In some practical control systems, the various constraints which come from an actuator have to be encountered. One such constraint is actuator saturation. Also in many mechanical systems, the tracking control is one of the most important problems. Therefore in the unstable system in which the actuator saturation exists, the domain of the initial states corresponding to an arbitrary reference signal, in which the tracking condition is achieved, should be considered. Such a tracking domain is generally complicated. In the paper, the approximate tracking domain is derived analytically with a simple numerical example.
{"title":"Tracking domains for unstable plants with saturating-like actuators","authors":"V. Yakubovich, S. Nakaura, K. Furuta","doi":"10.1109/CDC.1999.831348","DOIUrl":"https://doi.org/10.1109/CDC.1999.831348","url":null,"abstract":"In some practical control systems, the various constraints which come from an actuator have to be encountered. One such constraint is actuator saturation. Also in many mechanical systems, the tracking control is one of the most important problems. Therefore in the unstable system in which the actuator saturation exists, the domain of the initial states corresponding to an arbitrary reference signal, in which the tracking condition is achieved, should be considered. Such a tracking domain is generally complicated. In the paper, the approximate tracking domain is derived analytically with a simple numerical example.","PeriodicalId":137513,"journal":{"name":"Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)","volume":"2019 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132505745","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}
This paper considers a nonlinear stochastic control problem where the system dynamics is a controlled nonlinear backward stochastic differential equation and the state must coincide with a given random vector at the terminal time. A necessary condition of optimality in the form of a global maximum principle as well as a sufficient condition of optimality are presented. The general result is also applied to a backward linear-quadratic control problem and an optimal control is obtained explicitly as a feedback of the solution to a forward-backward equation. Finally, a nonlinear problem with additional integral constraints is discussed and it is shown that the duality gap is zero under the Slater condition.
{"title":"Optimal controls of backward stochastic differential equations","authors":"Nikolai Dokuchaev, X. Zhou","doi":"10.1109/CDC.1999.830191","DOIUrl":"https://doi.org/10.1109/CDC.1999.830191","url":null,"abstract":"This paper considers a nonlinear stochastic control problem where the system dynamics is a controlled nonlinear backward stochastic differential equation and the state must coincide with a given random vector at the terminal time. A necessary condition of optimality in the form of a global maximum principle as well as a sufficient condition of optimality are presented. The general result is also applied to a backward linear-quadratic control problem and an optimal control is obtained explicitly as a feedback of the solution to a forward-backward equation. Finally, a nonlinear problem with additional integral constraints is discussed and it is shown that the duality gap is zero under the Slater condition.","PeriodicalId":137513,"journal":{"name":"Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132591905","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}
This paper discusses the use of stochastic differential equations and point processes to model the long-term fading effects during transmission of electromagnetic waves over large areas, which are subject to multipaths and power loss due to long distance transmission and reflections. When measured in dBs, the power loss follows a mean reverting Ornstein-Uhlenbeck process, which implies that the power loss is log-normally distributed. The arrival times of different paths are modeled using non-homogeneous Poisson counting processes and their statistical properties of the multipath power loss are examined. The moment generating function of the received signal is calculated and subsequently exploited to derive a central limit theorem, and the second-order statistics of the channel.
{"title":"Stochastic models for long-term multipath fading channels and their statistical properties","authors":"C. D. Charalambous, N. Menemenlis","doi":"10.1109/CDC.1999.833330","DOIUrl":"https://doi.org/10.1109/CDC.1999.833330","url":null,"abstract":"This paper discusses the use of stochastic differential equations and point processes to model the long-term fading effects during transmission of electromagnetic waves over large areas, which are subject to multipaths and power loss due to long distance transmission and reflections. When measured in dBs, the power loss follows a mean reverting Ornstein-Uhlenbeck process, which implies that the power loss is log-normally distributed. The arrival times of different paths are modeled using non-homogeneous Poisson counting processes and their statistical properties of the multipath power loss are examined. The moment generating function of the received signal is calculated and subsequently exploited to derive a central limit theorem, and the second-order statistics of the channel.","PeriodicalId":137513,"journal":{"name":"Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132648463","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}
Daily production target setting for each production stage of a semiconductor wafer fabrication factory is a challenging machine capacity allocation problem due to the complex and re-entrant process flows. This paper summarizes a methodology developed by the author for the design of a daily target setting system over the past few years. The methodology realizes PULL-then-PUSH and proportional capacity allocation principles to meet production demands in a smooth way while maximizing machine utilization. As machine capacity allocation and available wafer flows are intertwined, the target setting problem can be viewed as a fixed-point iteration problem. A deterministic queueing analysis-based algorithm is designed to estimate cycle times and hence wafer flows. The methodology iterates between capacity allocation and cycle time estimation until a fixed-point capacity allocation is achieved.
{"title":"Demand-driven, iterative capacity allocation and cycle time estimation for re-entrant lines","authors":"Shi-Chung Chang","doi":"10.1109/CDC.1999.831259","DOIUrl":"https://doi.org/10.1109/CDC.1999.831259","url":null,"abstract":"Daily production target setting for each production stage of a semiconductor wafer fabrication factory is a challenging machine capacity allocation problem due to the complex and re-entrant process flows. This paper summarizes a methodology developed by the author for the design of a daily target setting system over the past few years. The methodology realizes PULL-then-PUSH and proportional capacity allocation principles to meet production demands in a smooth way while maximizing machine utilization. As machine capacity allocation and available wafer flows are intertwined, the target setting problem can be viewed as a fixed-point iteration problem. A deterministic queueing analysis-based algorithm is designed to estimate cycle times and hence wafer flows. The methodology iterates between capacity allocation and cycle time estimation until a fixed-point capacity allocation is achieved.","PeriodicalId":137513,"journal":{"name":"Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132723237","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}
Though a nonlinear stochastic dynamical system can be approximated by feedforward neural networks, the dimension of the input space of the network may be too large, making it to be of little practical importance. The Nonlinear Autoregressive Moving Average model with eXogenous input (NARMAX) is shown to be able to represent a nonlinear stochastic dynamical system under certain conditions. As the dimension of the input space is finite, it can be readily applied in a practical application. It is well known that the training of recurrent networks using the gradient method has a slow convergence rate. In this paper, a fast training algorithm based on the Newton-Raphson method for a recurrent neurofuzzy network with NARMAX structure is presented. The convergence and the uniqueness of the proposed training algorithm are established. A simulation example involving a nonlinear dynamical system corrupted with the correlated noise and a sinusoidal disturbance is used to illustrate the performance of the proposed training algorithm.
{"title":"Modelling of nonlinear stochastic dynamical systems using neurofuzzy networks","authors":"W. C. Chan, C. Chan, K. Cheung, Yu Wang","doi":"10.1109/CDC.1999.831328","DOIUrl":"https://doi.org/10.1109/CDC.1999.831328","url":null,"abstract":"Though a nonlinear stochastic dynamical system can be approximated by feedforward neural networks, the dimension of the input space of the network may be too large, making it to be of little practical importance. The Nonlinear Autoregressive Moving Average model with eXogenous input (NARMAX) is shown to be able to represent a nonlinear stochastic dynamical system under certain conditions. As the dimension of the input space is finite, it can be readily applied in a practical application. It is well known that the training of recurrent networks using the gradient method has a slow convergence rate. In this paper, a fast training algorithm based on the Newton-Raphson method for a recurrent neurofuzzy network with NARMAX structure is presented. The convergence and the uniqueness of the proposed training algorithm are established. A simulation example involving a nonlinear dynamical system corrupted with the correlated noise and a sinusoidal disturbance is used to illustrate the performance of the proposed training algorithm.","PeriodicalId":137513,"journal":{"name":"Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131460631","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}
One of the most serious challenges in the semiconductor memory business is the rapid price decline. We develop an allocation scheme that determines the die (chip) allocation among different memory products. The allocation takes into account available die capacity, customer service requirements, as well as price declines and demand distributions among different products.
{"title":"Capacity allocation in semiconductor fabrication","authors":"G. Feigin, K. Katircioglu, D. Yao","doi":"10.1109/CDC.1999.830144","DOIUrl":"https://doi.org/10.1109/CDC.1999.830144","url":null,"abstract":"One of the most serious challenges in the semiconductor memory business is the rapid price decline. We develop an allocation scheme that determines the die (chip) allocation among different memory products. The allocation takes into account available die capacity, customer service requirements, as well as price declines and demand distributions among different products.","PeriodicalId":137513,"journal":{"name":"Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128895092","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}
Algorithms verifying the covariance matrix of the Kalman filter innovation sequence are compared with respect to detected minimum fault rate and detection time. Four algorithms are dealt with: the algorithm verifying the trace of the covariance matrix of the innovation sequence; the algorithm verifying the sum of all elements of the inverse covariance matrix of the innovation sequence; the optimal algorithm verifying the ratio of two quadratic forms of which matrices are theoretic and selected covariance matrices of Kalman filter innovation sequence; and the algorithm verifying the generalized variance of the covariance matrix of the innovation sequence. The algorithms are implemented for longitudinal dynamics of an aircraft, and some suggestions are given on the use of the algorithms in flight control systems.
{"title":"Innovation sequence application to aircraft sensor fault detection: comparison of checking covariance matrix algorithms","authors":"F. Caliskan, C.M. Hajivyev","doi":"10.1109/CDC.1999.827977","DOIUrl":"https://doi.org/10.1109/CDC.1999.827977","url":null,"abstract":"Algorithms verifying the covariance matrix of the Kalman filter innovation sequence are compared with respect to detected minimum fault rate and detection time. Four algorithms are dealt with: the algorithm verifying the trace of the covariance matrix of the innovation sequence; the algorithm verifying the sum of all elements of the inverse covariance matrix of the innovation sequence; the optimal algorithm verifying the ratio of two quadratic forms of which matrices are theoretic and selected covariance matrices of Kalman filter innovation sequence; and the algorithm verifying the generalized variance of the covariance matrix of the innovation sequence. The algorithms are implemented for longitudinal dynamics of an aircraft, and some suggestions are given on the use of the algorithms in flight control systems.","PeriodicalId":137513,"journal":{"name":"Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131413035","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}
We consider the problem of identifying discrete-time linear parameter varying models of nonlinear or time-varying systems. We assume that inputs, outputs and the scheduling parameters are measured, and a form of the functional dependence of the coefficients on the parameters. We show how the identification problem can be reduced to a linear regression, and we give conditions on persistency of excitation in terms of the inputs and parameter trajectories.
{"title":"Identification of linear parameter varying models","authors":"Bassam Bamieh, L. Giarré","doi":"10.1109/CDC.1999.830205","DOIUrl":"https://doi.org/10.1109/CDC.1999.830205","url":null,"abstract":"We consider the problem of identifying discrete-time linear parameter varying models of nonlinear or time-varying systems. We assume that inputs, outputs and the scheduling parameters are measured, and a form of the functional dependence of the coefficients on the parameters. We show how the identification problem can be reduced to a linear regression, and we give conditions on persistency of excitation in terms of the inputs and parameter trajectories.","PeriodicalId":137513,"journal":{"name":"Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127464926","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}
We introduce a probabilistic measure named model set unfalsified probability (MSUP) for model set validation, where the model set is described by an LFT (linear fractional transformation) form. We derive upper and lower bounds of MSUP and show that the lower bound computation can be reduced to an LMI-based convex optimization. A numerical example confirms that the probabilistic approach more appropriately evaluates the suitability of a model set in robust controller design than deterministic approaches.
{"title":"A probabilistic approach to model set validation","authors":"T. Miyazato, T. Zhou, S. Hara","doi":"10.1109/CDC.1999.831289","DOIUrl":"https://doi.org/10.1109/CDC.1999.831289","url":null,"abstract":"We introduce a probabilistic measure named model set unfalsified probability (MSUP) for model set validation, where the model set is described by an LFT (linear fractional transformation) form. We derive upper and lower bounds of MSUP and show that the lower bound computation can be reduced to an LMI-based convex optimization. A numerical example confirms that the probabilistic approach more appropriately evaluates the suitability of a model set in robust controller design than deterministic approaches.","PeriodicalId":137513,"journal":{"name":"Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115225580","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}
We consider a model of a multiclass make-to-stock manufacturing system. External demand for each product class is met from the available finished goods inventory; unsatisfied demand is backlogged. The objective is to devise a production policy that minimizes inventory costs subject to guaranteeing stockout probabilities to stay bounded above by given constants /spl epsiv//sub j/, for each product class j (service level guarantees). Approximating the original system, we analyze a corresponding fluid model to take sequencing decisions and employ large deviation techniques to take idling ones, under various inventory cost structures. Our model is more realistic than most in the literature, since it can accommodate autocorrelated demand and service processes, both critical features of modern failure-prone manufacturing systems.
{"title":"Controlling make-to-stock manufacturing systems: a large deviations approach","authors":"D. Bertsimas, I. Paschalidis","doi":"10.1109/CDC.1999.832821","DOIUrl":"https://doi.org/10.1109/CDC.1999.832821","url":null,"abstract":"We consider a model of a multiclass make-to-stock manufacturing system. External demand for each product class is met from the available finished goods inventory; unsatisfied demand is backlogged. The objective is to devise a production policy that minimizes inventory costs subject to guaranteeing stockout probabilities to stay bounded above by given constants /spl epsiv//sub j/, for each product class j (service level guarantees). Approximating the original system, we analyze a corresponding fluid model to take sequencing decisions and employ large deviation techniques to take idling ones, under various inventory cost structures. Our model is more realistic than most in the literature, since it can accommodate autocorrelated demand and service processes, both critical features of modern failure-prone manufacturing systems.","PeriodicalId":137513,"journal":{"name":"Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115799319","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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