Genetic-based optimization of temperature set-point signals for buildings with unoccupied rooms

Data‐driven set‐point tuning of model‐free adaptive control

“…In the comparative simulation, we also consider three different disturbances. 19)- (24), the simulation result is shown in Figure 5. It can be seen from the Figure 5 that the proposed DDST-MFAC has good robustness against sinusoidal disturbance.…”

“…Applying DDST-MFAC method ( 19)- (24) and MFAC method (51)-(53), respectively, Figure 11 gives the comparison. One can see that when the tracking performance decreases, the proposed method quickly compensates it and improves the control performance, which demonstrates the superiority of the proposed DDST-MFAC approach.…”

“…According to the reset algorithm (24), we can obtain that | θ(i)| ≤ 𝜍 is bounded. Combining ( 31) and ( 33), one can derive that…”

“…However, most of the above set-point tuning methods [20][21][22][23][24][25][26] are model-based, relying on the explicit mechanistic model information of the systems. This is a disadvantage that may hinder their wide practical applications because it is not easy to build an accurate first-principle model or identify a model for a real plant that has complex dynamics, and large scales.…”

“…Consequently, the proposed DDST-MFAC is a data-driven method, which is different from traditional set-point tuning control. [20][21][22][23][24][25][26] The rest of the work is arranged as follows. Section 2 provides the problem formulation.…”