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The IUP Journal of Electrical and Electronics Engineering:
Modern Controller Design for an Unstable Bioreactor Process
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Bioreactors are used in many applications including industries concerned with food, beverages and pharmaceuticals. The objective of the paper is to design Linear Quadratic Regulator (LQR) control techniques for a class of unstable bioreactor process. Controller is designed to meet the desired time domain specifications such as rise time, settling time, maximum overshoot and stability margins. A comparative analysis based upon the systemís response characteristics of pole placement controller and LQR controller is presented. Simulation study has been done in MATLAB Simulink environment shows that LQR is better than pole placement controller. The results are also compared with the conventional controller.

 
 

Systems with structural and performance complexities require sophisticated controller design techniques. The performance of a control system can be represented by integral performance measures, the design of a system must be based on minimizing the performances index. Adaptive controllers have been used widely in bioprocesses, however, the control system design is not straightforward (Bastin and Dochain, 1990). There are different methods or procedures to control the bioreactor process. One of them is the pole placement controller having the advantage of giving a much clearer linkage between adjusted parameters and the resulting changes in controller behavior. However, one disadvantage with this method is that the placing of the poles at desired locations will lead to high gains. The first approach is based on frequency domain description and known as spectral factorization. In this method, the control law is obtained via solving an operator Diophantine equation (Callier and Winkin, 1990). State space model of a bioreactor is analyzed and its various operating regions are given by Wayne (1999). Sliding-mode observers are proposed by Jesus et al. (2009) for the estimation of specific growth rate and substrate concentration from biomass measurements in fermentation processes in which global convergence is demonstrated using Lyapunov stability theory.

 
 
 

Linear Quadratic Regulator (LQR) controller, Pole placement controller, Algebraic Riccati Equation (ARE), PID controller