Robust nonlinear HVAC systems control with evolutionary optimisation

Abstract

Purpose -The purpose of this research is to design a robust high-performance nonlinear multi-input multi-output heating, ventilation and air conditioning (HVAC) system controller for temperature and relative humidity regulation. Buildings are complex systems which are subjected to many unknown disturbances. Further complicating the control problem is the fact that, in practice, buildings and their systems have static nonlinearities such as power saturation that make stability difficult to guarantee. Therefore, in order to overcome these issues, a control system must be designed to be robust (performance insensitive) against uncertainties, static nonlinearities and effectively respond to unknown heat load and moisture disturbances. Design/methodology/approach -A state of the art nonlinear inverse dynamics (NID) technique is combined with a genetic algorithm (GA) optimisation scheme in order to improve robustness against uncertainty in the system's modelling assumptions. The parameter uncertainty problem is addressed by optimising the control system parameters over a specified range of uncertainty. The NID control structure provides further robustness with effective disturbance handling and a stability criteria that holds in the presence of actuator saturation. Findings -The proposed method delivers significantly more energy efficient performance whilst achieving improved thermal comfort when compared with a current industry standard HVAC controller design such as proportional-integral-derivative. The expected excellent response to disturbances is also demonstrated. Research limitations/implications -This method can easily be extended to account for other parameters with a specified uncertainty range. Practical implications -This research presents a method of optimised NID controller design which can be easily implemented in real HVAC controllers of building energy management systems with a high degree of confidence to provide high levels of thermal comfort whilst significantly reducing energy usage. Originality/value -A novel HVAC optimised NID control strategy using the robust inverse dynamics estimation feedback control topology with GA optimisation for improved robustness and tuning over a range of parameter uncertainty is described, designed and its performance benefits shown through simulation studies.

Computational intelligence techniques for HVAC systems: A review

“…However, the authors did not present information about computational time and statistical analysis of the optimizer. Moreover, the experiments can be extended by using different configurations of the GA. Counsell et al (2013) designed a robust non-linear controller. To improve its robustness, a non-linear inverse dynamics (NID) technique was combined with a GA.…”

Application of the genetic algorithm for cascade control of a HVAC system

“…In [6], fuzzy control is proposed of the heating in a household room in accordance with the energy consumption, response time and accuracy, which is performed within an admissible range specified by the thermal comfort in the home. Robust control is another method applied in a HVAC system [7,8]. In [7], a robust cascade control strategy for temperature control of an airconditioning system is proposed.…”

Daylight Design of Office Buildings: Optimisation of External Solar Shadings by Using Combined Simulation Methods

“…However, due to the complexity of using algorithms to solve design problems, they are generally circumscribed to engineering applications. Uses of GAs can be found for example in the optimization of Heating Ventilation and Air Conditioning (HVAC) control systems for temperature and relative humidity regulation, to deliver high levels of thermal comfort with reduced energy loads [7]. The definition of trade-off between users' comfort and expenditures (both capital and annual operational) is another example of application of GAs [8].…”