VU 325.001 Process Control


Due to the increasingly strict requirements (e.g. with regard to environmental compatibility and energy efficiency), classic linear single-variable control systems are no longer sufficient in many process engineering processes. In addition to the basic lecture, this course teaches advanced methods of control engineering in order to do justice to the special properties of complex process engineering systems.

Root Locus

The root locus method is a graphical method that enables the change of a system's eigenvalues ​​to be displayed as a function of various parameters (e.g. the controller gain). This allows the stability and parameter sensitivity of control loops to be analyzed in a clear manner and the robustness of control systems (e.g. with regard to aging) to be increased.

Advanced methods of process control

Various concepts (disturbance variable switching, cascade control, predictor control, etc.) are presented in order to improve the performance of control systems under different framework conditions (e.g. measurable disturbance or auxiliary variable, dead time, etc.). The importance of these control concepts is illustrated using simple and practical examples.

Multi-variable Control

In many process engineering plants, it is necessary to simultaneously keep several controlled variables at a set value or to guide them along trajectories. The controlled and manipulated variables are often strongly linked, and the desired behavior of the controlled system can only be achieved through coordinated intervention on several actuators. In such cases, i.e. when the coupling between the manipulated and/or controlled variables cannot be neglected, multivariable control systems are used. In this course, the description, decoupling and stability of two-variable systems are considered.

State space systems

The state space representation enables a more formal mathematical approach when designing control systems. In this way, the treatment of complex multivariable systems is formalized and significantly simplified. The controlled system is described by a so-called state space system, the strength of which lies in the fact that multiple-input-multiple-output (MIMO) systems are represented in the same way as single-input-single-output (SISO) systems. The representation in the state space thus enables the design of complex multivariable control systems, such as those used in vehicle and flight control. In addition to the design of state controllers, the use of a state observer is also explained.

Optimal Control

Another chapter covers the controller design through optimization (Linear Quadratic Regulator, LQR). The design goal here is no longer the placement of poles of the closed control loop, but the minimization of a cost function that takes into account the future course of the system state. This methodology enables intuitive access to tune the controller or to achieve the desired control performance. The VU is supported by extensive examples provided as complete MATLAB/Simulink packages. As part of a practical exercise in small groups, the students have the opportunity to test the knowledge they have acquired in laboratory experiments.

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TU Wien

Institute of Mechanics and Mechatronics

Research Unit of Control and Process Automation

Getreidemarkt 9 / E325-04 / 6th floor

1060 Vienna

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