Journal Article10.1002/AIC.690340413
Nonlinear inferential control
99
TL;DR: In this article, the authors present the structure of NLIC and the manner in which it is applied to processes when the controlled variables are measured, and the improvement in the process control using NLIC.
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Abstract: Nonlinear inferential control (NLIC) has been developed as a method for improving control of nonlinear systems. The controller is modelbased, and allows for direct use of available measurements. This paper presents the structure of NLIC and the manner in which it is applied to processes when the controlled variables are measured. Also described is the improvement in the process control using NLIC. Two illustrative examples are presented, a laboratory heat exchanger process and a simulated neutralization process. The results indicate that a substantial improvement in control is possible using NLIC.
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References
Nonlinear Inferential Control of Reactor Effluent Concentration from Temperature and Flow Measurements
J. Parrish,Coleman B. Brosilow +1 more
- 18 Jun 1986
TL;DR: In this paper, a nonlinear inferential control (NLIC) was used to improve control of a simulated reactor process, and the results showed a substantial improvement in control over that achieved with PI control.
17
Global transformations of nonlinear systems
TL;DR: In this paper, a technique for constructing a transformation under the assumption that {g\ldot[f\dotg],...,(adn-1}f\ldotsg)} span an n -dimensional space and that the set is an involutive set.
Nonlinear state feedback synthesis by global input/output linearization
Costas Kravaris,Chang-Bock Chung +1 more
TL;DR: In this article, the design of feedback controllers for trajectory tracking in single-input/single-output nonlinear systems is studied, and a nonlinear transformation of the form v = k (x) + λ(x) u that transforms this nonlinear input/output system into a linear system is first constructed.
Inferential control of processes: Part I. Steady state analysis and design
Babu Joseph,Coleman B. Brosilow +1 more
TL;DR: In this article, the authors present a static estimator which infers unmeasurable product qualities from secondary measurements, and the secondary measurements are selected so as to minimize the number of such measurements required to obtain an accurate estimate which is insensitive to modeling errors.