We consider the problem of steering the state of the system x(t) = A(t)x(t) + f(t, u(t)) to a pre-specified target set X. No assumptions about the structure of the target set are made, and criteria for controllability to a target at some prespecified time are obtained. Finally, we obtain criteria for global controllability.
{"title":"Controlling a system to a non-convex target","authors":"W. Hwang, W. Schmitendorf","doi":"10.1109/CDC.1980.271974","DOIUrl":"https://doi.org/10.1109/CDC.1980.271974","url":null,"abstract":"We consider the problem of steering the state of the system x(t) = A(t)x(t) + f(t, u(t)) to a pre-specified target set X. No assumptions about the structure of the target set are made, and criteria for controllability to a target at some prespecified time are obtained. Finally, we obtain criteria for global controllability.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1980-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114904302","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}
The design of controllers is considered as a parameter optimization problem within the framework of optimal control theory and the calculus of variations. This approach yields a minimum time optimal control problem which is singular. Methods for solving singular optimal control problems are applied to determine optimal parameters for controllers. Examples are presented to illustrate the design of control systems using this approach.
{"title":"Optimization of the parameters of controllers","authors":"Arthur Brummel, B. McInnis","doi":"10.1109/CDC.1980.271969","DOIUrl":"https://doi.org/10.1109/CDC.1980.271969","url":null,"abstract":"The design of controllers is considered as a parameter optimization problem within the framework of optimal control theory and the calculus of variations. This approach yields a minimum time optimal control problem which is singular. Methods for solving singular optimal control problems are applied to determine optimal parameters for controllers. Examples are presented to illustrate the design of control systems using this approach.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1980-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115186427","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}
A way to model systems with abruptly changing dynamics is suggested. The parameters of the system are described as realizations of a finite-state Markov chain. It is further discussed how to perform recursive parameter identification for this type of system. A crucial part in the identification algorithm is to estimate the present state of the Markov chain. The effects of some typical rules to do this estimation are examined. Also a procedure which reduces the need for a priori information is given.
{"title":"An approach to recursive identification of abruptly changing systems","authors":"M. Millnert","doi":"10.1109/CDC.1980.271957","DOIUrl":"https://doi.org/10.1109/CDC.1980.271957","url":null,"abstract":"A way to model systems with abruptly changing dynamics is suggested. The parameters of the system are described as realizations of a finite-state Markov chain. It is further discussed how to perform recursive parameter identification for this type of system. A crucial part in the identification algorithm is to estimate the present state of the Markov chain. The effects of some typical rules to do this estimation are examined. Also a procedure which reduces the need for a priori information is given.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1980-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121386432","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}
Simultaneous control of both species in the Lotka-Volterra prey-predator system model has been previously investigated by Goh, et al. [1], Vincent, et al. [2], Vincent [3], and others. More recently May, et al. [4] using a new model, somewhat akin to the Lotka-Volterra system, examined the consequences of simultaneous constant effort harvesting of both the prey (krill) and the predators (Baleen Whales).
{"title":"Multispaces harvesting of a pery-predator system","authors":"T. Vincent","doi":"10.1109/CDC.1980.271810","DOIUrl":"https://doi.org/10.1109/CDC.1980.271810","url":null,"abstract":"Simultaneous control of both species in the Lotka-Volterra prey-predator system model has been previously investigated by Goh, et al. [1], Vincent, et al. [2], Vincent [3], and others. More recently May, et al. [4] using a new model, somewhat akin to the Lotka-Volterra system, examined the consequences of simultaneous constant effort harvesting of both the prey (krill) and the predators (Baleen Whales).","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1980-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123351713","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 estimation of the unknown order of the autoregressive (AR) model obeyed by a finite time series of length N given only that it obeys a finite order AR model. We derive a family of consistent schemes for estimating the unknown order. We give explicit upperbounds for the probability of error of the decision rules.
{"title":"Consistent estimation of the order of autoregressive models","authors":"R. Kashyap","doi":"10.1109/CDC.1980.271948","DOIUrl":"https://doi.org/10.1109/CDC.1980.271948","url":null,"abstract":"We consider the estimation of the unknown order of the autoregressive (AR) model obeyed by a finite time series of length N given only that it obeys a finite order AR model. We derive a family of consistent schemes for estimating the unknown order. We give explicit upperbounds for the probability of error of the decision rules.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1980-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125385881","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 development and evaluation of an adaptive filter for predicting tank motion during the time-of-flight of a projectile. Tank accelerations are assumed to be the output of stationary Markov processes. The parameters of these models are determined by a combination of spectral analysis and maximum likelihood identification using tank tracks obtained under tactical conditions. The determination of model parameters and structure provides a case study of several complimentary features of different types of identification procedures. The various motion models corresponding to different tanks and tests were examined for their similarities and a reduced set of four models was chosen. These four models were used in a parallel bank of extended Kalman filters as an adaptive tracking filter. The filter with the greatest likelihood function at the time of firing was assumed to have the best motion model and thus its state vector was used to determine the lead offset of the gun. A Monte Carlo evaluation of hit probability was made for the adaptive filter and for conventional first-order prediction. The results demonstrate the superiority of the adaptive filter. The final phase of this effort involves the implementation of the adaptive filter on a microprocessor.
{"title":"Development and evaluation of an adaptive algorithm for predicting tank motion","authors":"B. Gibbs, D. Porter","doi":"10.1109/CDC.1980.271858","DOIUrl":"https://doi.org/10.1109/CDC.1980.271858","url":null,"abstract":"This paper discusses the development and evaluation of an adaptive filter for predicting tank motion during the time-of-flight of a projectile. Tank accelerations are assumed to be the output of stationary Markov processes. The parameters of these models are determined by a combination of spectral analysis and maximum likelihood identification using tank tracks obtained under tactical conditions. The determination of model parameters and structure provides a case study of several complimentary features of different types of identification procedures. The various motion models corresponding to different tanks and tests were examined for their similarities and a reduced set of four models was chosen. These four models were used in a parallel bank of extended Kalman filters as an adaptive tracking filter. The filter with the greatest likelihood function at the time of firing was assumed to have the best motion model and thus its state vector was used to determine the lead offset of the gun. A Monte Carlo evaluation of hit probability was made for the adaptive filter and for conventional first-order prediction. The results demonstrate the superiority of the adaptive filter. The final phase of this effort involves the implementation of the adaptive filter on a microprocessor.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1980-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128949914","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}
Efficient generation and correlation algorithms, combined with ideal aperiodic correlation properties, make complementary sequences a useful alternative to binary maximal length sequences (b.m.l.s) in multivariable system identification. Techniques for reducing the effects of errors on the response estimates are described and tested by simulation.
{"title":"Complementary sequences for multivariable system identification","authors":"R. Wilson","doi":"10.1109/CDC.1980.271889","DOIUrl":"https://doi.org/10.1109/CDC.1980.271889","url":null,"abstract":"Efficient generation and correlation algorithms, combined with ideal aperiodic correlation properties, make complementary sequences a useful alternative to binary maximal length sequences (b.m.l.s) in multivariable system identification. Techniques for reducing the effects of errors on the response estimates are described and tested by simulation.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1980-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128994615","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}
{"title":"Stabilizability and controllability of distributed bilinear control systems: Progress report","authors":"M. Slemrod","doi":"10.1109/CDC.1980.271911","DOIUrl":"https://doi.org/10.1109/CDC.1980.271911","url":null,"abstract":"","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"2278 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1980-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130260281","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}
The design problem of choosing a set of parameters so that inequality constraints are satisfied for a specified variation of parameter values about their nominal value, is considered. Such problems occur when systems must be synthesized from components whose values are only known to a certain tolerance. Simple algorithms exist for such problems when the constraints are convex. This paper presents an algorithm which is valid for the non-convex case, The algorithm utilizes concepts employed by Eaves and Zangwill in their generalized cutting plane algorithms.
{"title":"A cut map algorithm for design problems with parameter tolerances","authors":"D. Mayne, E. Polak, A. Voreadis","doi":"10.1109/CDC.1980.272014","DOIUrl":"https://doi.org/10.1109/CDC.1980.272014","url":null,"abstract":"The design problem of choosing a set of parameters so that inequality constraints are satisfied for a specified variation of parameter values about their nominal value, is considered. Such problems occur when systems must be synthesized from components whose values are only known to a certain tolerance. Simple algorithms exist for such problems when the constraints are convex. This paper presents an algorithm which is valid for the non-convex case, The algorithm utilizes concepts employed by Eaves and Zangwill in their generalized cutting plane algorithms.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1980-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129541138","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}
Surveillance system sensors generally provide information on location and on other attributes of the object detected. This additional attribute data can be employed in associating a given report with a set of previous reports in the data base (track) thought to represent a single object. The present Bayesian analysis of the association probabilities, arising from such attribute data goes beyond previous treatments in three ways. First, explicit allowance is made for four different types of attribute parameters encountered in many multi-sensor systems. The second distinguishing feature of this scheme is the explicit consideration of uncertainties in report parameters due to errors and deception, and of uncertainties in track parameters due both to these causes and to association probabilities less than unity. Finally, an inference procedure, based on conditional prior probabilities, is developed to treat cases where there is limited overlap between report and track attribute sets. This situation is frequently encountered in multi-sensor systems.
{"title":"A Bayesian analysis of surveillance attribute data","authors":"D. Atkinson","doi":"10.1109/CDC.1980.271918","DOIUrl":"https://doi.org/10.1109/CDC.1980.271918","url":null,"abstract":"Surveillance system sensors generally provide information on location and on other attributes of the object detected. This additional attribute data can be employed in associating a given report with a set of previous reports in the data base (track) thought to represent a single object. The present Bayesian analysis of the association probabilities, arising from such attribute data goes beyond previous treatments in three ways. First, explicit allowance is made for four different types of attribute parameters encountered in many multi-sensor systems. The second distinguishing feature of this scheme is the explicit consideration of uncertainties in report parameters due to errors and deception, and of uncertainties in track parameters due both to these causes and to association probabilities less than unity. Finally, an inference procedure, based on conditional prior probabilities, is developed to treat cases where there is limited overlap between report and track attribute sets. This situation is frequently encountered in multi-sensor systems.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1980-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131006382","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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