An approximate method for solving optimal control problems

An approximate method is presented for solving optimization problems. A truncated series of exponential functions is used to approximate a desired state variable or output of a system. Integral-square error is used as the criterion to be minimized. The coefficients of the series are determined in su...

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Published inIEEE transactions on automatic control Vol. 9; no. 4; pp. 554 - 556
Main Authors Chang, C., DeRusso, P.
Format Journal Article
LanguageEnglish
Published IEEE 01.10.1964
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ISSN0018-9286
DOI10.1109/TAC.1964.1105764

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Abstract An approximate method is presented for solving optimization problems. A truncated series of exponential functions is used to approximate a desired state variable or output of a system. Integral-square error is used as the criterion to be minimized. The coefficients of the series are determined in such a way that the error index is minimum, subject to the physical constraints imposed on the system. In this way, the dynamic problem is converted into a static one. The resulting problem becomes a general problem of mathematical programming which may be solved either by digital or analog computers. Once the coefficients of the approximating signal are determined, the input control signal is determined through the relationship of the process dynamics of the system. This approximate method of optimization is simple, and possesses the important advantage of requiring less computer time than exact optimization methods.
AbstractList An approximate method is presented for solving optimization problems. A truncated series of exponential functions is used to approximate a desired state variable or output of a system. Integral-square error is used as the criterion to be minimized. The coefficients of the series are determined in such a way that the error index is minimum, subject to the physical constraints imposed on the system. In this way, the dynamic problem is converted into a static one. The resulting problem becomes a general problem of mathematical programming which may be solved either by digital or analog computers. Once the coefficients of the approximating signal are determined, the input control signal is determined through the relationship of the process dynamics of the system. This approximate method of optimization is simple, and possesses the important advantage of requiring less computer time than exact optimization methods.
Author Chang, C.
DeRusso, P.
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Cites_doi 10.1109/TAC.1963.1105534
10.1287/opre.3.2.168
10.1287/opre.3.4.388
10.1093/qjmam/2.4.460
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References brown (ref3) 1957
chang (ref7) 1963
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jackson (ref5) 1956
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  doi: 10.1109/TAC.1963.1105534
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  publication-title: Approximate Optimization of Control Systems Using Operations Research
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  doi: 10.1287/opre.3.2.168
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  doi: 10.1287/opre.3.4.388
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  doi: 10.1093/qjmam/2.4.460
– year: 1956
  ident: ref5
  publication-title: Mathematical Programming by Analog Computer
– year: 1957
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  publication-title: Gradient Methods for the Computer Solution of System'Optimization Problems
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StartPage 554
SubjectTerms Aerodynamics
Analog computers
Computer errors
Control system synthesis
Electric variables control
Nonlinear dynamical systems
Optimal control
Optimization methods
Signal processing
Variable speed drives
Title An approximate method for solving optimal control problems
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