| The energy consumption of HVAC systems accounts for over 50% of the total energy consumption of buildings.In response to the carbon peaking and carbon neutrality goals,it is particularly important to vigorously develop HVAC energy-saving technologies to reduce the total energy consumption of the system.Variable Air Volume(VAV)air conditioning systems have become one of the main choices for modern building air conditioning systems due to their advantages in high energy efficiency and high flexibility,and have been widely used in large public buildings in China.However,due to its multivariable coupling characteristics and the hysteresis property of each control loop,how to effectively ensure the stable,safe and efficient operation of a variable air volume air conditioning system is the focus of its control technology research.Model Predictive Control(MPC)is a multivariable control strategy based on optimization theory.Its main characteristics are that it can handle the coupling problem of multivariable systems and explicitly consider the physical constraints of system inputs and outputs.Therefore,it is widely used in variable air volume air conditioning systems.This paper proposes a series of new predictive control methods based on event triggering mechanism for variable air volume air conditioning control systems,which are optimized from multiple aspects such as online computation and closed-loop control performance.The specific research content includes the following:Firstly,a novel event triggered predictive control algorithm is proposed to solve the problem of significantly increasing the online computational complexity of predictive control due to the increase in system dimensions and the increase in control and predictive time domains.Firstly,a trigger mechanism based on output slope and slope change rate is proposed,which is more in line with the dynamic characteristics of the system.An event triggered predictive control algorithm based on output slope and slope change rate is constructed and described.Then,the parameters of the proposed control strategy are adjusted using the Taraxacum optimization algorithm.Finally,simulation verifies the feasibility and effectiveness of the proposed event triggered predictive control algorithm.Secondly,a quasi-quadratic differential event triggered predictive control algorithm is proposed to address the network and computational resource constraints faced by networked variable air volume air conditioning systems.Firstly,a quasi-quadratic differential event triggering mechanism is constructed based on the slope change of the error between the optimal predicted state and the actual state.On this basis,a quasiquadratic differential event triggered predictive control algorithm is proposed,and it is further proved that the control system does not have Zeno phenomenon.In addition,the feasibility of the algorithm and the stability conditions of the closed-loop system are strictly derived theoretically.Finally,an example of a spring damper in an air conditioning system is simulated to verify the effectiveness and applicability of the proposed algorithm.Finally,experimental verification research is conducted on the event triggered predictive control algorithm proposed in this paper.Firstly,the control algorithm is validated on a four tank experimental platform.The results show that the proposed method achieves 45.5% and 49% improvement in solution times compared to existing predictive control methods.Secondly,experimental verification of the proposed algorithm is performed on a VAV air conditioning semi physical simulation experimental platform.The results show that compared with traditional predictive control algorithms,the algorithm reduces the number of solutions by 54.5% and 49%,respectively.The experimental results show that the event triggered predictive control strategy proposed in this paper can effectively reduce the consumption of communication and computing resources in VAV air conditioning systems,and has good control effects. |
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