Pub Date : 2014-11-03DOI: 10.1080/21642583.2014.956842
M. Mansouri, H. Nounou, M. Nounou
In this paper, several techniques are addressed for both estimation and control to be integrated into a unified closed-loop or feedback control system that is applicable for a general family of nonlinear control structures. The estimation techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). Specifically, two comparative studies are performed. In the first comparative study, the state variables are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square errors with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of the number of estimated model parameters on the accuracy and convergence of these techniques is also assessed. The results of both comparative studies show that the UKF provides a higher accuracy than the EKF, due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the PF provides a significant improvement over the UKF and EKF and can still provide both convergence as well as accuracy-related advantages over other estimation methods. This is because the covariance is propagated through linearization of the underlying nonlinear model, when the state transition and observation models are highly nonlinear.
{"title":"Nonlinear control and estimation in induction machine using state estimation techniques","authors":"M. Mansouri, H. Nounou, M. Nounou","doi":"10.1080/21642583.2014.956842","DOIUrl":"https://doi.org/10.1080/21642583.2014.956842","url":null,"abstract":"In this paper, several techniques are addressed for both estimation and control to be integrated into a unified closed-loop or feedback control system that is applicable for a general family of nonlinear control structures. The estimation techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). Specifically, two comparative studies are performed. In the first comparative study, the state variables are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square errors with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of the number of estimated model parameters on the accuracy and convergence of these techniques is also assessed. The results of both comparative studies show that the UKF provides a higher accuracy than the EKF, due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the PF provides a significant improvement over the UKF and EKF and can still provide both convergence as well as accuracy-related advantages over other estimation methods. This is because the covariance is propagated through linearization of the underlying nonlinear model, when the state transition and observation models are highly nonlinear.","PeriodicalId":22127,"journal":{"name":"Systems Science & Control Engineering: An Open Access Journal","volume":"43 1","pages":"642 - 654"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74269751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-31DOI: 10.1080/21642583.2014.970731
X. Hong, Junbin Gao, Xinwei Jiang, C. Harris
A new class of parameter estimation algorithms is introduced for Gaussian process regression (GPR) models. It is shown that the integration of the GPR model with probability distance measures of (i) the integrated square error and (ii) Kullback–Leibler (K–L) divergence are analytically tractable. An efficient coordinate descent algorithm is proposed to iteratively estimate the kernel width using golden section search which includes a fast gradient descent algorithm as an inner loop to estimate the noise variance. Numerical examples are included to demonstrate the effectiveness of the new identification approaches.
{"title":"Estimation of Gaussian process regression model using probability distance measures","authors":"X. Hong, Junbin Gao, Xinwei Jiang, C. Harris","doi":"10.1080/21642583.2014.970731","DOIUrl":"https://doi.org/10.1080/21642583.2014.970731","url":null,"abstract":"A new class of parameter estimation algorithms is introduced for Gaussian process regression (GPR) models. It is shown that the integration of the GPR model with probability distance measures of (i) the integrated square error and (ii) Kullback–Leibler (K–L) divergence are analytically tractable. An efficient coordinate descent algorithm is proposed to iteratively estimate the kernel width using golden section search which includes a fast gradient descent algorithm as an inner loop to estimate the noise variance. Numerical examples are included to demonstrate the effectiveness of the new identification approaches.","PeriodicalId":22127,"journal":{"name":"Systems Science & Control Engineering: An Open Access Journal","volume":"34 1","pages":"655 - 663"},"PeriodicalIF":0.0,"publicationDate":"2014-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84105690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-31DOI: 10.1080/21642583.2014.913212
S. Patil, S. G. Joshi
This paper describes an experimental analysis of 2 degree-of-freedom (DOF) quarter-car passive suspension system and hydraulic active suspension system (QC-H-ASS) for ride comfort. The passive suspension system, which models a quarter-car suspension, consists of the sprung mass, unsprung mass, a suspension spring and damper and a tyre spring. A hydraulic actuator has been considered as one of the most viable choices for an active suspension system due to its high power-to-weight ratio and low cost. Thus this model is modified to a 2 DOF QC-H-ASS by placing a hydraulic actuator, with its attendant control instrumentation, in between sprung and unsprung masses. The results show considerable improvement in ride comfort over the conventional passive system. The details of the quarter-car model development with the test set-ups for the passive and hydraulic active suspension systems, suspension elements employed, experimental analysis and results are presented.
{"title":"Experimental analysis of 2 DOF quarter-car passive and hydraulic active suspension systems for ride comfort","authors":"S. Patil, S. G. Joshi","doi":"10.1080/21642583.2014.913212","DOIUrl":"https://doi.org/10.1080/21642583.2014.913212","url":null,"abstract":"This paper describes an experimental analysis of 2 degree-of-freedom (DOF) quarter-car passive suspension system and hydraulic active suspension system (QC-H-ASS) for ride comfort. The passive suspension system, which models a quarter-car suspension, consists of the sprung mass, unsprung mass, a suspension spring and damper and a tyre spring. A hydraulic actuator has been considered as one of the most viable choices for an active suspension system due to its high power-to-weight ratio and low cost. Thus this model is modified to a 2 DOF QC-H-ASS by placing a hydraulic actuator, with its attendant control instrumentation, in between sprung and unsprung masses. The results show considerable improvement in ride comfort over the conventional passive system. The details of the quarter-car model development with the test set-ups for the passive and hydraulic active suspension systems, suspension elements employed, experimental analysis and results are presented.","PeriodicalId":22127,"journal":{"name":"Systems Science & Control Engineering: An Open Access Journal","volume":"12 1","pages":"621 - 631"},"PeriodicalIF":0.0,"publicationDate":"2014-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87040572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-31DOI: 10.1080/21642583.2014.970733
B. Safarinejadian, Masihollah Gharibzadeh, M. Rakhshan
Electricity price forecasting in the electricity market is one of the important purposes for improving the performance of market players and increasing their profits in a competitive electricity market. Since the system load is one of the important factors affecting electricity price changes, a two-factorial model based on fuzzy time series is presented in this paper for electricity price forecasting using the electricity prices of the previous days and the system load. In the proposed method, price and system load time series are fuzzified by fuzzy sets created based on the fuzzy C-means clustering algorithm. After determining proposed model coefficients by the Teaching–Learning-Based Optimization algorithm, this model is used for forecasting the next day electricity price. The promising performance of the proposed model is examined using Australia and Singapore electricity markets data.
{"title":"An optimized model of electricity price forecasting in the electricity market based on fuzzy timeseries","authors":"B. Safarinejadian, Masihollah Gharibzadeh, M. Rakhshan","doi":"10.1080/21642583.2014.970733","DOIUrl":"https://doi.org/10.1080/21642583.2014.970733","url":null,"abstract":"Electricity price forecasting in the electricity market is one of the important purposes for improving the performance of market players and increasing their profits in a competitive electricity market. Since the system load is one of the important factors affecting electricity price changes, a two-factorial model based on fuzzy time series is presented in this paper for electricity price forecasting using the electricity prices of the previous days and the system load. In the proposed method, price and system load time series are fuzzified by fuzzy sets created based on the fuzzy C-means clustering algorithm. After determining proposed model coefficients by the Teaching–Learning-Based Optimization algorithm, this model is used for forecasting the next day electricity price. The promising performance of the proposed model is examined using Australia and Singapore electricity markets data.","PeriodicalId":22127,"journal":{"name":"Systems Science & Control Engineering: An Open Access Journal","volume":"12 1","pages":"677 - 683"},"PeriodicalIF":0.0,"publicationDate":"2014-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73842209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-31DOI: 10.1080/21642583.2014.970732
S. Shatnawi, M. Gaber, Ella Haig
Massive Open Online Course (MOOC) systems have recently received significant recognition and are increasingly attracting the attention of education providers and educational researchers. MOOCs are neither precisely defined nor sufficiently researched in terms of their properties and usage. The large number of students enrolled in these courses can lead to insufficient feedback given to the students. A stream of student posts to courses’ forums makes the problem even more difficult. Students’–MOOCs’ interactions can be exploited using text mining techniques to enhance learning and personalise the learners’ experience. In this paper, the open issues in MOOCs are outlined. Text mining and streaming text mining techniques which can contribute to the success of these systems are reviewed and some open issues in MOOC systems are addressed. Finally, our vision of an intelligent personalised MOOC feedback management system that we term iMOOC is outlined.
{"title":"Text stream mining for Massive Open Online Courses: review and perspectives","authors":"S. Shatnawi, M. Gaber, Ella Haig","doi":"10.1080/21642583.2014.970732","DOIUrl":"https://doi.org/10.1080/21642583.2014.970732","url":null,"abstract":"Massive Open Online Course (MOOC) systems have recently received significant recognition and are increasingly attracting the attention of education providers and educational researchers. MOOCs are neither precisely defined nor sufficiently researched in terms of their properties and usage. The large number of students enrolled in these courses can lead to insufficient feedback given to the students. A stream of student posts to courses’ forums makes the problem even more difficult. Students’–MOOCs’ interactions can be exploited using text mining techniques to enhance learning and personalise the learners’ experience. In this paper, the open issues in MOOCs are outlined. Text mining and streaming text mining techniques which can contribute to the success of these systems are reviewed and some open issues in MOOC systems are addressed. Finally, our vision of an intelligent personalised MOOC feedback management system that we term iMOOC is outlined.","PeriodicalId":22127,"journal":{"name":"Systems Science & Control Engineering: An Open Access Journal","volume":"3 1","pages":"664 - 676"},"PeriodicalIF":0.0,"publicationDate":"2014-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87362810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-09DOI: 10.1080/21642583.2014.956266
A. U. Jawadekar, Sudhir Paraskar, S. Jadhav, G. Dhole
Induction motors are used in industrial, commercial and residential applications because they have considerable merits over other types of electric motors. These motors are used in various operating stresses that give rise to faults. Most recurrent faults in induction motors are bearing faults, stator interturn faults and cracked rotor bars. Early detection of induction motor faults is crucial for their reliable and economical operation. This could be done by motor monitoring, incipient fault detection and diagnosis. In many situations, failure of critically loaded machine can shut down an entire industry process. This growing demand for high-quality and low-cost production has increased the need for automated manufacturing systems with effective monitoring and control capabilities. Condition monitoring and fault diagnosis of an induction motor are of great importance in the production line. It can reduce the cost of maintenance and risk of unexpected failures by allowing the early detection of catastrophic failures. This work documents experimental results for multiple fault detection in induction motors using signal-processing and artificial neural network-based approaches. Motor line currents recorded under various fault conditions were analyzed using continuous wavelet transform. A feedforward neural network was used for fault characterization based on fault features extracted using continuous wavelet transform.
{"title":"Artificial neural network-based induction motor fault classifier using continuous wavelet transform","authors":"A. U. Jawadekar, Sudhir Paraskar, S. Jadhav, G. Dhole","doi":"10.1080/21642583.2014.956266","DOIUrl":"https://doi.org/10.1080/21642583.2014.956266","url":null,"abstract":"Induction motors are used in industrial, commercial and residential applications because they have considerable merits over other types of electric motors. These motors are used in various operating stresses that give rise to faults. Most recurrent faults in induction motors are bearing faults, stator interturn faults and cracked rotor bars. Early detection of induction motor faults is crucial for their reliable and economical operation. This could be done by motor monitoring, incipient fault detection and diagnosis. In many situations, failure of critically loaded machine can shut down an entire industry process. This growing demand for high-quality and low-cost production has increased the need for automated manufacturing systems with effective monitoring and control capabilities. Condition monitoring and fault diagnosis of an induction motor are of great importance in the production line. It can reduce the cost of maintenance and risk of unexpected failures by allowing the early detection of catastrophic failures. This work documents experimental results for multiple fault detection in induction motors using signal-processing and artificial neural network-based approaches. Motor line currents recorded under various fault conditions were analyzed using continuous wavelet transform. A feedforward neural network was used for fault characterization based on fault features extracted using continuous wavelet transform.","PeriodicalId":22127,"journal":{"name":"Systems Science & Control Engineering: An Open Access Journal","volume":"80 1","pages":"684 - 690"},"PeriodicalIF":0.0,"publicationDate":"2014-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84117552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-09-30DOI: 10.1080/21642583.2014.956265
Khaled Fawagreh, M. Gaber, Eyad Elyan
Ensemble classification is a data mining approach that utilizes a number of classifiers that work together in order to identify the class label for unlabeled instances. Random forest (RF) is an ensemble classification approach that has proved its high accuracy and superiority. With one common goal in mind, RF has recently received considerable attention from the research community to further boost its performance. In this paper, we look at developments of RF from birth to present. The main aim is to describe the research done to date and also identify potential and future developments to RF. Our approach in this review paper is to take a historical view on the development of this notably successful classification technique. We start with developments that were found before Breiman's introduction of the technique in 2001, by which RF has borrowed some of its components. We then delve into dealing with the main technique proposed by Breiman. A number of developments to enhance the original technique are then presented and summarized. Successful applications that utilized RF are discussed, before a discussion of possible directions of research is finally given.
{"title":"Random forests: from early developments to recent advancements","authors":"Khaled Fawagreh, M. Gaber, Eyad Elyan","doi":"10.1080/21642583.2014.956265","DOIUrl":"https://doi.org/10.1080/21642583.2014.956265","url":null,"abstract":"Ensemble classification is a data mining approach that utilizes a number of classifiers that work together in order to identify the class label for unlabeled instances. Random forest (RF) is an ensemble classification approach that has proved its high accuracy and superiority. With one common goal in mind, RF has recently received considerable attention from the research community to further boost its performance. In this paper, we look at developments of RF from birth to present. The main aim is to describe the research done to date and also identify potential and future developments to RF. Our approach in this review paper is to take a historical view on the development of this notably successful classification technique. We start with developments that were found before Breiman's introduction of the technique in 2001, by which RF has borrowed some of its components. We then delve into dealing with the main technique proposed by Breiman. A number of developments to enhance the original technique are then presented and summarized. Successful applications that utilized RF are discussed, before a discussion of possible directions of research is finally given.","PeriodicalId":22127,"journal":{"name":"Systems Science & Control Engineering: An Open Access Journal","volume":"12 1","pages":"602 - 609"},"PeriodicalIF":0.0,"publicationDate":"2014-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74788861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-09-18DOI: 10.1080/21642583.2014.956267
D. Karanjkar, S. Chatterji, Amod Kumar, S. Shimi
A novel fuzzy adaptive proportional-integral-derivative (PID) control strategy with online set-point tracking is presented for maximum power point tracking (MPPT) in solar photovoltaic (PV) system in this paper. The range of the membership functions of the fuzzy logic for online PID parameter tuner has been optimized using the relay feedback tuning method. The proposed MPPT controller has been designed with online set-point adjustment approach using current, radiation and temperature sensors. Real-time simulations have been carried out on MATLAB™/dSPACE™ ds1104 Research and Development control board platform for solar PV system with synchronous buck converter and resistive load. Performance of the proposed techniques has been compared with main existing MPPT techniques viz. perturb and observe, incremental conductance, fuzzy logic, neural network and adaptive neuro-fuzzy inference system-based methods of MPPT. Performance of various techniques has been compared based on tracking efficiency, steady state and dynamic behaviour. Experimental results showed the superiority of the proposed method for tracking maximum power point under rapidly varying solar radiations.
{"title":"Fuzzy adaptive proportional-integral-derivative controller with dynamic set-point adjustment for maximum power point tracking in solar photovoltaic system","authors":"D. Karanjkar, S. Chatterji, Amod Kumar, S. Shimi","doi":"10.1080/21642583.2014.956267","DOIUrl":"https://doi.org/10.1080/21642583.2014.956267","url":null,"abstract":"A novel fuzzy adaptive proportional-integral-derivative (PID) control strategy with online set-point tracking is presented for maximum power point tracking (MPPT) in solar photovoltaic (PV) system in this paper. The range of the membership functions of the fuzzy logic for online PID parameter tuner has been optimized using the relay feedback tuning method. The proposed MPPT controller has been designed with online set-point adjustment approach using current, radiation and temperature sensors. Real-time simulations have been carried out on MATLAB™/dSPACE™ ds1104 Research and Development control board platform for solar PV system with synchronous buck converter and resistive load. Performance of the proposed techniques has been compared with main existing MPPT techniques viz. perturb and observe, incremental conductance, fuzzy logic, neural network and adaptive neuro-fuzzy inference system-based methods of MPPT. Performance of various techniques has been compared based on tracking efficiency, steady state and dynamic behaviour. Experimental results showed the superiority of the proposed method for tracking maximum power point under rapidly varying solar radiations.","PeriodicalId":22127,"journal":{"name":"Systems Science & Control Engineering: An Open Access Journal","volume":"46 1","pages":"562 - 582"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74438299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-09-15DOI: 10.1080/21642583.2014.956268
T. Stockley, K. Thanapalan, M. Bowkett, J. Williams
This paper describes an open circuit voltage (OCV) prediction technique for lithium cells. The work contains an investigation to examine the charge and mixed state relaxation voltage curves, to analyse the potential for the OCV prediction technique in a practical system. The underlying principal of the technique described in this paper employs a simple equation paired with a polynomial to predict the equilibrated cell voltage after a small rest period. The polynomial coefficients are devised by the use of curve fitting and system identification techniques. The practical work detailed in this paper was conducted at the Centre for Automotive and Power System Engineering (CAPSE) battery laboratories at the University of South Wales. The results indicate that the proposed OCV prediction technique is highly effective and may be implemented with a simple battery management system.
{"title":"Design and implementation of an open circuit voltage prediction mechanism for lithium-ion battery systems","authors":"T. Stockley, K. Thanapalan, M. Bowkett, J. Williams","doi":"10.1080/21642583.2014.956268","DOIUrl":"https://doi.org/10.1080/21642583.2014.956268","url":null,"abstract":"This paper describes an open circuit voltage (OCV) prediction technique for lithium cells. The work contains an investigation to examine the charge and mixed state relaxation voltage curves, to analyse the potential for the OCV prediction technique in a practical system. The underlying principal of the technique described in this paper employs a simple equation paired with a polynomial to predict the equilibrated cell voltage after a small rest period. The polynomial coefficients are devised by the use of curve fitting and system identification techniques. The practical work detailed in this paper was conducted at the Centre for Automotive and Power System Engineering (CAPSE) battery laboratories at the University of South Wales. The results indicate that the proposed OCV prediction technique is highly effective and may be implemented with a simple battery management system.","PeriodicalId":22127,"journal":{"name":"Systems Science & Control Engineering: An Open Access Journal","volume":"27 1","pages":"707 - 717"},"PeriodicalIF":0.0,"publicationDate":"2014-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83622642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-08-26DOI: 10.1080/21642583.2014.956841
Yi Cao, Wen‐Hua Chen
Satellite control using magneto-torquers represents a control challenge combined with strong nonlinearity, variable dynamics and partial controllability. An automatic differentiation-based nonlinear model predictive control (NMPC) algorithm is developed in this work to tackle these issues. Based on the previously developed formulation of NMPC, a novel variable sampling-time scheme is proposed to provide a better trade-off between transient control performance and closed-loop stability. More specifically, a small sampling time is adopted to improve the response speed when the satellite is far away from the desired position, and a large sampling time is employed for the closed-loop stability when the satellite is around its equilibrium position. This scheme also significantly reduces the online computational burden associated with fixed sampling-time NMPC where a large prediction horizon has to be adopted in order to the ensure closed-loop stability. The proposed approach is demonstrated through nonlinear simulation of a specific satellite case with satisfactory results obtained.
{"title":"Variable sampling-time nonlinear model predictive control of satellites using magneto-torquers","authors":"Yi Cao, Wen‐Hua Chen","doi":"10.1080/21642583.2014.956841","DOIUrl":"https://doi.org/10.1080/21642583.2014.956841","url":null,"abstract":"Satellite control using magneto-torquers represents a control challenge combined with strong nonlinearity, variable dynamics and partial controllability. An automatic differentiation-based nonlinear model predictive control (NMPC) algorithm is developed in this work to tackle these issues. Based on the previously developed formulation of NMPC, a novel variable sampling-time scheme is proposed to provide a better trade-off between transient control performance and closed-loop stability. More specifically, a small sampling time is adopted to improve the response speed when the satellite is far away from the desired position, and a large sampling time is employed for the closed-loop stability when the satellite is around its equilibrium position. This scheme also significantly reduces the online computational burden associated with fixed sampling-time NMPC where a large prediction horizon has to be adopted in order to the ensure closed-loop stability. The proposed approach is demonstrated through nonlinear simulation of a specific satellite case with satisfactory results obtained.","PeriodicalId":22127,"journal":{"name":"Systems Science & Control Engineering: An Open Access Journal","volume":"57 1","pages":"593 - 601"},"PeriodicalIF":0.0,"publicationDate":"2014-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91201242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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