7. Adaptive control
 
The control algorithms that are discussed in the feedback control system sections are tuned based on either time invariant models or trial and error techniques. This poses serious problem in case of fermentation processes since these processes are highly nonlinear systems with poorly understood dynamics and time - varying parameters and a linear controller with constant tuning parameters may not be suitable for it. This situation necessitates a need for changing the controller tuning parameters to achieve satisfactory system performance. An obvious approach to fulfill this need is to adapt the controller based on the present operating conditions. This approach is appropriately known as adaptive control.

As already indicated, in adaptive control system, the controller learns about the process by acquiring data from the process and keeps on updating the control model. A parameter estimator monitors the process and estimates the process dynamics in terms of the parameters of a previously defined mathematical model of the process. A control design algorithm is then used to generate controller coefficients from these estimates, and the controller  sends  the required control signals to the devices controlling the process. An extremely important feature of an adaptive controller is the structure of the model used by the parameter estimator to analyze estimates of process dynamics. The process can be described by a set of mass balance equations, whose quantities can be measured directly or indirectly. It is suggested to look into references 20 and 21 for theoretical considerations in adaptive control.