The current advanced communication and computing devices, as well as the advances achieved in data storage, led to more advanced control techniques for industrial processes. Advanced control techniques employed for HVAC systems were also addressed in the literature of HVAC
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control. Gain Scheduling Control (GSC) is a control theory that can be employed where the procedure is to divide the system into several linear zones so that a specific self-tuned gain can be set for a PI or PID controller for each specific linear zone. The PI or PID controller can be applied for a specific linear zone associated with its operating condition so that self-tuning can be implemented based on the system state values (Afram and Janabi-Sharifi, 2014b). For example, (Tahersima et al., 2010) used two different PI controllers which are tuned for HVAC hydronic-radiator at the states of high heat and low heat requirements. In Pal and Mudi (2008) the authors focused on controlling the pressure of supplied air in a HAVC system using a PI controller. The controller was tuned based on the gains that form the error between the measured pressure and the set point of the air supply. Rasmussen and Alleyne (2010) have used the same control concept by offering a study to control an air conditioning system based on MIMO representation. The control strategy employed in this regard is GSC to improve the efficiency of the system during the demands of changing the cooling capacity.
A feedback linearization is also a technique that was used for HVAC nonlinear system models.
Instead of linearizing the nonlinear process near an operating point, the process can be subjected to a feedback linearization to transform it to an equivalent linearized process for the whole range of operating points (Miskovic and Vukic, 2009). Many researchers have used the Feedback Linearization Control methodology as employed by Thosar, Patra and Bhattacharyya (2008) and He and Asada (2003). They used a feedback linearization method to eliminate the nonlinearity in HVAC system dynamics and to generate a linear function enabling further control procedures. The researchers also employed a PI controller after achieving equivalent linear model to achieve the desired system performance. Semsar-Kazerooni, Yazdanpanah and Lucas (2008) also followed the same technique by linearizing the HVAC process first then applying a back-stepper controller to achieve the desired performance. The controller used in
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their research was also able to reject the disturbance caused by external leaked heat and moisture
The study of Moradi, Saffar-Avval and Bakhtiari-Nejad (2011) focused on proposing a nonlinear control for a nonlinear air handling unit, modelled as a MIMO system. The inputs considered in the study were the positions of the valves controlling the cold water and airflow rates while the outputs were the indoor temperature and relative humidity. The researchers have employed comparisons between two different control techniques, these are Gain Scheduling Control and the Feedback Linearization Control in which the dynamic behaviour of one controller was better than the other. He and Asada (2003) also used Feedback Linearization Control in their study. They managed to transform the linearity of the multi-unit HVAC system to a linear system for the whole range of operating points and thereafter applied a simple PI control.
Robust Control is one of the control techniques that also has been used for controlling HVAC systems. The Robust Control technique is employed to assure the performance of the system under control regardless of the changes in the system dynamics. The Robust control technique is considered as static in comparison with the adaptive control technique, because it does not adapt to the system dynamic variations but assess the performance and the stability of the system for a bounded range of unknown variables. Anderson et al. (2008) followed a robust control technique for a HVAC system to examine some other advanced controllers. They found that a robust control system could provide potential performance improvement. Based on robust control, Al-Assadi et al. (2004) achieved improved system performance pertaining to multiple zones indoor temperature and achieved stability with the presence of uncertainties of the model and external disturbances.
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Qin and Badgwell (2003) also studied Model Predictive Control (MPC) and defined it as a class of computer algorithms developed for controlling that predicting the plant future responses by computing a sequence of future manipulated variable adjustments. The technique is very popular since it does not need expert involvement during long time of operation period.
Some design techniques that emanate from the MPC technique can be used for controlling industrial processes, such as Model Algorithmic Control, Dynamic Matrix Control, Internal Model Control and Inferential Control technique (García, Prett and Morari, 1989). MPC is one of the advanced control techniques that has been implemented to regulate HVAC system operations in the last few years. It employs the system model aiming to predict the future behaviour of the system and applying afterwards the proper control technique (Ulusoy, 2018).
The MPC control technique employs an explicit HVAC model to forecast the future system states based on which a vector of controllers can be proposed within certain constrains and expected disturbances in order to optimise the control function cost and system performance (Afram and Janabi-Sharifi, 2014b). Aswani et al. (2012) have developed an MPC control technique to optimise the controlling cost of the transient and steady state responses of a HVAC system. The control technique was based on learning the amount of emitted heat by indoor occupancy during the day, month and year. The strategy used in this research is indeed a learning-based technique. Xi, Poo and Chou (2007) have employed a nonlinear MPC control to regulate the indoor temperature and relative humidity based on an optimization algorithm, which was used to generate the control signals online within the control constraints. The obtained results showed good control performance and low steady state errors.
Due to the nonlinearity and the different time lags and inertia, which are inherent characteristics of HVAC systems, it is a challenging task as mentioned in chapter two to develop an accurate mathematical model describing the real HVAC process over wide operating conditions.
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Therefore, intelligent control techniques are promising alternative control solutions for HVAC system in comparison with the traditional control methods. When using fuzzy logic controllers as intelligent control technique which is depending on Knowledge Base (KB), no mathematical modelling is required to design the controller (Mirinejad et al., 2012). In the KB procedure the
“if-then” which is designed based on human expertise, or according to learning and self- organization methods, does not need the mathematical model of the system. Erez et al. (2003) used genetic algorithms in order to develop a fuzzy logic controller incorporating smart tuning to regulate a HVAC system. The authors used real experiments combined with simulations to validate the effectiveness of the proposed control technique.