2018
DOI: 10.1002/asjc.2009
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Torque tracking control of electric load simulator with active motion disturbance and nonlinearity based on T‐S fuzzy model

Abstract: This paper develops a high performance nonlinear control method for an electric load simulator (ELS). The tracking performance of the ELS is mainly affected by the actuator's active motion disturbance and friction nonlinearity. First, a nonlinear model of ELS is developed, and then the Takagi-Sugeno fuzzy model is used to represent the friction nonlinearity ofthe ELS. A state observer is constructed to estimate the speed of the load system. For converting the tracking control into a stabilization problem, a ne… Show more

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Cited by 9 publications
(10 citation statements)
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References 38 publications
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“…Moreover, a part of the applied torque to joints is due to the interaction of the robot with the environment, and consequently the speed of the torque estimation in faulty condition is important. Although, the neural networks (Shen et al, 2014) and linguistic fuzzy-based estimators (Li et al, 2018) are powerful in approximating the nonlinear functions, they have limited capabilities in dealing with the features caused by changing the environment such as discontinuities or sudden jumps (Dehghan et al, 2015). Regarding this drawback, a wavelet-based TSFN has been developed which had significant improvements in estimation accuracy and convergence speed in such situations.…”
Section: Controller Design and Stability Analysismentioning
confidence: 99%
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“…Moreover, a part of the applied torque to joints is due to the interaction of the robot with the environment, and consequently the speed of the torque estimation in faulty condition is important. Although, the neural networks (Shen et al, 2014) and linguistic fuzzy-based estimators (Li et al, 2018) are powerful in approximating the nonlinear functions, they have limited capabilities in dealing with the features caused by changing the environment such as discontinuities or sudden jumps (Dehghan et al, 2015). Regarding this drawback, a wavelet-based TSFN has been developed which had significant improvements in estimation accuracy and convergence speed in such situations.…”
Section: Controller Design and Stability Analysismentioning
confidence: 99%
“…The intelligent networks as universal approximators have been emerged as a promising setup for controlling systems with unknown nonlinearities (Farid & Bigdeli, 2012). In recent years, there has been a growing interest in using Takagi-Sugeno fuzzy network (TSFN) in the control community (Li et al, 2018). TSFN has the merits of simple learning and computation power of the neural networks, and the human-like reasoning of fuzzy theory.…”
Section: Introductionmentioning
confidence: 99%
“…In order to deal with the robust command of uncertain dynamical systems, fuzzy logic schemes have been introduced, incorporating the user experience into the control design, by means of linguistic variables and logic rules, [18][19][20][21]. Fuzzy logic controllers provide interesting properties, which are useful to the control of uncertain physical processes [22][23][24][25], compensating unmodelled dynamics, such as in the case of electrically driven mechanical systems [26,27].…”
Section: Introductionmentioning
confidence: 99%
“…However, fast trolley positioning and small payload swing are conflicting requirements mostly due to the high coupling between the payload swing and the trolley acceleration or deceleration. Such systems include but are not limited to: inverted pendulum, gymnast robot, and offshore crane, among others, are underactuated mechanical systems [1][2][3][4][5]. They have fewer control inputs than degrees of freedom, which makes their control significantly more challenging.…”
Section: Introductionmentioning
confidence: 99%
“…There are two issues that need to be solved: (1) For the control design convenience, several existing approaches were based on the linearized dynamic model or making approximation operations to the crane dynamic model when performing stability analysis. (2) The existing fuzzy control methods for the crane control were utilized to deal with the model non-linearity, however, external disturbances were not considered.…”
Section: Introductionmentioning
confidence: 99%