From the control theory viewpoint, the distinguishing feature of decentralized control is incomplete information sharing between controllers. In many practical situations, this incomplete information sharing is imposed by bandwidth limitations on communication channels, or by limitations on the effective bandwidth of computer interfaces. This paper describes an approach to explicitly including bandwidth constraints in decentralized control system design.
{"title":"Applications of causal rate-distortion theory to decentralized control","authors":"Julian Center","doi":"10.1109/cdc.1978.268016","DOIUrl":"https://doi.org/10.1109/cdc.1978.268016","url":null,"abstract":"From the control theory viewpoint, the distinguishing feature of decentralized control is incomplete information sharing between controllers. In many practical situations, this incomplete information sharing is imposed by bandwidth limitations on communication channels, or by limitations on the effective bandwidth of computer interfaces. This paper describes an approach to explicitly including bandwidth constraints in decentralized control system design.","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"73 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116375005","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}
For continuous-time nonlinear deterministic system models with discrete nonlinear measurements in additive gaussian white noise, the extended Kalman filter (EKF) covariance propagation equations linearized about the true unknown trajectory provide the Cramér-Rao lower bound to the estimation error covariance matrix. A useful application is establishing the optimum filter performance for a given nonlinear estimation problem by developing a simulation of the nonlinear system and an EKF linearized about the true trajectory.
{"title":"The Cramer-Rao estimation error lower bound computation for deterministic nonlinear systems","authors":"James H. Taylor","doi":"10.1109/CDC.1978.268121","DOIUrl":"https://doi.org/10.1109/CDC.1978.268121","url":null,"abstract":"For continuous-time nonlinear deterministic system models with discrete nonlinear measurements in additive gaussian white noise, the extended Kalman filter (EKF) covariance propagation equations linearized about the true unknown trajectory provide the Cramér-Rao lower bound to the estimation error covariance matrix. A useful application is establishing the optimum filter performance for a given nonlinear estimation problem by developing a simulation of the nonlinear system and an EKF linearized about the true trajectory.","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115659583","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 presents computational results relating to solution of convex multicommodity network flow problems by using several recently developed optimization algorithms. These algorithms are based on the ideas of Gallager's method for distributed optimization of delay in data communication networks [1], and gradient projection ideas from nonlinear programming [2], [3]. They can be used both with and without a line search. An important common feature of the algorithms which distinguishes them from other existing methods is that they utilize second derivatives and are geared towards approximating a constrained version of Newton's method. The computational results confirm that the algorithms tend to employ good search directions as well as automatically generate a satisfactory stepsize regardless of the level and pattern of traffic input to the network. This latter advantage is of crucial importance when the algorithms are used for distributed routing of flow in data communication networks where the use of line search is nearly impossible.
{"title":"Validation of algorithms for optimal routing of flow in networks","authors":"D. Bertsekas, E. Gafni, K. Vastola","doi":"10.1109/CDC.1978.267924","DOIUrl":"https://doi.org/10.1109/CDC.1978.267924","url":null,"abstract":"This paper presents computational results relating to solution of convex multicommodity network flow problems by using several recently developed optimization algorithms. These algorithms are based on the ideas of Gallager's method for distributed optimization of delay in data communication networks [1], and gradient projection ideas from nonlinear programming [2], [3]. They can be used both with and without a line search. An important common feature of the algorithms which distinguishes them from other existing methods is that they utilize second derivatives and are geared towards approximating a constrained version of Newton's method. The computational results confirm that the algorithms tend to employ good search directions as well as automatically generate a satisfactory stepsize regardless of the level and pattern of traffic input to the network. This latter advantage is of crucial importance when the algorithms are used for distributed routing of flow in data communication networks where the use of line search is nearly impossible.","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121233939","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}
Recently, a new class of time-domain state space models has been developed (Ref. 1) to describe layered media systems. When layers are uniform, the resulting state equations are referred to as uniform causal functional equations (UCFE). An example of a UCFE is: x (t + ¿) = Ax (t) + b [m(t) + w(t)] (1) where, for a K-layer system, x (t) is a 2K x 1 state vector comprised of K upgoing states and K downgoing states, m(t) is the source signature, w(t) is a random process which reflects uncertainty about our knowledge of m(t), and A and b are matrices (of appropriate dimensions) which are functions of reflection coefficients r0, r1,..., rK which characterize the system. Additionally, ¿ is the one-way travel time for each layer. A surface measurement (i.e., seismogram) y(t), where y(t) = h' x(t) + n(t) (2) is also assumed available. This measurement is corrupted by measurement noise, n(t) and is in terms of vector h which is also a function of some of the reflection coefficients.
最近，一类新的时域状态空间模型被开发出来(参考文献1)来描述分层介质系统。当各层均匀时，产生的状态方程称为均匀因果泛函方程(UCFE)。UCFE的一个例子是:x (t +¿)= Ax (t) + b [m(t) + w(t)](1)，其中，对于K层系统，x (t)是由K个上升状态和K个下降状态组成的2K x 1状态向量，m(t)是源签名，w(t)是反映我们对m(t)知识的不确定性的随机过程，a和b是矩阵(具有适当的维数)，它们是反射系数r0, r1，…， rK表示系统的特征。此外，¿是每层的单程旅行时间。地面测量(即地震图)y(t)，其中y(t) = h' x(t) + n(t)(2)也假定可用。这个测量被测量噪声n(t)所破坏，并且用向量h表示，它也是一些反射系数的函数。
{"title":"Identification of reflection coefficients from noisy data by means of extended minimum variance estimators: A critical examination","authors":"J. Mendel","doi":"10.1109/CDC.1978.267957","DOIUrl":"https://doi.org/10.1109/CDC.1978.267957","url":null,"abstract":"Recently, a new class of time-domain state space models has been developed (Ref. 1) to describe layered media systems. When layers are uniform, the resulting state equations are referred to as uniform causal functional equations (UCFE). An example of a UCFE is: x (t + ¿) = Ax (t) + b [m(t) + w(t)] (1) where, for a K-layer system, x (t) is a 2K x 1 state vector comprised of K upgoing states and K downgoing states, m(t) is the source signature, w(t) is a random process which reflects uncertainty about our knowledge of m(t), and A and b are matrices (of appropriate dimensions) which are functions of reflection coefficients r0, r1,..., rK which characterize the system. Additionally, ¿ is the one-way travel time for each layer. A surface measurement (i.e., seismogram) y(t), where y(t) = h' x(t) + n(t) (2) is also assumed available. This measurement is corrupted by measurement noise, n(t) and is in terms of vector h which is also a function of some of the reflection coefficients.","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121415421","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}
Until now the human vocal tract area function and transfer function were studied from a time-series analysis of the speech signal alone, making some simple assumptions about the glottal source. In this paper we will present a study of the tract from measurements of the speech signal at the lips and the external throat-wall vibration signal near the glottis ("input/output measurements").
{"title":"Models for the human throat-wall and a study of the vocal tract from input/output measurements","authors":"N. Vemula, A. Engebretson, D. Elliott","doi":"10.1109/CDC.1978.268070","DOIUrl":"https://doi.org/10.1109/CDC.1978.268070","url":null,"abstract":"Until now the human vocal tract area function and transfer function were studied from a time-series analysis of the speech signal alone, making some simple assumptions about the glottal source. In this paper we will present a study of the tract from measurements of the speech signal at the lips and the external throat-wall vibration signal near the glottis (\"input/output measurements\").","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125061631","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 nearest neighbor approach to the classification of non-stationary time series is considered. A metric or measure of dissimilarity is computed between a new-to-be classified time series and each of a set of labeled sample time series. The new time series is classified by nearest neighbor rules. The metric is related to the criterion functional used in prediction error time series modeling methods. Engine fault time series data is considered. That data appears to be locally stationary. A Householder transformation - Akaike AIC criterion method for modeling time series by locally stationary AR models is applied to classify the data.
{"title":"Discrimination in locally stationary time series","authors":"W. Gersch, T. Brotherton","doi":"10.1109/CDC.1978.268029","DOIUrl":"https://doi.org/10.1109/CDC.1978.268029","url":null,"abstract":"A nearest neighbor approach to the classification of non-stationary time series is considered. A metric or measure of dissimilarity is computed between a new-to-be classified time series and each of a set of labeled sample time series. The new time series is classified by nearest neighbor rules. The metric is related to the criterion functional used in prediction error time series modeling methods. Engine fault time series data is considered. That data appears to be locally stationary. A Householder transformation - Akaike AIC criterion method for modeling time series by locally stationary AR models is applied to classify the data.","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121223779","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}
In this paper a partially observable system governed by a linear stochastic differential equation is considered. The expected loss is to be minimized in the class of all feedback controls depending linearly on the observation process subject to the condition that the terminal point of the system process lies in some fixed target set with a prescribed probability. The existence of optimal controls is shown via the construction of an equivalent deterministic control problem.
{"title":"Stochastic control under chance constraints","authors":"N. Christopeit","doi":"10.1109/CDC.1978.267977","DOIUrl":"https://doi.org/10.1109/CDC.1978.267977","url":null,"abstract":"In this paper a partially observable system governed by a linear stochastic differential equation is considered. The expected loss is to be minimized in the class of all feedback controls depending linearly on the observation process subject to the condition that the terminal point of the system process lies in some fixed target set with a prescribed probability. The existence of optimal controls is shown via the construction of an equivalent deterministic control problem.","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121743310","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 basis for all advanced manipulator control is a relationship between the cartesian coordinates of the end-effector and the manipulator joint coordinates. A direct method for assigning link coordinate systems and obtaining the end effector position, and Jacobian, in terms of joint coordinates is reviewed. Techniques for obtaining the solution to these equations for kinematically simple manipulators, which includes all commercially available manipulators, is presented. Finally the inverse Jacobian is developed from the solution.
{"title":"Kinematic control equations for simple manipulators","authors":"R. Paul, B. Shimano","doi":"10.1109/CDC.1978.268148","DOIUrl":"https://doi.org/10.1109/CDC.1978.268148","url":null,"abstract":"The basis for all advanced manipulator control is a relationship between the cartesian coordinates of the end-effector and the manipulator joint coordinates. A direct method for assigning link coordinate systems and obtaining the end effector position, and Jacobian, in terms of joint coordinates is reviewed. Techniques for obtaining the solution to these equations for kinematically simple manipulators, which includes all commercially available manipulators, is presented. Finally the inverse Jacobian is developed from the solution.","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"21 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124088053","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}
In this paper, several known computational solutions are readily obtained in a very natural way for the linear regulator, fixed end-point and servo-mechanism problems using a certain frame-work from scattering theory. The relationships between the solutions to the linear regulator problem with different terminal costs and the interplay between the forward and backward equations have enabled a concise derivation of the partitioned equations, the forward-backward equations, and Chandrasekhar equations for the problem. These methods have been extended to the fixed end-point, servo, and tracking problems.
{"title":"Scattering theory and linear optimal control: Regulator and servo problems","authors":"J. Warrior, N. Viswanadham","doi":"10.1109/CDC.1978.268048","DOIUrl":"https://doi.org/10.1109/CDC.1978.268048","url":null,"abstract":"In this paper, several known computational solutions are readily obtained in a very natural way for the linear regulator, fixed end-point and servo-mechanism problems using a certain frame-work from scattering theory. The relationships between the solutions to the linear regulator problem with different terminal costs and the interplay between the forward and backward equations have enabled a concise derivation of the partitioned equations, the forward-backward equations, and Chandrasekhar equations for the problem. These methods have been extended to the fixed end-point, servo, and tracking problems.","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130463976","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 identification of time invariant linear stochastic systems from cross-sectional data on non-stationary system behavior is considered. A strong consistency and asymptotic normality result for maximum likelihood and prediction error estimates of the system parameters, system and measurement noise covariances and the initial state covariance is proven. A new identifiability property for the system model is defined and appears in the set of conditions for this result. The non-stationary stochastic realization (i.e., covariance factorization) theorem in [1] describes sufficient conditions for the identifiability property to hold. An application illustrating the use of a computer program implementing the identification method is presented.
{"title":"Linear system identification from non-stationary cross-sectional data","authors":"R. Goodrich, P. Caines","doi":"10.1109/CDC.1978.267933","DOIUrl":"https://doi.org/10.1109/CDC.1978.267933","url":null,"abstract":"The identification of time invariant linear stochastic systems from cross-sectional data on non-stationary system behavior is considered. A strong consistency and asymptotic normality result for maximum likelihood and prediction error estimates of the system parameters, system and measurement noise covariances and the initial state covariance is proven. A new identifiability property for the system model is defined and appears in the set of conditions for this result. The non-stationary stochastic realization (i.e., covariance factorization) theorem in [1] describes sufficient conditions for the identifiability property to hold. An application illustrating the use of a computer program implementing the identification method is presented.","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127729711","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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