Process Modelling, Identification, and ControlSpringer Science & Business Media, 30 cze 2007 - 480 Control and automation in its broadest sense plays a fundamental role in process industries. Control assures stability of technologies, disturbance - tenuation, safety of equipment and environment as well as optimal process operation from economic point of view. This book intends to present modern automatic control methods and their applications in process control in p- cess industries. The processes studied mainly involve mass and heat transfer processes and chemical reactors. It is assumed that the reader has already a basic knowledge about c- trolled processes and about di?erential and integral calculus as well as about matrixalgebra.Automaticcontrolproblemsinvolvemathematicsmorethanit is usual in other engineering disciplines. The book treats problems in a similar way as it is in mathematics. The problem is formulated at ?rst, then the t- orem is stated. Only necessary conditions are usually proved and su?ciency is left aside as it follows from the physical nature of the problem solved. This helps to follow the engineering character of problems. The intended audience of this book includes graduate students but can also be of interest to practising engineers or applied scientists. |
Spis treści
1 | |
Mathematical Modelling of Processes | 13 |
Analysis of Process Models | 51 |
Dynamical Behaviour of Processes | 116 |
DiscreteTime Process Models | 189 |
Process Identification | 221 |
The Control Problem and Design of Simple Controllers | 252 |
Optimal Process Control | 297 |
Predictive Control | 402 |
Adaptive Control | 445 |
465 | |
474 | |
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approach assume calculated called characteristic closed-loop system coefficients Consider constant constraints continuous continuous-time control design control error control law controlled system corresponding cost function defined definite density derivative described determined deviation diagram difference differential equation discrete-time disturbance dynamical equal estimation Example exists feedback control final flow follows frequency gain given gives heat exchanger holds identification implemented initial conditions input integral Laplace transform linear liquid manipulated matrix measured method numerator observer obtained optimal control order system output parameters PID controller pole polynomial positive possible predictive problem process control process model Program properties random variable realised relation sampling second order setpoint shown in Fig shows signal solution specified stable state-space steady-state step response tank temperature term Theory trajectory transfer function unit variable vector written yields zero