Journal Article10.1109/91.890326
Fuzzy model predictive control
TL;DR: A fuzzy model predictive control (FMPC) approach is introduced to design a control system for a highly nonlinear process that avoids extensive online nonlinear optimization and permits the design of a controller based on linear control theory.
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Abstract: A fuzzy model predictive control (FMPC) approach is introduced to design a control system for a highly nonlinear process. In this approach, a process system is described by a fuzzy convolution model that consists of a number of quasi-linear fuzzy implications. In controller design, prediction errors and control energy are minimized through a two-layered iterative optimization process. At the lower layer, optimal local control policies are identified to minimize prediction errors in each subsystem. A near optimum is then identified through coordinating the subsystems to reach an overall minimum prediction error at the upper layer. The two-layered computing scheme avoids extensive online nonlinear optimization and permits the design of a controller based on linear control theory. The efficacy of the FMPC approach is demonstrated through three examples.
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References
Fuzzy identification of systems and its applications to modeling and control
T. Takagi,Michio Sugeno +1 more
- 01 Jan 1985
TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
20.1K
Model predictive control: theory and practice—a survey
TL;DR: The flexible constraint handling capabilities of MPC are shown to be a significant advantage in the context of the overall operating objectives of the process industries and the 1-, 2-, and ∞-norm formulations of the performance objective are discussed.
5.6K
•Book
Process Dynamics and Control
Dale E. Seborg,Thomas F. Edgar,Duncan A. Mellichamp +2 more
- 16 Aug 1989
TL;DR: This book discusses the development of Empirical Models from Process Data, Dynamic Behavior of First-Order and Second-Order Processes, and Dynamic Response Characteristics of More Complicated Processes.
2.4K
Paper: Model predictive heuristic control
TL;DR: In this paper, a new method of digital process control is described, which relies on three principles: 1) the multivariable plant is represented by its impulse responses which will be used on line by the control computer for long range prediction; 2) the behavior of the closed-loop system is prescribed by means of reference trajectories initiated on the actual outputs; 3) the control variables are computed in a heuristic way with the same procedure used in identification, which appears as a dual of the control under this formulation.
2K