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In technical practice often occur higher order processes when a design of an optimal controller leads to complicated control algorithms. One of possibilities of control of such processes is their approximation by lower-order model with time-delay (dead time). The contribution is focused on a choice of a suitable experimental identification method and a suitable excitation input signals for an estimation of process model parameters with time-delay. The further contribution is design of an algorithm for digital control of high-order processes which are approximated by second-order model of the process with time-delay. The designed control algorithms are based on a predictive control strategy. The controller’s algorithm uses the digital modification of the Smith Predictor (SP). The program system MATLAB/SIMULINK was used for simulation verification of these algorithms. INTRODUCTION Some technological processes in industry are characterized by high-order dynamic behaviour or large time constants and time-delays. For control engineering, such processes can often be approximated by the FOTD (first-order-time-delay) model. Timedelay in a process increases the difficulty of controlling it. However using the approximation of higher-order process by lower-order model with time-delay provides simplification of the control algorithms. Let us consider a continuous-time dynamical linear SISO (single input ( ) u t – single output ( ) y t ) system with time-delay d T . The transfer function of a pure transportation lag is d T s e where s is a complex variable. Overall transfer function with time-delay is in the form ( ) ( ) d T s d G s G s e = (1) where ( ) G s is the transfer function without time-delay. Processes with time-delay are difficult to control using standard feedback controllers. When a high performance of the control process is desired or the relative time-delay is very large, a predictive control strategy must be used. The predictive control strategy includes a model of the process in the structure of the controller. The first time-delay compensation algorithm was proposed by (Smith 1957). This control algorithm known as the Smith Predictor (SP) contained a dynamic model of the time-delay process and it can be considered as the first model predictive algorithm. Historically first modifications of time-delay algorithms were proposed for continuous-time (analogue) controllers. On the score of implementation problems, only the discrete versions are used in practice in this time. The digital pole assignment SP was designed using a polynomial approach in (Bobal et al. 2011a). The design of this controller was extended by a method for a choice of a suitable pole assignment of the characteristic polynomial. The designed digital SP was verified by simulation control of the fifth-order system which was identified by a second-order model with time-delay. IDENTIFICATION OF TIME-DELAY PROCESSES In this paper, the time-delay is obtained separately from an off-line identification using the least squares method (LSM). The measured process output ( ) y k is generally influenced by noise. These nonmeasurable disturbances cause errors e in the determination of model parameters and therefore real output vector is in the form = + y FΘ e (2) It is possible to obtain the LSM expression for calculation of the vector of the parameter estimates ( ) 1 ˆ − = T T Θ F F F y (3) The matrix F has dimension (N-n-d, 2n), the vector y (N-n-d) and the vector of parameter model estimates Proceedings 26th European Conference on Modelling and Simulation ©ECMS Klaus G. Troitzsch, Michael Mohring, Ulf Lotzmann (Editors) ISBN: 978-0-9564944-4-3 / ISBN: 978-0-9564944-5-0 (CD) Θ (2n). N is the number of samples of measured input and output data, n is the model order. Equation (3) serves for calculation of the vector of the parameter estimates Θ using N samples of measured input-output data. The individual vectors and matrices in Equations (2) and (3) have the form ( ) ( ) ( ) ( ) ( ) ( )

Identification And Digital Control Of Higher-Order Processes Using Predictive Strategy