Design and experimental validation of control strategies for commercial gas preheating systems

Heating gases with a time-varying mass flow is a common task in many applications in process engineering. Despite the distributed parameter characteristics of gas preheaters, state-of-the-art controllers for such devices rely on the usage of a heuristically tuned PID control structure (proportional, integrating, differentiating) with built-in limitations of the actuator operating range. These actuator constraints are caused by the facts that an active cooling of gases is commonly not possible and that the maximum heating power is typically limited. The same may hold for underlying constraints on the admissible variation rates of the gas temperatures. However, the use of PID controllers may lead to an unsatisfactory control behavior if an application with rapid changes between different operating points is desired. In such cases, actuator constraints may become active if they are not explicitly accounted for by a suitable trajectory planning procedure. The activation of these constraints then leads to an integrator windup with its well-known performance degradation and risk of instability. For this reason, a modelbased anti-windup strategy is presented in this paper, which can be implemented by means of the internal model control principle. It can be used in combination with state feedback controllers in a cascaded architecture to significantly improve the system's capabilities for avoiding an integrator windup and for tracking predefined temperature trajectories which describe a smooth transition between different steady-state operating points.

[1]  B. Ross Barmish,et al.  New Tools for Robustness of Linear Systems , 1993 .

[2]  Carlos E. Garcia,et al.  Internal model control. A unifying review and some new results , 1982 .

[3]  Andreas Rauh,et al.  Observer-based predictive temperature control for distributed heating systems based on the method of integrodifferential relations , 2012, 2012 17th International Conference on Methods & Models in Automation & Robotics (MMAR).

[4]  Alex Zheng,et al.  Anti-windup design for internal model control , 1994 .

[5]  H. Aschemann,et al.  A differential-algebraic approach for robust control design and disturbance compensation of finite-dimensional models of heat transfer processes , 2013, 2013 IEEE International Conference on Mechatronics (ICM).

[6]  Johan Efberg,et al.  YALMIP : A toolbox for modeling and optimization in MATLAB , 2004 .

[7]  Andreas Rauh,et al.  A new procedure for the design of iterative learning controllers using a 2D systems formulation of processes with uncertain spatio-temporal dynamics , 2013 .

[8]  Jos F. Sturm,et al.  A Matlab toolbox for optimization over symmetric cones , 1999 .

[9]  Tore Hägglund,et al.  Automatic Tuning of Pid Controllers , 1988 .

[10]  Andreas Rauh,et al.  Verified Stability Analysis for Interval-Based Sliding Mode and Predictive Control Procedures with Applications to High-Temperature Fuel Cell Systems , 2013, NOLCOS.

[11]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .