## 1 Introduction
### Existing Solutions and Literature Review  
Over the last several decades, enormous development has occurred in the area of Automatic Control [8]. Revelations in Control Theory and progress in the development of equipment for the realisation of control algorithms have been immense. However, advanced control algorithms have not yet found a place in modern commercial bioreactor control systems. Commercial bioreactors are still equipped primarily with simple closed-loop control systems, or, in many cases, only with open-loop control systems. Regulatory structures based on an open-loop control strategy are not appropriate for accounting for cell and media variations present in real industrial cases. The bioreactor should be able to provide an output to specified process parameter control elements to correct for any deviation in the value of these parameters from the user-defined set point [9]. Therefore, most bioreactors use feedback control loops, but only with simple Proportional-Integral-Derivative (PID) controllers to maintain environmental variables (liquid temperature, dissolved oxygen concentration, and pH) at the required set-points. This simple regulatory structure is popular, due to simple realisation and the availability of accurate, reliable, and cheap sensors to measure physiological variables, such as growth rate, cell density, and product concentration. There are still many variables, however, for which on-line measurements are not available. This is the reason that many of the key process variables are limited to off-line analysis. Thus, advances in on-line measurement technology have resulted in model-based control strategies offering the potential for improved bioreactor performance [10]. On the other hand, there are many academic publications which deal with the application of advanced control theories for bioreactors' control. In one review paper [11], the current status of the efficient control strategies for bioreactor systems is investigated, together with the related areas of sensor technology, optimisation, and state and parameter estimation. Reference [12] presents the design and analysis of adaptive and robust-adaptive control strategies for a class of anaerobic wastewater treatment processes. Sliding and adaptive sliding mode control strategies for a class of nonlinear biotechnological processes are described [13]. The same technique was also used for the state and parameter estimation for nonlinear delay systems [14]. Furthermore, for a special type of batch bioreactors, an application of a fractional order iterative learning controller was described in [15]. The evolution of the physical structure of bioreactor control systems, together with a discussion about the properties of the novel flat organisational control system for bioreactors can be found in [16]. Reference [16] has described related smart sensors and actuators and their application circumstances, with the hope of improving the efficiency, robustness, and economics of bioprocess control further. Significant progress was also made in the development of control strategies, novel optimisation algorithms, and software frameworks for control systems. These topics have been the subject of some reviews, e.g., [17]. Batch-to-batch iterative learning control of a fed-batch fermentation process using linearised models identified from process operation data is presented in [18]. Recently, an identification tool used for process modelling has been reported in [19], and the global stability of continuous bioreactors has been investigated in [20]. While many publications consider control of continuous- and fed-batch bioreactors, a relatively small number of publications deal with the control of batch bioreactors. Conventional control techniques and most of the modern control approaches assure good control behaviour only when the controlled plant structure and parameters are relatively well known, which is rarely satisfied in real bioreactors.