By Robert Haber
Describing the rules and functions of single-input, single-output and multivariable predictive regulate in an easy and vigorous demeanour, this functional e-book additionally considers such matters because the dealing with of on-off keep an eye on, nonlinearities and decoupling difficulties. It provides instructions and strategies for decreasing the computational call for for real-time applications.With its many numerical examples and several other case stories (incl. injection molding laptop and waste water remedy) and commercial purposes (stripping column, distillation column, furnace) this can be worthwhile interpreting for college kids and engineers who objective to appreciate and follow predictive regulate in a wide selection of approach engineering program areas.From the Contents:predictive on-off controlpredictive keep an eye on of single-variable processespredictive keep an eye on of multivariable processesnonlinear predictive controlpredictive PI(D) controlcase studiesindustrial applicationspractical features and a few destiny developments
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Additional resources for Predictive Control in Process Engineering: From the Basics to the Applications
The forced response is a linear function of the manipulated variable sequence in the future horizon. A quadratic cost function of the manipulated signal sequence can be minimized analytically without iteration if there are no constraints. 3 summarizes the cases mentioned. 12 shows the general structure of a predictive controller. If the minimization of the cost function leads to an analytical solution, then the control algorithm is a difference equation like with PID control. 3 Comparison of the minimization algorithms.
1952) On the automatic control of generalized passive systems. Transactions ASME, 74, 175–185. S. (1987) Generalized predictive control. Part I. The basic algorithm. Automatica, 23(2), 137–148. 3 Kuhn, U. (1995) Eine praxisnahe Einstellregel für PID-Regler: Die TSummenregel (A practical tuning rule for PID controllers: the T-sum rule). Automatisierungstechnische Praxis, 37(5), 10–16. 29 2 Linear SISO Model Descriptions The system model gives a description of the behavior of a system. Modeling considers some important viewpoints on how to handle the process, and ignores some others which at this time are not at the forefront of interest.
5 2s C 22 s 2 where s denotes the Laplace operator. 01. 2. Integrating process: Process parameters: integrating time constant TI D 2 s and time constant T1 D 1 s: G(s) D 1 . 01. 4 Control of different linear processes. No. Open-loop step response 1. Oscillating h Set value and controlled signal y h 1 1 0 2 4 6 h 0 2 4 6 8 10 t [s] 2 3 4 5 t [s] 2 3 4 5 t [s] 2 3 4 5 t [s] y h 1 1 yr y 0 0 1 2 3 4 5 t [s] 0 1 y h 1 1 yr h 0 0 0 4. Unstable 10 t [s] 8 0 3. Inverse response y 0 0 2. Integrating yr 1 h 2 3 4 5 t [s] 1 y h 1 0 y 1 yr y 0 0 0 1 2 3 4 5 t [s] 0 1 3.
Predictive Control in Process Engineering: From the Basics to the Applications by Robert Haber