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JournalISSN: 0536-1524

IEEE Transactions on Applications and Industry 

Institute of Electrical and Electronics Engineers
About: IEEE Transactions on Applications and Industry is an academic journal. The journal publishes majorly in the area(s): Control system & Adaptive control. It has an ISSN identifier of 0536-1524. Over the lifetime, 115 publications have been published receiving 911 citations.

Papers published on a yearly basis

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Journal ArticleDOI
TL;DR: A general and unique approach to the analysis and synthesis of control systems in the parameter plane is presented which may be advantageously applied to the design of linear continuous systems, sampled-data systems, nonlinear systems, and systems with distributed parameters.
Abstract: A general and unique approach to the analysis and synthesis of control systems in the parameter plane is presented which may be advantageously applied to the design of linear continuous systems, sampled-data systems, nonlinear systems, and systems with distributed parameters This paper is concerned with the application of the method to linear continuous systems As a simple and rapid procedure for factoring characteristic polynomials in the parameter plane, the proposed method permits the designer to maintain control over salient characteristics of both transient and frequency responses The introduction of the Chebyshev functions greatly facilitates the procedure and makes it suitable for simulation on either analog or digital computers In the design procedure, all graphical and analytical operations are performed in the real domain

65 citations

Journal ArticleDOI
TL;DR: The purpose of this paper is to develop a fairly general approach to adaptive control systems which, at the same time, can be mechanized in a reasonable fashion.
Abstract: The purpose of this paper is to develop a fairly general approach to adaptive control systems which, at the same time, can be mechanized in a reasonable fashion. The development is carried out initially for a simple system with a linear time variant physical process and then extended to the general linear time variant case. Examples are given. A combined learning model and model referenced time variant system is considered next. Finally, the extension of this adaptive concept to control systems which have nonlinear time variant physical processes is presented. Numerous practical implications of the theory are developed in such fields as aerospace vehicle guidance and control, process control, etc. For instance, in re-entry eters of the controlled vehicle vary by two orders of magnitude, while the vehicle dynamic response must remain relatively constant.

53 citations

Journal ArticleDOI
TL;DR: In this paper, the optimal design of sampleddata systems with quantized control signals is considered and a design compromise between the extremes of proportional control systems and relay control systems is achieved.
Abstract: Optimum design of sampleddata systems with quantized control signals is considered. The control signals m i (k) are members of a set [- CL , ... - C2 , - C1 , 0, C 1 , C 2 , ... C L ] where c i ≫ 0, i= 1, 2,... L, and C k ≫C j for k≫j. By selecting a proper set [c i ], a design compromise between the extremes of proportional control systems and relay control systems is achieved. Dynamic programming techniques are used in carrying out the optimum design. Both minimum summed-square-error systems and minimum -N systems are discussed and illustrated for dynamic performance.

41 citations

Journal ArticleDOI
TL;DR: In this paper, an extension of the Lur'e stability function to a class of discrete-time systems which may contain saturation-type nonlinear elements is presented which demonstrate the improved stability inequalities available from this function.
Abstract: This paper presents an extension of the Lur'e stability function to a class of discrete-time systems which may contain saturation-type nonlinear elements. Examples are presented which demonstrate the improved stability inequalities available from this function.

37 citations

Journal ArticleDOI
TL;DR: This work has shown that when the state of a control system is represented as a pattern, learning to make the control decisions actually becomes the same as learning to classify the patterns.
Abstract: Adaptive or self-optimizing systems utilize feedback principles to achieve automatic performance optimization. These principles have been applied to both control systems and adaptive logic structures. The Adaline (adaptive linear threshold element) is essentially the same as an adaptive sampled-data system with quantized input and output signals. A digital controller made of adaptive neurons comprises a pattern-recognizing control system. When the state of a control system is represented as a pattern, learning to make the control decisions actually becomes the same as learning to classify the patterns.

36 citations

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Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
19901
196466
196347
19621