An optimal control problem for a class of distributed parameter systems with bilinear type controls applied to the coefficient matries is formulated. The necessary conditions for optimal bilinear controls from the variational technique are given. The conjugate gradient minimization algorithm is presented with a numerical example.
{"title":"Optimal control of bilinear distributed parameter systems","authors":"Kwang Y. Lee, James Clary","doi":"10.1109/CDC.1978.267995","DOIUrl":"https://doi.org/10.1109/CDC.1978.267995","url":null,"abstract":"An optimal control problem for a class of distributed parameter systems with bilinear type controls applied to the coefficient matries is formulated. The necessary conditions for optimal bilinear controls from the variational technique are given. The conjugate gradient minimization algorithm is presented with a numerical example.","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"18 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":"121802597","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}
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}
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}
Modern statistical continuous-time nonlinear filtering theory has become so esoteric that its utility for practical applications is frequently questioned. This paper examines whether there may be an alternative rationale for arriving at a plausible nonlinear filter which could be more readily implemented in practice. This rationale dispenses with statistics entirely and approaches the problem as nonlinear least squares curve fitting. In order to do this we consider only a model which contains observation "noise" only, and no state "noise". The object is not so much to come up with a specific filter which solves a specific problem as to gain insight into the nature of the obstacles to computational ease which seem inherent in any formulation.
{"title":"Nonstatistical nonlinear filtering","authors":"R. Mortensen","doi":"10.1109/CDC.1978.267906","DOIUrl":"https://doi.org/10.1109/CDC.1978.267906","url":null,"abstract":"Modern statistical continuous-time nonlinear filtering theory has become so esoteric that its utility for practical applications is frequently questioned. This paper examines whether there may be an alternative rationale for arriving at a plausible nonlinear filter which could be more readily implemented in practice. This rationale dispenses with statistics entirely and approaches the problem as nonlinear least squares curve fitting. In order to do this we consider only a model which contains observation \"noise\" only, and no state \"noise\". The object is not so much to come up with a specific filter which solves a specific problem as to gain insight into the nature of the obstacles to computational ease which seem inherent in any formulation.","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"31 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":"127114460","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}
The design of an aircraft lateral control system, subject to several performance criteria and constraints, is considered. While in the previous studies of the same model a single criterion optimization, with other performance requirements expressed as constraints, has been pursued, the current approach involves a multiple criteria optimization. In particular, a Pareto Optimal solution is sought. To the best of the authors' knowledge, this approach has not been reported in the previous literature, while applied to the class of problems under consideration.
{"title":"A multiple objective optimization approach to aircraft control systems design","authors":"D. Tabak, A. Schy, D. Giesy, K. Johnson","doi":"10.1109/CDC.1978.267953","DOIUrl":"https://doi.org/10.1109/CDC.1978.267953","url":null,"abstract":"The design of an aircraft lateral control system, subject to several performance criteria and constraints, is considered. While in the previous studies of the same model a single criterion optimization, with other performance requirements expressed as constraints, has been pursued, the current approach involves a multiple criteria optimization. In particular, a Pareto Optimal solution is sought. To the best of the authors' knowledge, this approach has not been reported in the previous literature, while applied to the class of problems under consideration.","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"119 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":"117338491","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}
This paper presents two results: (i) a new structure for the solution of nonlinear analytic systems, and (ii) an application of Bellman's Fundamental Technique to obtain the sub-optimal-feedback control of a class of quasilinear systems with non-quadratic performance indices. The application of the Fundamental Technique with a non-linear auxiliary equation is shown to result in higher order approximating equations which are linear. Using the method by separation of variables, two examples are solved.
{"title":"A functional expansion approach to the solution of nonlinear feedback systems","authors":"Ramanand Singh, T. Johnson","doi":"10.1109/CDC.1978.267944","DOIUrl":"https://doi.org/10.1109/CDC.1978.267944","url":null,"abstract":"This paper presents two results: (i) a new structure for the solution of nonlinear analytic systems, and (ii) an application of Bellman's Fundamental Technique to obtain the sub-optimal-feedback control of a class of quasilinear systems with non-quadratic performance indices. The application of the Fundamental Technique with a non-linear auxiliary equation is shown to result in higher order approximating equations which are linear. Using the method by separation of variables, two examples are solved.","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"390 2 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":"133516947","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}
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