The Smith deadtime compensator with and without model gain-adaptation is applied via a digital computer to a heat exchange process. A first order model with deadtime forms the basis of the well-known compensation scheme which is used in combination with a main process controller of the proportional plus integral (PI) form. Here, adaptation of the static model gain is added in an attempt to stabilize the control system in the face of certain types of process changes. This gain-adaptor also uses PI control of the static model gain to force the undelayed model response into agreement with the process response. Further, as the static model gain changes, the main process controller gain is changed in an attempt to maintain stability. Test runs were made on an actual double-pipe heat exchanger of an industrial size using steam to heat water. Control of the outlet water temperature (process response) was tried using the deadtime compensation schemes and regular PI control by itself. The responses for step changes in set-point water temperature show the superior tracking behavior of the deadtime compensation schemes. For decreases in the water flow rate, it is shown that regular PI control and Smith's method can give oscillatory responses. With proper tuning the gain-adaptive procedure is shown to maintain stability for this type of process change.
{"title":"Gain-adaptive control applied to a heat exchange process using a first order plus deadtime compensator","authors":"H. Chiang, L. Durbin","doi":"10.1109/CDC.1980.271802","DOIUrl":"https://doi.org/10.1109/CDC.1980.271802","url":null,"abstract":"The Smith deadtime compensator with and without model gain-adaptation is applied via a digital computer to a heat exchange process. A first order model with deadtime forms the basis of the well-known compensation scheme which is used in combination with a main process controller of the proportional plus integral (PI) form. Here, adaptation of the static model gain is added in an attempt to stabilize the control system in the face of certain types of process changes. This gain-adaptor also uses PI control of the static model gain to force the undelayed model response into agreement with the process response. Further, as the static model gain changes, the main process controller gain is changed in an attempt to maintain stability. Test runs were made on an actual double-pipe heat exchanger of an industrial size using steam to heat water. Control of the outlet water temperature (process response) was tried using the deadtime compensation schemes and regular PI control by itself. The responses for step changes in set-point water temperature show the superior tracking behavior of the deadtime compensation schemes. For decreases in the water flow rate, it is shown that regular PI control and Smith's method can give oscillatory responses. With proper tuning the gain-adaptive procedure is shown to maintain stability for this type of process change.","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":"115186167","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}
J. Wakabayashi, Akira Fukumoto, Shin-ichi Tashima, Isao Kawahara
The authors propose a diagnostic system of nuclear power plants which is composed of three blocks, i.e. 1) detection and classification block, 2) disturbance estimation block and 3) storage of past observed signals. In the block-1, a set of observed signals is identified with one of the categories prescribed to present the normal and several anomalous situations in multidimentional space, where the linear discriminant functions basing maximum likelihood technique are utilized. An approximate linear dynamic model for the individual prescribed anomalous state is identified beforehand, where the disturbance and several assumed variables are utilized in a dynamic model and a observed vector is composed of several selected observed signals. The Kalman filters for all anomalous categories are obtained using corresponding dynamic models, and they are provided in the block-2. When the present state is identified to one of the prescribed anomalous situations by the block-1, a Kalman filter corresponding to the identified category is selected from the block-2, and the disturbance is estimated using the past observed signals obtained from the block-3 and future coming signals. The linear discriminate functions and the approximate linear dynamic models are derived using the data base of prescribed categories obtained from the accurate plant simulator. The database will be improved by the experience of actual plant. The effectiveness of this diagnostic system was examined by the computer experiment. The results show that classification of the present operating state and estimation of disturbance are available with reasonable reliability and reasonable computation time.
{"title":"Application of adaptive Kalman filtering technique for the diagnostic system of nuclear power plants","authors":"J. Wakabayashi, Akira Fukumoto, Shin-ichi Tashima, Isao Kawahara","doi":"10.1109/CDC.1980.272031","DOIUrl":"https://doi.org/10.1109/CDC.1980.272031","url":null,"abstract":"The authors propose a diagnostic system of nuclear power plants which is composed of three blocks, i.e. 1) detection and classification block, 2) disturbance estimation block and 3) storage of past observed signals. In the block-1, a set of observed signals is identified with one of the categories prescribed to present the normal and several anomalous situations in multidimentional space, where the linear discriminant functions basing maximum likelihood technique are utilized. An approximate linear dynamic model for the individual prescribed anomalous state is identified beforehand, where the disturbance and several assumed variables are utilized in a dynamic model and a observed vector is composed of several selected observed signals. The Kalman filters for all anomalous categories are obtained using corresponding dynamic models, and they are provided in the block-2. When the present state is identified to one of the prescribed anomalous situations by the block-1, a Kalman filter corresponding to the identified category is selected from the block-2, and the disturbance is estimated using the past observed signals obtained from the block-3 and future coming signals. The linear discriminate functions and the approximate linear dynamic models are derived using the data base of prescribed categories obtained from the accurate plant simulator. The database will be improved by the experience of actual plant. The effectiveness of this diagnostic system was examined by the computer experiment. The results show that classification of the present operating state and estimation of disturbance are available with reasonable reliability and reasonable computation time.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"29 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":"114228558","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 deals with a class of nonlinear systems, linear with respect to the inputs. A necessary and sufficient condition of observability for any input function is obtained. A canonical form is given.
{"title":"Observability for any u(t) of a class of nonlinear systems","authors":"J. Gauthier, G. Bornard","doi":"10.1109/CDC.1980.271933","DOIUrl":"https://doi.org/10.1109/CDC.1980.271933","url":null,"abstract":"This paper deals with a class of nonlinear systems, linear with respect to the inputs. A necessary and sufficient condition of observability for any input function is obtained. A canonical form is given.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"81 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":"126219497","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 we study and solve completely the static Nash and Leader-Follower games where the players have quadratic costs and linear measurements of a random variable which enters linearly into the costs, see (1)- (7). Several dynamic cases are included in the static formulation as long as appropriate nestedness conditions [4] are imposed on the information of the players.
{"title":"Stochastic quadratic Nash and leader-follower games","authors":"G. Papavassilopoulos","doi":"10.1109/CDC.1980.271990","DOIUrl":"https://doi.org/10.1109/CDC.1980.271990","url":null,"abstract":"In this paper we study and solve completely the static Nash and Leader-Follower games where the players have quadratic costs and linear measurements of a random variable which enters linearly into the costs, see (1)- (7). Several dynamic cases are included in the static formulation as long as appropriate nestedness conditions [4] are imposed on the information of the players.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"11 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":"126698310","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 we derive recursive expression for certain distance measures between time-continuous, stationary, vector Gaussian processes, and then utilize them to derive upper bounds to the mean square error performance of the Bayes and Maximum Likelihood estimate of a parameter, when only a finite-valued parameter set is utilized. The question of convergence when the true parameter value does not belong to the finite set is also answered.
{"title":"Performance bounds for parameter estimation from time-continuous observations","authors":"D. Kazakos","doi":"10.1109/CDC.1980.271883","DOIUrl":"https://doi.org/10.1109/CDC.1980.271883","url":null,"abstract":"In this paper we derive recursive expression for certain distance measures between time-continuous, stationary, vector Gaussian processes, and then utilize them to derive upper bounds to the mean square error performance of the Bayes and Maximum Likelihood estimate of a parameter, when only a finite-valued parameter set is utilized. The question of convergence when the true parameter value does not belong to the finite set is also answered.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"131 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":"121543840","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 short paper examines use of the singular value decomposition of the augmented matrix [A - ¿I,B] to find its null space and then, subsequently, the subspace of possible closed loop eigenvectors and the necessary feedback matrix, K, for the assignment of the specified closed loop eigenvalues and eigenvectors. This paper describes the very attractive computational alternative of using the singular value decomposition rather than the previously reported approach of elementary column operations. The assignment of complex eigenvalues and repeated eigenvalues using the same basic singular value decomposition of a real matrix is also discussed.
{"title":"Computation of the subspaces for entire eigenstructure assignment via the singular value decomposition","authors":"J. Silverthorn, J. Reid","doi":"10.1109/CDC.1980.271993","DOIUrl":"https://doi.org/10.1109/CDC.1980.271993","url":null,"abstract":"This short paper examines use of the singular value decomposition of the augmented matrix [A - ¿I,B] to find its null space and then, subsequently, the subspace of possible closed loop eigenvectors and the necessary feedback matrix, K, for the assignment of the specified closed loop eigenvalues and eigenvectors. This paper describes the very attractive computational alternative of using the singular value decomposition rather than the previously reported approach of elementary column operations. The assignment of complex eigenvalues and repeated eigenvalues using the same basic singular value decomposition of a real matrix is also discussed.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"2 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":"121689844","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 two-variable approach to the model, reduction problem with Hankel norm criterion is discussed. The problem is proved to be reducible to obtain a two-variable all-pass rational function, interpolating a set of parametric values at specified points inside the unit circle. A polynomial formulation and the properties of the optimal Hankel norm approximations are then shown to result directly from the general form of the solution of the interpolation problem considered. As a consequence, the recursive Nevanlinna algorithm can be employed and the essential stability properties of the solution can be established with the help of the Nevanlinna matrix [9]. This short paper is meant to briefly summarize the work in the full paper [8], where the reader is referred to for more details.
{"title":"Rational approximations with Hankel-norm criterion","authors":"Y. Genin, S. Kung","doi":"10.1109/CDC.1980.271843","DOIUrl":"https://doi.org/10.1109/CDC.1980.271843","url":null,"abstract":"A two-variable approach to the model, reduction problem with Hankel norm criterion is discussed. The problem is proved to be reducible to obtain a two-variable all-pass rational function, interpolating a set of parametric values at specified points inside the unit circle. A polynomial formulation and the properties of the optimal Hankel norm approximations are then shown to result directly from the general form of the solution of the interpolation problem considered. As a consequence, the recursive Nevanlinna algorithm can be employed and the essential stability properties of the solution can be established with the help of the Nevanlinna matrix [9]. This short paper is meant to briefly summarize the work in the full paper [8], where the reader is referred to for more details.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"15 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":"121723734","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 class of analytic time varying linear systems is considered. Different balanced realizations of such systems are defined, and their existence and properties analyzed. The results are then used to derive reduced order approximations (also for unstable systems). A method is suggested to determine the order of a "good" approximation.
{"title":"On generalized balanced realizations","authors":"E. Verriest, T. Kailath","doi":"10.1109/CDC.1980.271848","DOIUrl":"https://doi.org/10.1109/CDC.1980.271848","url":null,"abstract":"The class of analytic time varying linear systems is considered. Different balanced realizations of such systems are defined, and their existence and properties analyzed. The results are then used to derive reduced order approximations (also for unstable systems). A method is suggested to determine the order of a \"good\" approximation.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"36 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":"124758346","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 control of a stochastic system with unknown parameters is considered. A novel cost function which includes the variance of the innovations process is used to optimize the performance of the system. The cost function has two parts: One that reflects the goal of regulating the output and the second one that reflects the need to gather as much information as possible about the parameters of the system, the latter being represented by the variance of the innovations process. The control law derived has an explicit solution that allows for an easy implementation, and has dual properties. The relationships among the controller obtained in this paper and the certainty equivalence and cautious controllers are analized. Simulation results show the quasi-optimal performance of the new controller.
{"title":"Dual control through innovations","authors":"R. Milito, C. Padilla, R. Padilla, D. Cadorin","doi":"10.1109/CDC.1980.271813","DOIUrl":"https://doi.org/10.1109/CDC.1980.271813","url":null,"abstract":"The control of a stochastic system with unknown parameters is considered. A novel cost function which includes the variance of the innovations process is used to optimize the performance of the system. The cost function has two parts: One that reflects the goal of regulating the output and the second one that reflects the need to gather as much information as possible about the parameters of the system, the latter being represented by the variance of the innovations process. The control law derived has an explicit solution that allows for an easy implementation, and has dual properties. The relationships among the controller obtained in this paper and the certainty equivalence and cautious controllers are analized. Simulation results show the quasi-optimal performance of the new controller.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"64 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":"131487534","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 Probabilistic Data Association (PDA) method, which is based on computing the posterior probability of each candidate measurement found in a validation gate, assumes that only one real target is present and all other measurements are Poisson-distributed clutter. In this paper, some new theoretical results are presented on the Joint Probabilistic Data Association (JPDA) algorithm, in which joint posterior probabilities are computed for multiple targets in Poisson clutter. The algorithm is applied to a passive sonar tracking problem wlth multiple sensors and targets, in which a target is not fully observable from a single sensor. Targets are modeled with four geographic states, two or more acoustic states, and realistic (i.e. low) probabilities of detection at each sample time. Simulation results are presented for two heavily interfering targets; these illustrate the dramatic improvements obtained by computing joint probabilities.
{"title":"Multi-target tracking using joint probabilistic data association","authors":"T. Fortmann, Y. Bar-Shalom, M. Scheffe","doi":"10.1109/CDC.1980.271915","DOIUrl":"https://doi.org/10.1109/CDC.1980.271915","url":null,"abstract":"The Probabilistic Data Association (PDA) method, which is based on computing the posterior probability of each candidate measurement found in a validation gate, assumes that only one real target is present and all other measurements are Poisson-distributed clutter. In this paper, some new theoretical results are presented on the Joint Probabilistic Data Association (JPDA) algorithm, in which joint posterior probabilities are computed for multiple targets in Poisson clutter. The algorithm is applied to a passive sonar tracking problem wlth multiple sensors and targets, in which a target is not fully observable from a single sensor. Targets are modeled with four geographic states, two or more acoustic states, and realistic (i.e. low) probabilities of detection at each sample time. Simulation results are presented for two heavily interfering targets; these illustrate the dramatic improvements obtained by computing joint probabilities.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"26 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":"126269782","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：