Fractional power rate reaching law for augmented sliding mode performance

Rohith, G

doi:10.1016/j.jfranklin.2020.11.018

Cited by 18 publications

(16 citation statements)

References 24 publications

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“…Let Q and R be processed noise covariance and measurement noise covariance respectively. The apriori error covariance P is given by equation (6).…”

Section: Kalman Filtermentioning

confidence: 99%

“…Jahed and Farrokhi 4 discussed the design of a fuzzy based robust control for a TRMS using angular speed given by tachometer in a simulation platform, and Tao et al 5 discussed the fuzzy sliding and integral sliding controller design for a TRMS. In Rohith, 6 a new control law was proposed for the design of sliding motor controllers, which mitigate the chattering problem with the gain variation and thereby guarantees faster system response and robustness. Design of an auto tuning based PID controller with fractional-order reference model approximation for a DC rotor in a TRMS model is discussed in Alagoz et al 7 The data regarding the process variable is derived from the angular position of pitch and yaw.…”

Section: Introductionmentioning

confidence: 99%

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Design of a soft sensing technique for measuring pitch and yaw angular positions for a Twin Rotor MIMO System

et al. 2021

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Background: This paper presents a soft sensor design technique for estimation of pitch and yaw angular positions of a Twin Rotor MIMO System (TRMS). The objective of the proposed work was to calculate the value of pitch and yaw angular positions using a stochastic estimation technique. Methods: Measurements from optical sensors were used to measure fan blade rotations per minute (RPM). The Kalman filter, which is a stochastic estimator, was used in the proposed system and its results were compared with those of the Luenberger observer and neural network. The Twin Rotor MIMO System is a nonlinear system with significant cross coupling between its rotors. Results: The estimators were designed for the decoupled system and were applied in real life to the coupled TRMS. The convergence of estimation to the actual values was checked on a practical setup. The Kalman filter estimators were evaluated for various inputs and disturbances, and the results were corroborated in real time. Conclusion: From the proposed work it was seen that the Kalman filter had at least Integral Absolute Error (IAE), Integral Square Error (ISE), Integral Time Absolute Error (ITAE) as compared to the neural network and the Luenberger based observer.

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“…Let Q and R be processed noise covariance and measurement noise covariance respectively. The apriori error covariance P is given by equation (6).…”

Section: Kalman Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of a soft sensing technique for measuring pitch and yaw angular positions for a Twin Rotor MIMO System

et al. 2021

View full text Add to dashboard Cite

show abstract

“…But this solution is not obvious in this case because of the non-integer order [12]. One other solution can be simply using the supposed 'augmented model' consistent with the fractional-order model [13][14][15][16][17][18]. The non-integer order in this solution is not calculated in agreement with the closed-loop (CL) reference model but chosen instead.…”

Section: Introductionmentioning

confidence: 99%

Fractional Order Linear Active Disturbance Rejection Control for Linear Flexible Joint System

Mehedi¹,

Mansouri²,

Al‐Saggaf³

et al. 2022

Computers, Materials &Amp; Continua

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A linear flexible joint system using fractional order linear active disturbance rejection control is studied in this paper. With this control scheme, the performance against disturbances, uncertainties, and attenuation is enhanced. Linear active disturbance rejection control (LADRC) is mainly based on an extended state observer (ESO) technology. A fractional integral (FOI) action is combined with the LADRC technique which proposes a hybrid control scheme like FO-LADRC. Incorporating this FOI action improves the robustness of the standard LADRC. The set-point tracking of the proposed FO-LADRC scheme is designed by Bode's ideal transfer function (BITF) based robust closed-loop concept, an appropriate pole placement method. The effectiveness of the proposed FO-LADRC scheme is illustrated through experimental results on the linear flexible joint system (LFJS). The results show the enhancement of the robustness with disturbance rejection. Furthermore, a comparative analysis is presented with the results obtained using the integer-order LADRC and FO-LADRC scheme.

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“…Jahed and Farrokhi 10 discussed the design of a fuzzy-based robust control for a TRMS using angular speed given by a tachometer in a simulation platform, and Tao et al 11 discussed the fuzzy sliding and integral sliding controller design for a TRMS. In Rohith, 12 a new control law was proposed for the design of sliding motor controllers, which mitigate the…”

Section: Introductionmentioning

confidence: 99%

“… 11 discussed the fuzzy sliding and integral sliding controller design for a TRMS. In Rohith, 12 a new control law was proposed for the design of sliding motor controllers, which mitigate the chattering problem with the gain variation and thereby guarantees faster system response and robustness. The design of an auto-tuning based PID controller with fractional-order reference model approximation for a DC rotor in a TRMS model is discussed in Alagoz et al .…”

Section: Introductionmentioning

confidence: 99%

Design of a soft sensing technique for measuring pitch and yaw angular positions for a Twin Rotor MIMO System

et al. 2021

View full text Add to dashboard Cite

Background: This paper presents a soft sensor design technique for the estimation of pitch and yaw angular positions of a Twin Rotor MIMO System (TRMS). The objective of the proposed work was to calculate the value of pitch and yaw angular positions using a stochastic estimation technique. Methods: Measurements from optical sensors were used to measure fan blade rotations per minute (RPM). The Kalman filter, which is a stochastic estimator, was used in the proposed system and its results were compared with those of the Luenberger observer and neural network. The Twin Rotor MIMO System is a nonlinear system with significant cross-coupling between its rotors. Results: The estimators were designed for the decoupled system and were applied in real life to the coupled TRMS. The convergence of estimation to the actual values was checked on a practical setup. The Kalman filter estimators were evaluated for various inputs and disturbances, and the results were corroborated in real-time. Conclusion: From the proposed work it was seen that the Kalman filter had at least Integral Absolute Error (IAE), Integral Square Error (ISE), Integral Time Absolute Error (ITAE) as compared to the neural network and the Luenberger based observer.

show abstract

Fractional power rate reaching law for augmented sliding mode performance

Cited by 18 publications

References 24 publications

Design of a soft sensing technique for measuring pitch and yaw angular positions for a Twin Rotor MIMO System

Design of a soft sensing technique for measuring pitch and yaw angular positions for a Twin Rotor MIMO System

Fractional Order Linear Active Disturbance Rejection Control for Linear Flexible Joint System

Design of a soft sensing technique for measuring pitch and yaw angular positions for a Twin Rotor MIMO System

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