Nonlinear extended output feedback control for CSTRs with van de Vusse reaction

Abstract This paper developed an output-feedback control system for regulation of continuous stirred tank reactors (CSTRs) with van de Vusse reaction. The reactors are often used as benchmark representatives of nonminimum-phase processes. Control of such nonlinear processes is difficult because they exhibit the inverse response. Linear controllers usually give unsatisfactory results in this case and thus nonlinear control approaches are more suitable. The proposed control system consists of a nonlinear observer and an extended nonlinear state feedback controller. The extension consists in adding the integrator to the controller for improving steady state performance of the control system. Stability of the control system including the observer dynamics is guaranteed, thanks to the existence of an input-to-state Lyapunov function. Simulation studies are conducted to illustrate the effectiveness of the proposed control system and its robustness.

[1]  J. V. Vusse.  Plug-flow type reactor versus tank reactor , 1964 .

[2]  Veit Hagenmeyer,et al.  Design of adaptive feedforward control under input constraints for a benchmark CSTR based on a BVP solver , 2009, Comput. Chem. Eng..

[3]  Piotr M. Marusak,et al.  Advantages of an easy to design fuzzy predictive algorithm in control systems of nonlinear chemical reactors , 2009, Appl. Soft Comput..

[4]  Santosh Devasia,et al.  Output tracking between operating points for nonlinear processes: Van de Vusse example , 2002, IEEE Trans. Control. Syst. Technol..

[5]  B. Srinivasan,et al.  Real‐time optimization of dynamic systems using multiple units , 2007 .

[6]  Francis J. Doyle,et al.  Efficient optimization approaches to nonlinear model predictive control , 2003 .

[7]  Wei Wu.  Adaptive nonlinear control of nonminimum-phase processes , 1999 .

[8]  Chyi-Tsong Chen,et al.  Design of a sliding mode control system for chemical processes , 2005 .

[9]  Ronald K. Pearson,et al.  Nonlinear model-based control using second-order Volterra models , 1995, Autom..

[10]  Eduardo Sontag,et al.  On characterizations of the input-to-state stability property , 1995 .

[11]  Sebastian Engell,et al.  Gain-scheduling trajectory control of a continuous stirred tank reactor , 1998 .

[12]  Ralf Rothfuß,et al.  Flatness based control of a nonlinear chemical reactor model , 1996, Autom..

[13]  B. Wayne Bequette,et al.  Computationally efficient neural predictive control based on a feedforward architecture , 2006 .

[14]  Costas Kravaris,et al.  Optimal controller tuning for nonlinear processes , 2005, Autom..

[15]  Lorenz T. Biegler,et al.  Optimization approaches to nonlinear model predictive control , 1991 .

[16]  Hannu T. Toivonen,et al.  A neural network model predictive controller , 2006 .

[17]  Veit Hagenmeyer,et al.  Comparative evaluation of nonlinear model predictive and flatness-based two-degree-of-freedom control design in view of industrial application , 2007 .

[18]  Yun Li,et al.  Nonparametric nonlinear model predictive control , 2004 .

[19]  Wei Wu,et al.  Stable inverse control for nonminimum-phase nonlinear processes , 1999 .

[20]  Shun-Hung Tsai,et al.  Robust H∞ control for Van de Vusse reactor via T-S fuzzy bilinear scheme , 2011, Expert Syst. Appl..