Pub Date : 2009-02-01DOI: 10.1109/TSMCB.2008.2002853
Zhijun Li, Pey Yuen Tao, Shuzhi Sam Ge, Martin Adams, Wijerupage Sardha Wijesoma
In this paper, coupled dynamics are presented for two cooperating mobile robotic manipulators manipulating an object with relative motion in the presence of uncertainties and external disturbances. Centralized robust adaptive controls are introduced to guarantee the motion, and force trajectories of the constrained object converge to the desired manifolds with prescribed performance. The stability of the closed-loop system and the boundedness of tracking errors are proved using Lyapunov stability synthesis. The tracking of the constraint trajectory/force up to an ultimately bounded error is achieved. The proposed adaptive controls are robust against relative motion disturbances and parametric uncertainties and are validated by simulation studies.
{"title":"Robust adaptive control of cooperating mobile manipulators with relative motion.","authors":"Zhijun Li, Pey Yuen Tao, Shuzhi Sam Ge, Martin Adams, Wijerupage Sardha Wijesoma","doi":"10.1109/TSMCB.2008.2002853","DOIUrl":"https://doi.org/10.1109/TSMCB.2008.2002853","url":null,"abstract":"<p><p>In this paper, coupled dynamics are presented for two cooperating mobile robotic manipulators manipulating an object with relative motion in the presence of uncertainties and external disturbances. Centralized robust adaptive controls are introduced to guarantee the motion, and force trajectories of the constrained object converge to the desired manifolds with prescribed performance. The stability of the closed-loop system and the boundedness of tracking errors are proved using Lyapunov stability synthesis. The tracking of the constraint trajectory/force up to an ultimately bounded error is achieved. The proposed adaptive controls are robust against relative motion disturbances and parametric uncertainties and are validated by simulation studies.</p>","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":" ","pages":"103-16"},"PeriodicalIF":0.0,"publicationDate":"2009-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2008.2002853","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39998615","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 : 2009-02-01DOI: 10.1109/TSMCB.2008.2005527
Debashis Sen, Sankar K Pal
Quantifying ambiguities in images using fuzzy set theory has been of utmost interest to researchers in the field of image processing. In this paper, we present the use of rough set theory and its certain generalizations for quantifying ambiguities in images and compare it to the use of fuzzy set theory. We propose classes of entropy measures based on rough set theory and its certain generalizations, and perform rigorous theoretical analysis to provide some properties which they satisfy. Grayness and spatial ambiguities in images are then quantified using the proposed entropy measures. We demonstrate the utility and effectiveness of the proposed entropy measures by considering some elementary image processing applications. We also propose a new measure called average image ambiguity in this context.
{"title":"Generalized rough sets, entropy, and image ambiguity measures.","authors":"Debashis Sen, Sankar K Pal","doi":"10.1109/TSMCB.2008.2005527","DOIUrl":"https://doi.org/10.1109/TSMCB.2008.2005527","url":null,"abstract":"<p><p>Quantifying ambiguities in images using fuzzy set theory has been of utmost interest to researchers in the field of image processing. In this paper, we present the use of rough set theory and its certain generalizations for quantifying ambiguities in images and compare it to the use of fuzzy set theory. We propose classes of entropy measures based on rough set theory and its certain generalizations, and perform rigorous theoretical analysis to provide some properties which they satisfy. Grayness and spatial ambiguities in images are then quantified using the proposed entropy measures. We demonstrate the utility and effectiveness of the proposed entropy measures by considering some elementary image processing applications. We also propose a new measure called average image ambiguity in this context.</p>","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":" ","pages":"117-28"},"PeriodicalIF":0.0,"publicationDate":"2009-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2008.2005527","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39998616","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 : 2009-02-01DOI: 10.1109/TSMCB.2008.2002851
Marvin K Bugeja, Simon G Fabri, Liberato Camilleri
This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.
{"title":"Dual adaptive dynamic control of mobile robots using neural networks.","authors":"Marvin K Bugeja, Simon G Fabri, Liberato Camilleri","doi":"10.1109/TSMCB.2008.2002851","DOIUrl":"https://doi.org/10.1109/TSMCB.2008.2002851","url":null,"abstract":"<p><p>This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.</p>","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":" ","pages":"129-41"},"PeriodicalIF":0.0,"publicationDate":"2009-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2008.2002851","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39997934","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 : 2009-02-01DOI: 10.1109/TSMCB.2008.2002854
Yeong-Chan Chang
This paper addresses the problem of designing robust tracking controls for a large class of strict-feedback nonlinear systems involving plant uncertainties and external disturbances. The input and virtual input weighting matrices are perturbed by bounded time-varying uncertainties. An adaptive fuzzy-based (or neural-network-based) dynamic feedback tracking controller will be developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error should be as small as possible. First, the adaptive approximators with linearly parameterized models are designed, and a partitioned procedure with respect to the developed adaptive approximators is proposed such that the implementation of the fuzzy (or neural network) basis functions depends only on the state variables but does not depend on the tuning approximation parameters. Furthermore, we extend to design the nonlinearly parameterized adaptive approximators. Consequently, the intelligent robust tracking control schemes developed in this paper possess the properties of computational simplicity and easy implementation. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed control algorithms.
{"title":"Intelligent robust tracking control for a class of uncertain strict-feedback nonlinear systems.","authors":"Yeong-Chan Chang","doi":"10.1109/TSMCB.2008.2002854","DOIUrl":"https://doi.org/10.1109/TSMCB.2008.2002854","url":null,"abstract":"This paper addresses the problem of designing robust tracking controls for a large class of strict-feedback nonlinear systems involving plant uncertainties and external disturbances. The input and virtual input weighting matrices are perturbed by bounded time-varying uncertainties. An adaptive fuzzy-based (or neural-network-based) dynamic feedback tracking controller will be developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error should be as small as possible. First, the adaptive approximators with linearly parameterized models are designed, and a partitioned procedure with respect to the developed adaptive approximators is proposed such that the implementation of the fuzzy (or neural network) basis functions depends only on the state variables but does not depend on the tuning approximation parameters. Furthermore, we extend to design the nonlinearly parameterized adaptive approximators. Consequently, the intelligent robust tracking control schemes developed in this paper possess the properties of computational simplicity and easy implementation. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed control algorithms.","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":" ","pages":"142-55"},"PeriodicalIF":0.0,"publicationDate":"2009-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2008.2002854","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39997935","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 : 2008-12-01DOI: 10.1109/TSMCB.2008.928233
Chuandong Li, G. Feng, Tingwen Huang
This paper formulates and studies a model of hybrid impulsive and switching Hopfield neural networks (NNs). Using switching Lyapunov functions and a generalized Halanay inequality, some general criteria, which characterize the impulse and switching effects in aggregated form, for asymptotic and exponential stability of such NNs with arbitrary and conditioned impulsive switching are established. Several numerical examples are given for illustration and interpretation of the theoretical results.
{"title":"On Hybrid Impulsive and Switching Neural Networks","authors":"Chuandong Li, G. Feng, Tingwen Huang","doi":"10.1109/TSMCB.2008.928233","DOIUrl":"https://doi.org/10.1109/TSMCB.2008.928233","url":null,"abstract":"This paper formulates and studies a model of hybrid impulsive and switching Hopfield neural networks (NNs). Using switching Lyapunov functions and a generalized Halanay inequality, some general criteria, which characterize the impulse and switching effects in aggregated form, for asymptotic and exponential stability of such NNs with arbitrary and conditioned impulsive switching are established. Several numerical examples are given for illustration and interpretation of the theoretical results.","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":"214 1","pages":"1549-1560"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75576387","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 : 2008-11-05DOI: 10.1109/TSMCA.2008.2008691
J. M. Kay, J. Frolik
Wireless sensor networks are characterized by energy-constrained nodes that are tasked with collecting and forwarding environmental parameters with a requisite measurement, which is spatial and temporal fidelity. At the system level, fidelity is not the only issue of interest but also the achievement of a low-cost solution and a long life for the deployed network. As such, sensor nodes should be low in complexity and should achieve the requisite fidelity requirements, with minimum communication and coordination. This paper proposes that these nodes can operate as automata and still achieve the overall system performance requirements with minimal control. This paper presents and analyzes an automaton architecture and a control strategy designed to maintain spatial fidelity as the performance objective. In particular, we show the following: 1) that the architecture permits control of the number of nodes actively transmitting information in each epoch (denoted by Q); 2) that the variance of Q can be controlled and, particularly, can be set to a value significantly less than that of a Bernoulli-process benchmark (i.e., the architecture is expedient with respect to the control of this variance); 3) that the control strategy is scalable over several orders of magnitudes; and 4) that the methodology is efficient in approaching benchmark performance with respect to energy usage. The proposed methodology has the following specific advantages over the benchmark: 1) The total number of sensors deployed in the network need not be known, and 2) the strategy maintains a robust control of Q over changes in the commanded value and changes in the number of deployed sensors.
{"title":"2008 Index IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans Vol. 38","authors":"J. M. Kay, J. Frolik","doi":"10.1109/TSMCA.2008.2008691","DOIUrl":"https://doi.org/10.1109/TSMCA.2008.2008691","url":null,"abstract":"Wireless sensor networks are characterized by energy-constrained nodes that are tasked with collecting and forwarding environmental parameters with a requisite measurement, which is spatial and temporal fidelity. At the system level, fidelity is not the only issue of interest but also the achievement of a low-cost solution and a long life for the deployed network. As such, sensor nodes should be low in complexity and should achieve the requisite fidelity requirements, with minimum communication and coordination. This paper proposes that these nodes can operate as automata and still achieve the overall system performance requirements with minimal control. This paper presents and analyzes an automaton architecture and a control strategy designed to maintain spatial fidelity as the performance objective. In particular, we show the following: 1) that the architecture permits control of the number of nodes actively transmitting information in each epoch (denoted by Q); 2) that the variance of Q can be controlled and, particularly, can be set to a value significantly less than that of a Bernoulli-process benchmark (i.e., the architecture is expedient with respect to the control of this variance); 3) that the control strategy is scalable over several orders of magnitudes; and 4) that the methodology is efficient in approaching benchmark performance with respect to energy usage. The proposed methodology has the following specific advantages over the benchmark: 1) The total number of sensors deployed in the network need not be known, and 2) the strategy maintains a robust control of Q over changes in the commanded value and changes in the number of deployed sensors.","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":"1 1","pages":"1453-1467"},"PeriodicalIF":0.0,"publicationDate":"2008-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73726465","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 : 2008-10-13DOI: 10.1026/1617-6391.7.4.183
U.-C. Klehe
Die Reise der DIN 33430 begann mit großen Zielen: Man wollte Qualitätsstandards für Eignungsbeurteilungen etablieren, Praktikern einen Leitfaden an die Hand geben, helfen, den Beurteilungsprozess kontinuierlich zu verbessern, und Bewerbende vor Fehlbehandlungen schützen (DIN, 2002). In der Praxis ist dieser Weg jedoch nicht stolperfrei: Es gab scharfe Kritik (z.B. von der BDA und einigen Städtetagen), und auch mit der rosigsten Meinung über die DIN 33430 (z. B. Reimann, 2005) kann man nicht umhin, Reimann, Frenzel, Michalke, und Peper zustimmen zu müssen, dass die tatsächliche Verbreitung der DIN in der Praxis nach wie vor eher dürftig ausfällt (Fischer, 2003; Michalke, Peper, & Reimann, 2007).
{"title":"Die DIN 33430: eine komplexe Norm für eine komplexe Welt [The DIN 33430: A complex norm for a complex world]","authors":"U.-C. Klehe","doi":"10.1026/1617-6391.7.4.183","DOIUrl":"https://doi.org/10.1026/1617-6391.7.4.183","url":null,"abstract":"Die Reise der DIN 33430 begann mit großen Zielen: Man wollte Qualitätsstandards für Eignungsbeurteilungen etablieren, Praktikern einen Leitfaden an die Hand geben, helfen, den Beurteilungsprozess kontinuierlich zu verbessern, und Bewerbende vor Fehlbehandlungen schützen (DIN, 2002). In der Praxis ist dieser Weg jedoch nicht stolperfrei: Es gab scharfe Kritik (z.B. von der BDA und einigen Städtetagen), und auch mit der rosigsten Meinung über die DIN 33430 (z. B. Reimann, 2005) kann man nicht umhin, Reimann, Frenzel, Michalke, und Peper zustimmen zu müssen, dass die tatsächliche Verbreitung der DIN in der Praxis nach wie vor eher dürftig ausfällt (Fischer, 2003; Michalke, Peper, & Reimann, 2007).","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2008-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87950309","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 : 2008-09-22DOI: 10.1007/978-3-540-87527-7_21
Zhi-hui Zhan, Jun Zhang
{"title":"Adaptive Particle Swarm Optimization","authors":"Zhi-hui Zhan, Jun Zhang","doi":"10.1007/978-3-540-87527-7_21","DOIUrl":"https://doi.org/10.1007/978-3-540-87527-7_21","url":null,"abstract":"","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":"20 1","pages":"1362-1381"},"PeriodicalIF":0.0,"publicationDate":"2008-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91076080","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 : 2008-08-01DOI: 10.1109/TSMCB.2008.924139
P. Werbos
This forward to the special issue on adaptive dynamic programming (ADP) and reinforcement learning in feedback control is written by Paul Werbos, the founder of ADP.
{"title":"Foreword: ADP - The Key Direction for Future Research in Intelligent Control and Understanding Brain Intelligence","authors":"P. Werbos","doi":"10.1109/TSMCB.2008.924139","DOIUrl":"https://doi.org/10.1109/TSMCB.2008.924139","url":null,"abstract":"This forward to the special issue on adaptive dynamic programming (ADP) and reinforcement learning in feedback control is written by Paul Werbos, the founder of ADP.","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":"28 1","pages":"898-900"},"PeriodicalIF":0.0,"publicationDate":"2008-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82102708","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 : 2008-08-01DOI: 10.1109/TSMCB.2008.925890
F. Lewis, D. Liu, G. Lendaris
The 18 papers in this special issue focus on adaptive dynamic programming and reinforcement learning in feedback control.
这期特刊的18篇论文集中在反馈控制中的自适应动态规划和强化学习。
{"title":"Guest Editorial: Special Issue on Adaptive Dynamic Programming and Reinforcement Learning in Feedback Control","authors":"F. Lewis, D. Liu, G. Lendaris","doi":"10.1109/TSMCB.2008.925890","DOIUrl":"https://doi.org/10.1109/TSMCB.2008.925890","url":null,"abstract":"The 18 papers in this special issue focus on adaptive dynamic programming and reinforcement learning in feedback control.","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":"16 1","pages":"896-897"},"PeriodicalIF":0.0,"publicationDate":"2008-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90655570","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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