies with conventional SMC method. Additionally, finite time concept is used to speed up the convergence rate. Finite time stability proof is performed by using Lyapunov stability theory. The numerical simulation is carried out in Simulink/MATLAB to reveal the validity of the proposed controllers for the smart grid chaotic system. A comprehensive comparison is made by performing simulation for the Fractional Order Adaptive Sliding Mode Control (FOASMC) controller and defining three performance criteria, among the proposed controllers in this study and FOASMC controller.

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System identification and control of robot manipulator based on fuzzy adaptive differential evolution algorithm

Cited by 40 publications

References 18 publications

Modeling of a 2-DOF Piezoelectric Micromanipulator at High Frequency Rates through Nonlinear Black-box System Identification

Modeling of a 2-DOF Piezoelectric Micromanipulator at High Frequency Rates through Nonlinear Black-box System Identification

Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

Two Novel Approaches of NTSMC and ANTSMC Synchronization for Smart Grid Chaotic Systems

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