Process Modelling, Identification, and Control
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.
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Mathematical Modelling of Processes
Analysis of Process Models
Dynamical Behaviour of Processes
DiscreteTime Process Models
The Control Problem and Design of Simple Controllers
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adaptive control algorithm assume asymptotically Automatic Control BIBO stability block scheme calculated closed-loop system coefficients Consider constant constraints continuous-time system control design control law controlled process controlled system coprime corresponding cost function covariance matrix CSTR defined derivative deviation variables differential equation Diophantine equation discrete-time dynamical estimation Example feedback control flow rate follows frequency G(jw given heat exchanger identification initial conditions input variables integral Laplace transform linearised liquid LQ control manipulated variable mathematical model MATLAB method minimisation multivariable observer optimal control optimisation output variable parametrisation PID controller predictive control problem process control process model random variable reactor realised recursive Riccati equation Runge–Kutta method sampling second order system setpoint shown in Fig signal Simulink singlevariable solution specific heat capacity stabilising stable state-space model step change step response stochastic process tank temperature Theorem trajectory vector yields zero