An accelerometer can be constructed using a radioactive proof mass the position of which can be determined on the basis of the difference between the mean number of emitted charged particles detected at opposite walls when the proof mass is not centered. If the emission rate is relatively small, however, the mean counting rate is very noisy; satisfactory operation requires optimum signal processing. The control system, designed by linear stochastic control theory, generates forces that tend to keep the proof mass centered between walls of the instrument and simultaneously produces on estimated acceleration output. The Poisson-type noise of the pickoff is approximated by Gaussian noise; the acceleration input is modeled as a random walk. It is found by simulation that for an instrument with 1.0 inch spacing between the walls, the proof mass excursion can be confined to a peak-to-peak amplitude of 0.36 inch in a severe acceleration environment. The accelerometer output tracks a constant input without error and, when the input acceleration has a random component, the rms error is of the order of ten percent of the random component.
{"title":"Control system design using radioactive pickoff","authors":"B. Friedland, J. Richman","doi":"10.1109/CDC.1975.270724","DOIUrl":"https://doi.org/10.1109/CDC.1975.270724","url":null,"abstract":"An accelerometer can be constructed using a radioactive proof mass the position of which can be determined on the basis of the difference between the mean number of emitted charged particles detected at opposite walls when the proof mass is not centered. If the emission rate is relatively small, however, the mean counting rate is very noisy; satisfactory operation requires optimum signal processing. The control system, designed by linear stochastic control theory, generates forces that tend to keep the proof mass centered between walls of the instrument and simultaneously produces on estimated acceleration output. The Poisson-type noise of the pickoff is approximated by Gaussian noise; the acceleration input is modeled as a random walk. It is found by simulation that for an instrument with 1.0 inch spacing between the walls, the proof mass excursion can be confined to a peak-to-peak amplitude of 0.36 inch in a severe acceleration environment. The accelerometer output tracks a constant input without error and, when the input acceleration has a random component, the rms error is of the order of ten percent of the random component.","PeriodicalId":164707,"journal":{"name":"1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1975-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128148564","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}
Secure power system operation is obtained, to a large extent, by on-line monitoring of the system assessing its security, and providing control action to improve its security level. Traditionally, the economy of system operation was the overriding factor. By combining economy with security, the problem becomes quite complex from the points of view of: (a) Methodology of security assessment (b) Overall objectives of system optimization, and (c) Computational feasibility for on-line analysis. In this paper, an overview of the application of optimization theory to this problem is examined. A critical analysis of a selected subset of methodologies is provided.
{"title":"Security constrained optimization for power systems","authors":"A. Debs","doi":"10.1109/CDC.1975.270697","DOIUrl":"https://doi.org/10.1109/CDC.1975.270697","url":null,"abstract":"Secure power system operation is obtained, to a large extent, by on-line monitoring of the system assessing its security, and providing control action to improve its security level. Traditionally, the economy of system operation was the overriding factor. By combining economy with security, the problem becomes quite complex from the points of view of: (a) Methodology of security assessment (b) Overall objectives of system optimization, and (c) Computational feasibility for on-line analysis. In this paper, an overview of the application of optimization theory to this problem is examined. A critical analysis of a selected subset of methodologies is provided.","PeriodicalId":164707,"journal":{"name":"1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1975-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124278496","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 review of the design of an interactive digital image processing system is presented. Reasons for interactive image analysis are given and the functional requirements of a system are determined from image processing problems and by considering the techniques applied by a human photointerpreter. Design considerations and the implementation of an interactive image processing system on a general purpose computer are described. Several examples demonstrating the application of interactive digital image processing are given.
{"title":"Interactive analysis of digital images: A systems design overview","authors":"Hans Moik","doi":"10.1109/CDC.1975.270688","DOIUrl":"https://doi.org/10.1109/CDC.1975.270688","url":null,"abstract":"A review of the design of an interactive digital image processing system is presented. Reasons for interactive image analysis are given and the functional requirements of a system are determined from image processing problems and by considering the techniques applied by a human photointerpreter. Design considerations and the implementation of an interactive image processing system on a general purpose computer are described. Several examples demonstrating the application of interactive digital image processing are given.","PeriodicalId":164707,"journal":{"name":"1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1975-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113940848","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}
M. Athans, P. Houpt, D. Looze, D. Orlhac, S. Gershwin, J. Speyer
This paper deals with the dynamic stochastic control of a freeway corridor system, which consists of a multilane freeway interconnected with entry and exit ramps to one or more parallel traffic arterials, and subject to ramp controls and signals at intersections. Key issues associated with the use of modern estimation and control theory are presented.
{"title":"Stochastic control of freeway corridor systems","authors":"M. Athans, P. Houpt, D. Looze, D. Orlhac, S. Gershwin, J. Speyer","doi":"10.1109/CDC.1975.270590","DOIUrl":"https://doi.org/10.1109/CDC.1975.270590","url":null,"abstract":"This paper deals with the dynamic stochastic control of a freeway corridor system, which consists of a multilane freeway interconnected with entry and exit ramps to one or more parallel traffic arterials, and subject to ramp controls and signals at intersections. Key issues associated with the use of modern estimation and control theory are presented.","PeriodicalId":164707,"journal":{"name":"1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1975-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116915226","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}
Generalized partitioned solutions of Riccati equations are presented in terms of forward and backward-time differentiations that are theoretically interesting, computationally attractive, as well as they provide important new interpretations of these results. This approach leads also to important generalizations of previous Riccati solutions such as the Chandrasekhar and the partitioned algorithms. Specifically, it is shown that the generalized partitioned solutions may be given in terms of a generalized Chandrasekhar algorithm. These generalizations pertain to arbitrary initial conditions and time-varying models. Furthermore, based on these partitioned solutions, robust and fast algorithms are obtained for the effective numerical solution of Riccati equations. A particularly effective doubling algorithm is also given for calculating the steady-state solution of time-invariant Riccati equations. The partitioned algorithms are given exactly in terms of a set of elemental solutions which are both simple as well as completely decoupled, and as such computable in either a parallel or serial processing mode. Moreover, the overall solution is given by a simple recursive operation on the elemental solutions.
{"title":"Partitioned Riccati algorithms","authors":"D. Lainiotis","doi":"10.1109/CDC.1975.270602","DOIUrl":"https://doi.org/10.1109/CDC.1975.270602","url":null,"abstract":"Generalized partitioned solutions of Riccati equations are presented in terms of forward and backward-time differentiations that are theoretically interesting, computationally attractive, as well as they provide important new interpretations of these results. This approach leads also to important generalizations of previous Riccati solutions such as the Chandrasekhar and the partitioned algorithms. Specifically, it is shown that the generalized partitioned solutions may be given in terms of a generalized Chandrasekhar algorithm. These generalizations pertain to arbitrary initial conditions and time-varying models. Furthermore, based on these partitioned solutions, robust and fast algorithms are obtained for the effective numerical solution of Riccati equations. A particularly effective doubling algorithm is also given for calculating the steady-state solution of time-invariant Riccati equations. The partitioned algorithms are given exactly in terms of a set of elemental solutions which are both simple as well as completely decoupled, and as such computable in either a parallel or serial processing mode. Moreover, the overall solution is given by a simple recursive operation on the elemental solutions.","PeriodicalId":164707,"journal":{"name":"1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1975-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117273008","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 major advances have been made in the analysis and estimation of parameters of stochastic compartmental models. The theory of illness-death processes as given by Chiang (1) provides a basis for the analysis of this important class of stochastic models. Motivated by the need for stochastic pharmocokinetic models, we have derived results which enable us to identify the parameters of m compartment models using time series data from one to r compartments. Following Matis and Hartley (2) we have derived explicit expressions for the elements of the covariance matrix for the case of observations from r compartments. We then incorporate the covariance matrix in a generalized least squares estimation of the parameters from time-series data. The parameters identification procedure, which uses a modified Gauss-Newton technique to minimize the generalized sum of squares, yields estimates of the values of the flow rates between compartments and standard deviations for these parameters.
{"title":"Analysis and parameter identification of stochastic compartmental models","authors":"A. Kapadia, B. McInnis, S. El-asfouri","doi":"10.1109/CDC.1975.270745","DOIUrl":"https://doi.org/10.1109/CDC.1975.270745","url":null,"abstract":"Recently major advances have been made in the analysis and estimation of parameters of stochastic compartmental models. The theory of illness-death processes as given by Chiang (1) provides a basis for the analysis of this important class of stochastic models. Motivated by the need for stochastic pharmocokinetic models, we have derived results which enable us to identify the parameters of m compartment models using time series data from one to r compartments. Following Matis and Hartley (2) we have derived explicit expressions for the elements of the covariance matrix for the case of observations from r compartments. We then incorporate the covariance matrix in a generalized least squares estimation of the parameters from time-series data. The parameters identification procedure, which uses a modified Gauss-Newton technique to minimize the generalized sum of squares, yields estimates of the values of the flow rates between compartments and standard deviations for these parameters.","PeriodicalId":164707,"journal":{"name":"1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1975-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115297141","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}
Much interest has been generated in the design of multivariable systems. In particular, the system design problem can be transformed into a problem involving a system performance index with algebraic constraints. This paper investigates several approaches to solving the system design problem through application of optimization techniques, once the system performance index and constraint problem has been formulated. The pole placement problem with feedback gain constraints is developed as the motivating example.
{"title":"Pole placement with feedback gain constraints","authors":"Gordon Lee, D. Jordan","doi":"10.1109/CDC.1975.270675","DOIUrl":"https://doi.org/10.1109/CDC.1975.270675","url":null,"abstract":"Much interest has been generated in the design of multivariable systems. In particular, the system design problem can be transformed into a problem involving a system performance index with algebraic constraints. This paper investigates several approaches to solving the system design problem through application of optimization techniques, once the system performance index and constraint problem has been formulated. The pole placement problem with feedback gain constraints is developed as the motivating example.","PeriodicalId":164707,"journal":{"name":"1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1975-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130164072","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 method is proposed for estimating the norm of a system having a random damping term. Apart from the damping, the systems of interest would be norm invariant. Hence the estimation scheme is really a way of monitoring the damping. The estimator is a first order linear filter, and the results are suboptimal.
{"title":"Norm estimation for systems with random damping","authors":"C. Sims","doi":"10.1109/CDC.1975.270706","DOIUrl":"https://doi.org/10.1109/CDC.1975.270706","url":null,"abstract":"A method is proposed for estimating the norm of a system having a random damping term. Apart from the damping, the systems of interest would be norm invariant. Hence the estimation scheme is really a way of monitoring the damping. The estimator is a first order linear filter, and the results are suboptimal.","PeriodicalId":164707,"journal":{"name":"1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes","volume":"824 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1975-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125085562","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}
It is argued that the interests of control and system theorists can be divided into two sets: the technical interests labeled Control Theory and the "System" interests called Cybernetics. It is suggested that unique contributions to economics can only come from Cybernetics.
{"title":"Role of control and system theory in economics","authors":"P. Varaiya","doi":"10.1109/CDC.1975.270640","DOIUrl":"https://doi.org/10.1109/CDC.1975.270640","url":null,"abstract":"It is argued that the interests of control and system theorists can be divided into two sets: the technical interests labeled Control Theory and the \"System\" interests called Cybernetics. It is suggested that unique contributions to economics can only come from Cybernetics.","PeriodicalId":164707,"journal":{"name":"1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1975-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116402049","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 the model of this paper the state of the system follows a linear auto-regressive process and is observed with noise. The decision-maker's problem is to estimate the current state: with a payoff function quadratic in the decision variables the optimal estimator is the conditional mean, given the observations. With the use of the Kalman filter results of interest to economists are obtained. The basic result is that the optimal estimate is a convex linear combination of the current observation and the previous optimal estimate. This is a generalization of the 'adaptive expectations' formula widely used in economics.
{"title":"A generalized 'adaptive expectations' formula in auto-regressive models","authors":"Ronald Britto","doi":"10.1109/CDC.1975.270613","DOIUrl":"https://doi.org/10.1109/CDC.1975.270613","url":null,"abstract":"In the model of this paper the state of the system follows a linear auto-regressive process and is observed with noise. The decision-maker's problem is to estimate the current state: with a payoff function quadratic in the decision variables the optimal estimator is the conditional mean, given the observations. With the use of the Kalman filter results of interest to economists are obtained. The basic result is that the optimal estimate is a convex linear combination of the current observation and the previous optimal estimate. This is a generalization of the 'adaptive expectations' formula widely used in economics.","PeriodicalId":164707,"journal":{"name":"1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes","volume":"376 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1975-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116473070","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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