Robust MPC for temperature control of air-conditioning systems concerning on constraints and multitype uncertainties

This paper presents a robust model-based predictive control (MPC) strategy for temperature control of an air-conditioning system, which consists of multiple local-loop processes and each process suffers from different dynamics uncertainties or variations. When an appropriate sampling period is chosen to discretise the system for computer control, a state-space discrete model with an uncertainty polytope is developed to describe the mixing uncertainties of these type of systems. The main benefit of the proposed description is that robust model predictive control can be easily used to design a robust controller for such a system while taking account of constraints associated with this system. A linear matrix inequality-based MPC algorithm is employed for control design. Case study was conducted on a dynamic simulation platform of an air-conditioning system, which evaluated the developed strategy in various simulation tests by comparing with the conventional PID control. Results demonstrated that the developed strategy is able to deal with constraints and allows stable and robust control while maintaining acceptable thermal comfort. Although the strategy is illustrated and validated using a constant air volume air-conditioning system, it can be applied to other constraint HVAC processes suffering from similar uncertainties. Practical applications: The main benefit of the developed strategy (including the proposed description and the adopted robust control algorithm) for practical application is that uncertainties and constraints can be dealt with simultaneously in one framework. Constraints in HVAC systems exist due to the application of actuators, for example, the rate limit considered in the paper. Taking account of constraints is useful to prevent unnecessary damages to equipments and maintain the system operating in a safe mode. Most importantly, the control objectives can be achieved in a constraint manner. The consideration of uncertainties, probably due to the changes of operating environment, is useful to release the work of accurate modelling of HVAC processes and online tuning of controllers.