In recent times,wind energy receives maximum attention and has become a significant green energy source globally.The wind turbine(WT)entered into several domains such as power electronics that are employed to assist t...In recent times,wind energy receives maximum attention and has become a significant green energy source globally.The wind turbine(WT)entered into several domains such as power electronics that are employed to assist the connection process of a wind energy system and grid.The turbulent characteristics of wind profile along with uncertainty in the design of WT make it highly challenging for prolific power extraction.The pitch control angle is employed to effectively operate the WT at the above nominal wind speed.Besides,the pitch controller needs to be intelligent for the extraction of sustainable secure energy and keep WTs in a safe operating region.To achieve this,proportional–integral–derivative(PID)controllers are widely used and the choice of optimal parameters in the PID controllers needs to be properly selected.With this motivation,this paper designs an oppositional brain storm optimization(OBSO)based fractional order PID(FOPID)design for sustainable and secure energy in WT systems.The proposed model aims to effectually extract the maximum power point(MPPT)in the low range of weather conditions and save the WT in high wind regions by the use of pitch control.The OBSO algorithm is derived from the integration of oppositional based learning(OBL)concept with the traditional BSO algorithm in order to improve the convergence rate,which is then applied to effectively choose the parameters involved in the FOPID controller.The performance of the presented model is validated on the pitch control of a 5 MW WT and the results are examined under different dimensions.The simulation outcomes ensured the promising characteristics of the proposed model over the other methods.展开更多
The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action.But with the endorsement of renewable energy for harsh environmen...The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action.But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime concerns.These problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification error.Hence to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection methodology.Based on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance requirement.Further,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a fault.The results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.展开更多
This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion(AGDI)to track the position of a Linear Flexible Joint Cart(LFJC)system along with vibration suppression of the flexible joint...This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion(AGDI)to track the position of a Linear Flexible Joint Cart(LFJC)system along with vibration suppression of the flexible joint.The proposed AGDI control law will be comprised of two control elements.The baseline(continuous)control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control objectives.The control law is realized by inverting the prescribed dynamics using dynamically scaledMoore-Penrose generalized inversion.To boost the robust attributes against system nonlinearities,parametric uncertainties and external perturbations,a discontinuous control law will be augmented which is based on the concept of sliding mode principle.In discontinuous control law,the sliding mode gain is made adaptive in order to achieve improved tracking performance and chattering reduction.The closed-loop stability of resultant control law is established by introducing a positive define Lyapunov candidate function such that semi-global asymptotic attitude tracking of LFJC system is guaranteed.Rigorous computer simulations followed by experimental investigation will be performed on Quanser’s LFJC system to authenticate the feasibility of proposed control approach for its application to real world problems.展开更多
The Ball and beam system(BBS)is an attractive laboratory experimental tool because of its inherent nonlinear and open-loop unstable properties.Designing an effective ball and beam system controller is a real challenge...The Ball and beam system(BBS)is an attractive laboratory experimental tool because of its inherent nonlinear and open-loop unstable properties.Designing an effective ball and beam system controller is a real challenge for researchers and engineers.In this paper,the control design technique is investigated by using Intelligent Dynamic Inversion(IDI)method for this nonlinear and unstable system.The proposed control law is an enhanced version of conventional Dynamic Inversion control incorporating an intelligent control element in it.The Moore-PenroseGeneralized Inverse(MPGI)is used to invert the prescribed constraint dynamics to realize the baseline control law.A sliding mode-based intelligent control element is further augmented with the baseline control to enhance the robustness against uncertainties,nonlinearities,and external disturbances.The semi-global asymptotic stability of IDI control is guaranteed in the sense of Lyapunov.Numerical simulations and laboratory experiments are carried out on this ball and beam physical system to analyze the effectiveness of the controller.In addition to that,comparative analysis of RGDI control with classical Linear Quadratic Regulator and Fractional Order Controller are also presented on the experimental test bench.展开更多
This paper describes a system designed for linear servo cart systems that employs an integral-based Linear Active Disturbance Rejection Control(ILADRC)scheme to detect and respond to disturbances.The upgrade in this c...This paper describes a system designed for linear servo cart systems that employs an integral-based Linear Active Disturbance Rejection Control(ILADRC)scheme to detect and respond to disturbances.The upgrade in this control technique provides extensive immunity to uncertainties,attenuation,internal disturbances,and external sources of noise.The fundamental technology base of LADRC is Extended State Observer(ESO).LADRC,when combined with Integral action,becomes a hybrid control technique,namely ILADRC.Setpoint tracking is based on Bode’s Ideal Transfer Function(BITF)in this proposed ILADRC technique.This proves to be a very robust and appropriate pole placement scheme.The proposed LSC system has experimented with the hybrid ILADRC technique plotted the results.From the results,it is evident that the proposed ILADRC scheme enhances the robustness of the LSC system with remarkable disturbance rejection.Furthermore,the results of a linear quadratic regulator(LQR)and ILADRC schemes are comparatively analyzed.This analysis deduced the improved performance of ILADRC over the LQR control scheme.展开更多
Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Indu...Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.展开更多
Linear active disturbance rejection control(LADRC)is a powerful control structure thanks to its performance in uncertainties,internal and external disturbances estimation and cancelation.An extended state observer(ESO...Linear active disturbance rejection control(LADRC)is a powerful control structure thanks to its performance in uncertainties,internal and external disturbances estimation and cancelation.An extended state observer(ESO)based controller is the key to the LADRC method.In this article,the LADRC scheme combined with a fractional-order integral action(FOILADRC)is proposed to improve the robustness of the standard LADRC.Using the robust closed-loop Bode’s ideal transfer function(BITF),an appropriate pole placement method is proposed to design the set-point tracking controller of the FOI-LADRC scheme.Numerical simulations and experimental results on a cart-pendulum system will illustrate the effectiveness of the proposed FOI-LADRC scheme for the disturbance rejection,the set-point tracking and the improved robustness.To illustrate the LADRC control schemes and to verify the performance of the proposed FOI-LADRC,compared to the standard LADRC and IOI-LADRC structures,two tests will be carried out.First,simulation tests on an academic example will be presented to show the effect of the different parameters of the control law on the performance of the closed-loop system.Then,these three control structures are implemented on an experimental test bench,the cart-pendulum system,to show their efficiency and to show the superiority of the proposed method compared to the two other structures.展开更多
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...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.展开更多
Tracking load changes in a pressurized water reactor(PWR)with the help of an efficient core power control scheme in a nuclear power station is very important.The reason is that it is challenging to maintain a stable c...Tracking load changes in a pressurized water reactor(PWR)with the help of an efficient core power control scheme in a nuclear power station is very important.The reason is that it is challenging to maintain a stable core power according to the reference value within an acceptable tolerance for the safety of PWR.To overcome the uncertainties,a non-integer-based fractional order control method is demonstrated to control the core power of PWR.The available dynamic model of the reactor core is used in this analysis.Core power is controlled using a modified state feedback approach with a non-integer integral scheme through two different approximations,CRONE(Commande Robuste d’Ordre Non Entier,meaning Non-integer orderRobust Control)and FOMCON(non-integer order modeling and control).Simulation results are produced using MATLAB■program.Both non-integer results are compared with an integer order PI(Proportional Integral)algorithm to justify the effectiveness of the proposed scheme.Sate-spacemodel Core power control Non-integer control Pressurized water reactor PI controller CRONE FOMCON.展开更多
基金Deputyship for Research and Innovation,Ministry of Education in Saudi Arabia,project number(IFPRC-040-135-2020)。
文摘In recent times,wind energy receives maximum attention and has become a significant green energy source globally.The wind turbine(WT)entered into several domains such as power electronics that are employed to assist the connection process of a wind energy system and grid.The turbulent characteristics of wind profile along with uncertainty in the design of WT make it highly challenging for prolific power extraction.The pitch control angle is employed to effectively operate the WT at the above nominal wind speed.Besides,the pitch controller needs to be intelligent for the extraction of sustainable secure energy and keep WTs in a safe operating region.To achieve this,proportional–integral–derivative(PID)controllers are widely used and the choice of optimal parameters in the PID controllers needs to be properly selected.With this motivation,this paper designs an oppositional brain storm optimization(OBSO)based fractional order PID(FOPID)design for sustainable and secure energy in WT systems.The proposed model aims to effectually extract the maximum power point(MPPT)in the low range of weather conditions and save the WT in high wind regions by the use of pitch control.The OBSO algorithm is derived from the integration of oppositional based learning(OBL)concept with the traditional BSO algorithm in order to improve the convergence rate,which is then applied to effectively choose the parameters involved in the FOPID controller.The performance of the presented model is validated on the pitch control of a 5 MW WT and the results are examined under different dimensions.The simulation outcomes ensured the promising characteristics of the proposed model over the other methods.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number“IFPHI-022-135-2020”and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action.But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime concerns.These problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification error.Hence to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection methodology.Based on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance requirement.Further,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a fault.The results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.
基金This research work was funded by Institutional Fund Projects under Grant No.(IFPHI-106-135-2020).
文摘This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion(AGDI)to track the position of a Linear Flexible Joint Cart(LFJC)system along with vibration suppression of the flexible joint.The proposed AGDI control law will be comprised of two control elements.The baseline(continuous)control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control objectives.The control law is realized by inverting the prescribed dynamics using dynamically scaledMoore-Penrose generalized inversion.To boost the robust attributes against system nonlinearities,parametric uncertainties and external perturbations,a discontinuous control law will be augmented which is based on the concept of sliding mode principle.In discontinuous control law,the sliding mode gain is made adaptive in order to achieve improved tracking performance and chattering reduction.The closed-loop stability of resultant control law is established by introducing a positive define Lyapunov candidate function such that semi-global asymptotic attitude tracking of LFJC system is guaranteed.Rigorous computer simulations followed by experimental investigation will be performed on Quanser’s LFJC system to authenticate the feasibility of proposed control approach for its application to real world problems.
基金This research work was funded by Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia under Grant No.(IFPRC-023-135-2020).
文摘The Ball and beam system(BBS)is an attractive laboratory experimental tool because of its inherent nonlinear and open-loop unstable properties.Designing an effective ball and beam system controller is a real challenge for researchers and engineers.In this paper,the control design technique is investigated by using Intelligent Dynamic Inversion(IDI)method for this nonlinear and unstable system.The proposed control law is an enhanced version of conventional Dynamic Inversion control incorporating an intelligent control element in it.The Moore-PenroseGeneralized Inverse(MPGI)is used to invert the prescribed constraint dynamics to realize the baseline control law.A sliding mode-based intelligent control element is further augmented with the baseline control to enhance the robustness against uncertainties,nonlinearities,and external disturbances.The semi-global asymptotic stability of IDI control is guaranteed in the sense of Lyapunov.Numerical simulations and laboratory experiments are carried out on this ball and beam physical system to analyze the effectiveness of the controller.In addition to that,comparative analysis of RGDI control with classical Linear Quadratic Regulator and Fractional Order Controller are also presented on the experimental test bench.
基金This research work was funded by Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia under grant no(IFPRC-023-135-2020)。
文摘This paper describes a system designed for linear servo cart systems that employs an integral-based Linear Active Disturbance Rejection Control(ILADRC)scheme to detect and respond to disturbances.The upgrade in this control technique provides extensive immunity to uncertainties,attenuation,internal disturbances,and external sources of noise.The fundamental technology base of LADRC is Extended State Observer(ESO).LADRC,when combined with Integral action,becomes a hybrid control technique,namely ILADRC.Setpoint tracking is based on Bode’s Ideal Transfer Function(BITF)in this proposed ILADRC technique.This proves to be a very robust and appropriate pole placement scheme.The proposed LSC system has experimented with the hybrid ILADRC technique plotted the results.From the results,it is evident that the proposed ILADRC scheme enhances the robustness of the LSC system with remarkable disturbance rejection.Furthermore,the results of a linear quadratic regulator(LQR)and ILADRC schemes are comparatively analyzed.This analysis deduced the improved performance of ILADRC over the LQR control scheme.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia has funded this project under Grant No.(G:651-135-1443).
文摘Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.
基金This project was supported by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under grant No.(DF-474-135-1441).The authors,therefore,gratefully acknowledge DSR technical and financial support.
文摘Linear active disturbance rejection control(LADRC)is a powerful control structure thanks to its performance in uncertainties,internal and external disturbances estimation and cancelation.An extended state observer(ESO)based controller is the key to the LADRC method.In this article,the LADRC scheme combined with a fractional-order integral action(FOILADRC)is proposed to improve the robustness of the standard LADRC.Using the robust closed-loop Bode’s ideal transfer function(BITF),an appropriate pole placement method is proposed to design the set-point tracking controller of the FOI-LADRC scheme.Numerical simulations and experimental results on a cart-pendulum system will illustrate the effectiveness of the proposed FOI-LADRC scheme for the disturbance rejection,the set-point tracking and the improved robustness.To illustrate the LADRC control schemes and to verify the performance of the proposed FOI-LADRC,compared to the standard LADRC and IOI-LADRC structures,two tests will be carried out.First,simulation tests on an academic example will be presented to show the effect of the different parameters of the control law on the performance of the closed-loop system.Then,these three control structures are implemented on an experimental test bench,the cart-pendulum system,to show their efficiency and to show the superiority of the proposed method compared to the two other structures.
基金This research work was funded by Institutional Fund Projects under Grant No.(IFPRC-027-135-2020).
文摘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.
基金This project was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,Saudi Arabia under grant no.(KEP-Msc-36-135-38).
文摘Tracking load changes in a pressurized water reactor(PWR)with the help of an efficient core power control scheme in a nuclear power station is very important.The reason is that it is challenging to maintain a stable core power according to the reference value within an acceptable tolerance for the safety of PWR.To overcome the uncertainties,a non-integer-based fractional order control method is demonstrated to control the core power of PWR.The available dynamic model of the reactor core is used in this analysis.Core power is controlled using a modified state feedback approach with a non-integer integral scheme through two different approximations,CRONE(Commande Robuste d’Ordre Non Entier,meaning Non-integer orderRobust Control)and FOMCON(non-integer order modeling and control).Simulation results are produced using MATLAB■program.Both non-integer results are compared with an integer order PI(Proportional Integral)algorithm to justify the effectiveness of the proposed scheme.Sate-spacemodel Core power control Non-integer control Pressurized water reactor PI controller CRONE FOMCON.
