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The kinetics of bioreactions often involve some uncertainties and the dynamics of the process vary during the course of fermentation. For such processes, conventional control schemes may not provide satisfactory control performance and demands extra effort to design advanced control schemes. In this study, a dynamic fuzzy model based predictive controller (DFMBPC) is presented for the control of a biochemical reactor. The DFMBPC incorporates an adaptive fuzzy modeling framework into a model based predictive control scheme to derive analytical controller output. The DFMBPC has the¯exibility to opt with various types of fuzzy models whose choice also lead to improve the control performance. The performance of DFMBPC is evaluated by comparing with a fuzzy model based predictive controller (FMBPC) with no model adaptation and a conventional PI controller. The results show that DFMBPC provides better performance for tracking setpoint changes and rejecting unmeasured disturbances in the biochemical reactor. IntroductionBiochemical processes offer a considerable scope for the application of fuzzy techniques in modelling, simulation and control. Bioprocesses are dif®cult to control because the kinetics of bioreactions often involve some uncertainties and the dynamics of the process vary during the course of fermentation. For such processes, conventional control schemes may not provide satisfactory control performance and demands extra effort to design advanced control schemes. In recent years, there has been a strong and growing interest in the application of fuzzy controllers to nonlinear chemical processes. Most of the fuzzy controllers reported in literature are rule based controllers [1±3]. These controllers consist of a set of heuristic control rules, each of which speci®es a certain control action to be taken, depending on the actual state of the process. One of the disadvantages of this control scheme is that signi®cant knowledge engineering effort is required for setting a complete and consistent rule base. The other type of fuzzy controllers that have been employed for nonlinear chemical processes in recent years are relational model based controllers [4,5]. Fuzzy relational models are nonlinear models which are concerned with identifying the relationships between the combinations of fuzzy input reference sets and the resulting fuzzy outputs. Relational model based controllers perform well for systems with a small number of variables. With more process variables, the size of the relational model increases and demands more computing requirements. There is no simple approach to derive the controller output from the relational model and generally numerical optimization search techniques are to be resorted. Establishing a dynamic model from the input-output data of the plant and employing such a model for advanced control strategy such as model predictive control has always been an important concern for process engineers. Model based predictive control is a powerful control strategy which has been widely accepted in...

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