Pub Date : 2001-09-05DOI: 10.1109/ISIC.2001.971487
G. Yen, Liang-Wei Ho
Much research attention has been done on fault detection and diagnosis, but little on "general" failure accommodation. Due to the inherent complexity of nonlinear systems, most model-based analytical redundancy fault diagnosis and accommodation studies deal with linear systems with simple faults. In this paper, online fault accommodation control under catastrophic system failure is investigated. The main interest is in unanticipated component failures. Through discrete-time Lyapunov stability theory, necessary and sufficient conditions for online stability and performance under failures are derived and a systematic procedure and technique for proper fault accommodation under the unanticipated failures are developed. A complete architecture of fault diagnosis and accommodation has also been presented by incorporating the developed intelligent fault tolerant control framework with a cost-effective fault detection scheme and a multiple-model based failure diagnosis process to efficiently handle the false alarms and the accommodation of both the anticipated and unanticipated failures in online situations.
{"title":"On-line multiple-model based fault diagnosis and accommodation","authors":"G. Yen, Liang-Wei Ho","doi":"10.1109/ISIC.2001.971487","DOIUrl":"https://doi.org/10.1109/ISIC.2001.971487","url":null,"abstract":"Much research attention has been done on fault detection and diagnosis, but little on \"general\" failure accommodation. Due to the inherent complexity of nonlinear systems, most model-based analytical redundancy fault diagnosis and accommodation studies deal with linear systems with simple faults. In this paper, online fault accommodation control under catastrophic system failure is investigated. The main interest is in unanticipated component failures. Through discrete-time Lyapunov stability theory, necessary and sufficient conditions for online stability and performance under failures are derived and a systematic procedure and technique for proper fault accommodation under the unanticipated failures are developed. A complete architecture of fault diagnosis and accommodation has also been presented by incorporating the developed intelligent fault tolerant control framework with a cost-effective fault detection scheme and a multiple-model based failure diagnosis process to efficiently handle the false alarms and the accommodation of both the anticipated and unanticipated failures in online situations.","PeriodicalId":367430,"journal":{"name":"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)","volume":"81 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131333965","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 : 2001-09-05DOI: 10.1109/ISIC.2001.971483
P. Peng, Youping Zhang
This paper presents a faster, robust learning algorithm for a neural network controller design. The learning scheme is regarded as finding the optimal weights via the proposed minimum seeking scheme. The only information required is the system output measurement.
{"title":"Minimum seeking neural network for direct feedback control","authors":"P. Peng, Youping Zhang","doi":"10.1109/ISIC.2001.971483","DOIUrl":"https://doi.org/10.1109/ISIC.2001.971483","url":null,"abstract":"This paper presents a faster, robust learning algorithm for a neural network controller design. The learning scheme is regarded as finding the optimal weights via the proposed minimum seeking scheme. The only information required is the system output measurement.","PeriodicalId":367430,"journal":{"name":"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122613818","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 : 2001-09-05DOI: 10.1109/ISIC.2001.971505
G. Loreto, Wen Yu, R. Garrido
We propose a stable 2D visual servoing algorithm for planar robot manipulators. We assume that gravity and friction are unknown and that there exists modeling errors in the vision system. By using a radial basis function neural network, it is shown that these uncertainties can be compensated. We prove that without or with unmodeled dynamics, the 2D visual servoing with neural networks compensation is Lyapunov stable.
{"title":"Stable visual servoing with neural network compensation","authors":"G. Loreto, Wen Yu, R. Garrido","doi":"10.1109/ISIC.2001.971505","DOIUrl":"https://doi.org/10.1109/ISIC.2001.971505","url":null,"abstract":"We propose a stable 2D visual servoing algorithm for planar robot manipulators. We assume that gravity and friction are unknown and that there exists modeling errors in the vision system. By using a radial basis function neural network, it is shown that these uncertainties can be compensated. We prove that without or with unmodeled dynamics, the 2D visual servoing with neural networks compensation is Lyapunov stable.","PeriodicalId":367430,"journal":{"name":"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125072150","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 : 2001-09-05DOI: 10.1109/ISIC.2001.971493
G. Galan, S. Jagannathan
MAR'S greenhouse operation requires robot arms that are capable of manipulating objects such as plant trays, fruits, vegetables and so on. Grasping and manipulation of objects have been a challenging task for robots. It is important that the manipulator performs these tasks accurately and faster with out damaging the object. The complex grasping task can be defined as object contact control and manipulation subtasks. In this paper, object contact subtask is defined in terms of following a trajectory accurately so that the object to be grasped is in contact with the gripper. The proposed controller scheme consists of a feedforward action generating neural network (NN) that compensates for the nonlinear gripper and object contact dynamics. The learning of this NN is performed online based on a critic signal so that a 3-finger gripper tracks a predefined desired trajectory, which is specified in terms of a desired position and velocity for object contact control. Novel weight tuning updates are derived for the action generating NN and a Lyapunov-based stability analysis is presented. Simulation results are shown for a 3-finger gripper making contact with an object.
{"title":"Adaptive critic-based neural network object contact controller for a three-finger gripper","authors":"G. Galan, S. Jagannathan","doi":"10.1109/ISIC.2001.971493","DOIUrl":"https://doi.org/10.1109/ISIC.2001.971493","url":null,"abstract":"MAR'S greenhouse operation requires robot arms that are capable of manipulating objects such as plant trays, fruits, vegetables and so on. Grasping and manipulation of objects have been a challenging task for robots. It is important that the manipulator performs these tasks accurately and faster with out damaging the object. The complex grasping task can be defined as object contact control and manipulation subtasks. In this paper, object contact subtask is defined in terms of following a trajectory accurately so that the object to be grasped is in contact with the gripper. The proposed controller scheme consists of a feedforward action generating neural network (NN) that compensates for the nonlinear gripper and object contact dynamics. The learning of this NN is performed online based on a critic signal so that a 3-finger gripper tracks a predefined desired trajectory, which is specified in terms of a desired position and velocity for object contact control. Novel weight tuning updates are derived for the action generating NN and a Lyapunov-based stability analysis is presented. Simulation results are shown for a 3-finger gripper making contact with an object.","PeriodicalId":367430,"journal":{"name":"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122537240","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 : 2001-09-05DOI: 10.1109/ISIC.2001.971482
K. Moore
In this note we make an observation about the equivalence between the necessary and sufficient condition for convergence and the sufficient condition for monotonic convergence in discrete-time, P-type iterative learning control. Specifically, requirements on the plant are given so that convergence of the learning algorithm ensures monotonic convergence. In particular, for the case where one minus the learning gain times the first Markov parameter is positive, but less than one, it is shown that if the first non-zero Markov parameter of the system has a larger magnitude than the sum of the magnitudes of the next N-1 Markov parameters, then convergence of the learning control algorithm implies monotonic convergence, independent of the learning gain. For the case where one minus the learning gain times the first Markov parameter is negative, but greater than negative one, a condition depending on the learning gain is derived whereby learning convergences also implies monotonic convergence.
{"title":"An observation about monotonic convergence in discrete-time, P-type iterative learning control","authors":"K. Moore","doi":"10.1109/ISIC.2001.971482","DOIUrl":"https://doi.org/10.1109/ISIC.2001.971482","url":null,"abstract":"In this note we make an observation about the equivalence between the necessary and sufficient condition for convergence and the sufficient condition for monotonic convergence in discrete-time, P-type iterative learning control. Specifically, requirements on the plant are given so that convergence of the learning algorithm ensures monotonic convergence. In particular, for the case where one minus the learning gain times the first Markov parameter is positive, but less than one, it is shown that if the first non-zero Markov parameter of the system has a larger magnitude than the sum of the magnitudes of the next N-1 Markov parameters, then convergence of the learning control algorithm implies monotonic convergence, independent of the learning gain. For the case where one minus the learning gain times the first Markov parameter is negative, but greater than negative one, a condition depending on the learning gain is derived whereby learning convergences also implies monotonic convergence.","PeriodicalId":367430,"journal":{"name":"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132513293","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 : 2001-09-05DOI: 10.1109/ISIC.2001.971514
E. Sánchez, S. Čelikovský, J. González, E. Ramirez
The paper presents the application of fuzzy control and nonlinear estimation to wastewater treatment plants. This biological process is highly nonlinear and its control is a challenging task due to unmeasured variables and input disturbances. Fuzzy control is quite efficient to reduce the effect of unmeasured disturbances, but the used structure requires the measurement of all the state variables. In order to estimate the unmeasured ones, a nonlinear estimator is proposed. The final control structure is composed by the nonlinear estimator and the fuzzy control. The applicability of the proposed approach is validated via simulations.
{"title":"Wastewater treatment plant control by combining fuzzy logic and nonlinear estimation","authors":"E. Sánchez, S. Čelikovský, J. González, E. Ramirez","doi":"10.1109/ISIC.2001.971514","DOIUrl":"https://doi.org/10.1109/ISIC.2001.971514","url":null,"abstract":"The paper presents the application of fuzzy control and nonlinear estimation to wastewater treatment plants. This biological process is highly nonlinear and its control is a challenging task due to unmeasured variables and input disturbances. Fuzzy control is quite efficient to reduce the effect of unmeasured disturbances, but the used structure requires the measurement of all the state variables. In order to estimate the unmeasured ones, a nonlinear estimator is proposed. The final control structure is composed by the nonlinear estimator and the fuzzy control. The applicability of the proposed approach is validated via simulations.","PeriodicalId":367430,"journal":{"name":"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116655825","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 : 2001-09-05DOI: 10.1109/ISIC.2001.971539
E. M. Hugo, J. du Plessis
An automated, multi-mode, fuzzy logic controller design method is presented. The method uses the sum of weights method to design consequences of an initial control rule base. The performance of the multi-mode controller is then improved using a model reference adaptive control scheme. The performance of the multi-mode fuzzy logic controller is compared to a self-learning, single-mode fuzzy logic controller using computer simulations and nonlinear plants.
{"title":"Automated multi-mode fuzzy logic controller design","authors":"E. M. Hugo, J. du Plessis","doi":"10.1109/ISIC.2001.971539","DOIUrl":"https://doi.org/10.1109/ISIC.2001.971539","url":null,"abstract":"An automated, multi-mode, fuzzy logic controller design method is presented. The method uses the sum of weights method to design consequences of an initial control rule base. The performance of the multi-mode controller is then improved using a model reference adaptive control scheme. The performance of the multi-mode fuzzy logic controller is compared to a self-learning, single-mode fuzzy logic controller using computer simulations and nonlinear plants.","PeriodicalId":367430,"journal":{"name":"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126464688","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 : 2001-09-01DOI: 10.1109/ISIC.2001.971525
C. Cox, I. Fletcher, A. Adgar
Compared to other process industries, the technology employed by the water industry is of a relatively low level. In general, however, methods of process regulation are far from ideal, leading to inefficient plant operation, occurrence of unnecessary costs and in some cases low water quality. Improvements in control and supervision methods have been recognised as one means of achieving higher water quality and efficiency objectives in the potable water industry. Attempts to improve the performance of water treatment works through the application of improved control and measurement have had variable success. The most quoted reason for this is that the individual dynamic operations defining the treatment cycle are complex, highly non-linear and poorly understood. These problems are compounded by the use of faulty or badly maintained sensors. Because of their ability to capture non-linear information very efficiently, artificial neural networks (ANNs) have found great popularity amongst the control community and other disciplines. The paper discusses an application of ANNs at surface water treatment works. The study is used to describe how the introduction of ANNs has resulted in more reliable system measurement and consequently improved coagulation control.
{"title":"ANN-based sensing and control developments in the water industry: a decade of innovation","authors":"C. Cox, I. Fletcher, A. Adgar","doi":"10.1109/ISIC.2001.971525","DOIUrl":"https://doi.org/10.1109/ISIC.2001.971525","url":null,"abstract":"Compared to other process industries, the technology employed by the water industry is of a relatively low level. In general, however, methods of process regulation are far from ideal, leading to inefficient plant operation, occurrence of unnecessary costs and in some cases low water quality. Improvements in control and supervision methods have been recognised as one means of achieving higher water quality and efficiency objectives in the potable water industry. Attempts to improve the performance of water treatment works through the application of improved control and measurement have had variable success. The most quoted reason for this is that the individual dynamic operations defining the treatment cycle are complex, highly non-linear and poorly understood. These problems are compounded by the use of faulty or badly maintained sensors. Because of their ability to capture non-linear information very efficiently, artificial neural networks (ANNs) have found great popularity amongst the control community and other disciplines. The paper discusses an application of ANNs at surface water treatment works. The study is used to describe how the introduction of ANNs has resulted in more reliable system measurement and consequently improved coagulation control.","PeriodicalId":367430,"journal":{"name":"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132820680","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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