The problem of designing a controller to render a class of nonlinear systems simultaneously noninteractive with asymptotically stable fixed modes is addressed. It is shown that for a nonlinear system with unstable fixed dynamics (nonminimum phase system) where there exists no dynamic state feedback which could render the closed-loop system noninteractive and stable, a high-gain dynamic control strategy exists which achieves noninteraction with stability. The use of this control strategy introduces a two-time scale in the closed-loop system dynamics. By utilizing the concept of integral manifold, an exact reduced order model of the full order system is obtained. Using this, it is possible to design a dynamic state feedback controller which would simultaneously result in asymptotically stable fixed dynamics and a noninteracting input/output map.<>
{"title":"Nonlinear noninteracting control with stability: a high gain control approach","authors":"K. Khorasani","doi":"10.1109/CDC.1990.203423","DOIUrl":"https://doi.org/10.1109/CDC.1990.203423","url":null,"abstract":"The problem of designing a controller to render a class of nonlinear systems simultaneously noninteractive with asymptotically stable fixed modes is addressed. It is shown that for a nonlinear system with unstable fixed dynamics (nonminimum phase system) where there exists no dynamic state feedback which could render the closed-loop system noninteractive and stable, a high-gain dynamic control strategy exists which achieves noninteraction with stability. The use of this control strategy introduces a two-time scale in the closed-loop system dynamics. By utilizing the concept of integral manifold, an exact reduced order model of the full order system is obtained. Using this, it is possible to design a dynamic state feedback controller which would simultaneously result in asymptotically stable fixed dynamics and a noninteracting input/output map.<<ETX>>","PeriodicalId":287089,"journal":{"name":"29th IEEE Conference on Decision and Control","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128325888","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 continuity of the behavior of dynamical systems is studied as a function of the parameters in their behavioral equations. The problem is motivated by an RLC circuit whose port behavior exhibits a discontinuity as a function of the numerical values of the elements in the circuit. The main result is that a system described by difference equations involving manifest (external) and latent (internal) variables will have a continuous behavior in the limit if the limit system is observable.<>
{"title":"Continuity of latent variable models","authors":"J. Willems, J. Nieuwenhuis","doi":"10.1109/CDC.1990.203519","DOIUrl":"https://doi.org/10.1109/CDC.1990.203519","url":null,"abstract":"The continuity of the behavior of dynamical systems is studied as a function of the parameters in their behavioral equations. The problem is motivated by an RLC circuit whose port behavior exhibits a discontinuity as a function of the numerical values of the elements in the circuit. The main result is that a system described by difference equations involving manifest (external) and latent (internal) variables will have a continuous behavior in the limit if the limit system is observable.<<ETX>>","PeriodicalId":287089,"journal":{"name":"29th IEEE Conference on Decision and Control","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128455554","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 nonlinear least squares parameter estimation procedure is discussed. The main objective is to extend previous results in order to obtain certain functional invariance theorems. In particular, weak convergence methods are used to prove an asymptotic normality result and Strassen's invariance principle is applied to establish a law of the iterated logarithm. Some examples are presented.<>
{"title":"Invariance principles and applications to distributed parameter identification","authors":"G. Yin, B. Fitzpatrick","doi":"10.1109/CDC.1990.203486","DOIUrl":"https://doi.org/10.1109/CDC.1990.203486","url":null,"abstract":"A nonlinear least squares parameter estimation procedure is discussed. The main objective is to extend previous results in order to obtain certain functional invariance theorems. In particular, weak convergence methods are used to prove an asymptotic normality result and Strassen's invariance principle is applied to establish a law of the iterated logarithm. Some examples are presented.<<ETX>>","PeriodicalId":287089,"journal":{"name":"29th IEEE Conference on Decision and Control","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128639838","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 authors propose a learning scheme using feedback-error-learning for a neural network model applied to adaptive nonlinear feedback control. After the neural network compensates perfectly or partially for the nonlinearity of the controlled object through learning, the response of the controlled object follows the desired set in the conventional feedback controller. This learning scheme does not require the knowledge of the nonlinearity of a controlled object in advance. Using the proposed approach, the actual responses after learning correspond to desired responses. When the desired response in Cartesian space is required, learning impedance control is derived. The convergence properties of the neural networks are provided by the averaged equation and Lyapunov method. Simulation results on this learning approach are presented. The proposed scheme can be used for many kinds of controlled objects, such as chemical plants, machines, and robots.<>
{"title":"Learning control for a closed loop system using feedback-error-learning","authors":"H. Gomi, M. Kawato","doi":"10.1109/CDC.1990.203403","DOIUrl":"https://doi.org/10.1109/CDC.1990.203403","url":null,"abstract":"The authors propose a learning scheme using feedback-error-learning for a neural network model applied to adaptive nonlinear feedback control. After the neural network compensates perfectly or partially for the nonlinearity of the controlled object through learning, the response of the controlled object follows the desired set in the conventional feedback controller. This learning scheme does not require the knowledge of the nonlinearity of a controlled object in advance. Using the proposed approach, the actual responses after learning correspond to desired responses. When the desired response in Cartesian space is required, learning impedance control is derived. The convergence properties of the neural networks are provided by the averaged equation and Lyapunov method. Simulation results on this learning approach are presented. The proposed scheme can be used for many kinds of controlled objects, such as chemical plants, machines, and robots.<<ETX>>","PeriodicalId":287089,"journal":{"name":"29th IEEE Conference on Decision and Control","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129338824","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 following three problems are addressed: (1) multiple-input single-output heteroassociative networks for signal validation for distributed sensor arrays; (2) multiple-input multiple-output autoassociative networks for plant-wide monitoring of a set of process variables for diagnostics; and (3) artificial neural networks for online estimation of chemical composition from spectroscopy data. Both static and dynamic forms of the backpropagation network (BPN) have been developed and applied to the solution of these problems. Chemometric data from Raman FT (Fourier transform) spectroscopy was used to estimate chemical sample composition. Several features of network training and implementations are presented, including adaptive updating of the sigmoidal threshold function during training, an optimal choice of hidden layer nodes using Shannon's information theory approach, and automatic scaling of network inputs and outputs for data encoding and decoding. The details of the development and implementation of the multilayer perceptrons and applications to industrial problems are highlighted.<>
{"title":"Development and application of neural network algorithms for process diagnostics","authors":"B. Upadhyaya, G. Mathai, E. Eryurek","doi":"10.1109/CDC.1990.203401","DOIUrl":"https://doi.org/10.1109/CDC.1990.203401","url":null,"abstract":"The following three problems are addressed: (1) multiple-input single-output heteroassociative networks for signal validation for distributed sensor arrays; (2) multiple-input multiple-output autoassociative networks for plant-wide monitoring of a set of process variables for diagnostics; and (3) artificial neural networks for online estimation of chemical composition from spectroscopy data. Both static and dynamic forms of the backpropagation network (BPN) have been developed and applied to the solution of these problems. Chemometric data from Raman FT (Fourier transform) spectroscopy was used to estimate chemical sample composition. Several features of network training and implementations are presented, including adaptive updating of the sigmoidal threshold function during training, an optimal choice of hidden layer nodes using Shannon's information theory approach, and automatic scaling of network inputs and outputs for data encoding and decoding. The details of the development and implementation of the multilayer perceptrons and applications to industrial problems are highlighted.<<ETX>>","PeriodicalId":287089,"journal":{"name":"29th IEEE Conference on Decision and Control","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124608959","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 authors propose a controller design method for coordinative manipulation by multiple robot manipulators in which both the dynamics of the manipulator and the dynamics of the object are taken into account. In particular, the vector defined from the contact points between the object and the manipulators is adopted to express the orientation of the object globally. The proposed control system achieves the object trajectory tracking with appropriate grasping forces. Furthermore, a method for controlling the posture of the manipulators by using kinematic redundancy subject to the coordinative manipulation is presented.<>
{"title":"Controller design for coordinative manipulation by multiple manipulators","authors":"T. Sugie, K. Kumashita, T. Ono","doi":"10.1109/CDC.1990.203362","DOIUrl":"https://doi.org/10.1109/CDC.1990.203362","url":null,"abstract":"The authors propose a controller design method for coordinative manipulation by multiple robot manipulators in which both the dynamics of the manipulator and the dynamics of the object are taken into account. In particular, the vector defined from the contact points between the object and the manipulators is adopted to express the orientation of the object globally. The proposed control system achieves the object trajectory tracking with appropriate grasping forces. Furthermore, a method for controlling the posture of the manipulators by using kinematic redundancy subject to the coordinative manipulation is presented.<<ETX>>","PeriodicalId":287089,"journal":{"name":"29th IEEE Conference on Decision and Control","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124641303","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 problem of matching a given input-output behavior for systems described by general nonlinear differential equations is considered. The paper complements previous work on the exact matching of a model for nonlinear systems. In particular, the authors investigate the case where the initial state does not belong to the appropriate submanifold of the state space where strong model matching is achieved. Conditions for the solvability of the asymptotic model matching are proposed.<>
{"title":"Asymptotic nonlinear model matching","authors":"R. Castro, M. D. Di Benedetto","doi":"10.1109/CDC.1990.203428","DOIUrl":"https://doi.org/10.1109/CDC.1990.203428","url":null,"abstract":"The problem of matching a given input-output behavior for systems described by general nonlinear differential equations is considered. The paper complements previous work on the exact matching of a model for nonlinear systems. In particular, the authors investigate the case where the initial state does not belong to the appropriate submanifold of the state space where strong model matching is achieved. Conditions for the solvability of the asymptotic model matching are proposed.<<ETX>>","PeriodicalId":287089,"journal":{"name":"29th IEEE Conference on Decision and Control","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129444998","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 nonlinear adaptive state feedback input-output linearizing control is designed for a fifth-order model of an induction motor which includes both electrical and mechanical dynamics under the assumptions of linear magnetic circuits. The control algorithm contains a nonlinear identification scheme which asymptotically tracks the true values of the load torque and rotor resistance, which are assumed to be constant but unknown. Once those parameters are identified, the two control goals of regulating rotor speed and rotor flux amplitude are decoupled. Full state measurements are required. Preliminary simulations show that a good performance is maintained when flux signals are provided to the adaptive control algorithm.<>
{"title":"Adaptive partial feedback linearization of induction motors","authors":"R. Marino, S. Peresada, P. Valigi","doi":"10.1109/CDC.1990.203408","DOIUrl":"https://doi.org/10.1109/CDC.1990.203408","url":null,"abstract":"A nonlinear adaptive state feedback input-output linearizing control is designed for a fifth-order model of an induction motor which includes both electrical and mechanical dynamics under the assumptions of linear magnetic circuits. The control algorithm contains a nonlinear identification scheme which asymptotically tracks the true values of the load torque and rotor resistance, which are assumed to be constant but unknown. Once those parameters are identified, the two control goals of regulating rotor speed and rotor flux amplitude are decoupled. Full state measurements are required. Preliminary simulations show that a good performance is maintained when flux signals are provided to the adaptive control algorithm.<<ETX>>","PeriodicalId":287089,"journal":{"name":"29th IEEE Conference on Decision and Control","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129687107","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 authors designed an H/sup infinity / controller and applied it to control a pneumatic rubber artificial muscle named Rubbertuator whose dynamics is highly nonlinear. Some simple experiments show the effectiveness of the controller. A short description of the experiments is provided.<>
{"title":"H/sup infinity / control of a certain nonlinear actuator","authors":"K. Osuka, T. Kimura, T. Ono","doi":"10.1109/CDC.1990.203616","DOIUrl":"https://doi.org/10.1109/CDC.1990.203616","url":null,"abstract":"The authors designed an H/sup infinity / controller and applied it to control a pneumatic rubber artificial muscle named Rubbertuator whose dynamics is highly nonlinear. Some simple experiments show the effectiveness of the controller. A short description of the experiments is provided.<<ETX>>","PeriodicalId":287089,"journal":{"name":"29th IEEE Conference on Decision and Control","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129884931","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 manufacturing facility is considered in which there are m identical machines that are equally capable of producing n distinct part types. The production must be scheduled to meet a deterministic demand. However, the machines are subject to a Markovian breakdown and repair process. The paper simplifies the problem by using the hierarchical structure that is inherent in this model. Thus, it is assumed that the time scale in which the machine breakdown and repair process is occurring is much shorter than the time scale in which the costs are being evaluated. That is, both the holding cost function and the discount rate are small, and the demand is nearly constant. A general limiting procedure is presented for manufacturing systems in the presence of an hierarchy between the time scales of certain events. This procedure is described in an example with two parts and two machines. Also, a convergence rate is obtained for a simpler problem.<>
{"title":"Asymptotic analysis of manufacturing systems","authors":"H. Soner","doi":"10.1109/CDC.1990.203660","DOIUrl":"https://doi.org/10.1109/CDC.1990.203660","url":null,"abstract":"A manufacturing facility is considered in which there are m identical machines that are equally capable of producing n distinct part types. The production must be scheduled to meet a deterministic demand. However, the machines are subject to a Markovian breakdown and repair process. The paper simplifies the problem by using the hierarchical structure that is inherent in this model. Thus, it is assumed that the time scale in which the machine breakdown and repair process is occurring is much shorter than the time scale in which the costs are being evaluated. That is, both the holding cost function and the discount rate are small, and the demand is nearly constant. A general limiting procedure is presented for manufacturing systems in the presence of an hierarchy between the time scales of certain events. This procedure is described in an example with two parts and two machines. Also, a convergence rate is obtained for a simpler problem.<<ETX>>","PeriodicalId":287089,"journal":{"name":"29th IEEE Conference on Decision and Control","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129892797","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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