![](bg6.png)
The Figure 8 shows the experimental (with
outdoor temperature -8°C) result, received with
Smith's predictor. The structure of this predictor is
also given in this Figure 4.
From Table 1 it can be said that PID with Smith
Predictor gives better transient response
characteristics than PID controller for process with
constant time delay.
Table 1: Comparison of responses.
4 CONCLUSIONS
The article shows that for objects with a big value of
transport delay it is suggested to use Smith
Predictor. When compared to the usual PID, the
Smith Predictor more improves the system’s
response to set-point changes.
Finally the experimental result of the heating
control with both traditional PID regulator and PID
with Smith predictor are built in PLC
microcontroller. By comparison with traditional PID
regulator, the experimental results demonstrate the
effectiveness of the proposed methods towards the
heating valve delays and system uncertainties
integrated in the building heating control system (see
Table 1). A consistency of Smith predictor control
signals of all possible time delays can be generated
in advance and the actual delays will be
compensated. This control method is mathematically
simple implemented in PLC microcontroller with
reduce resources.
The temperature control system based on the
Smith Predictor controller can precisely control the
temperature inside the instrument. Therefore, it is
able to provide the best temperature for enzymatic
detection to ensure the accuracy of results. Should
the system under control be an integral process,
complementary outside temperature should also be
incorporated into the control system.
In future work is planned to address all above-
mentioned problems within the framework of
research by providing following solutions:
Matlab machine learning toolbox could solve the
problem of optimizing the Building Management
System algorithm by analyzing weather forecast for
the next day and sing Finite Difference Method in
self-learning model. The automation of the home or
the building has a great potential in reducing the cost
and energy consumption using, machine learning for
intelligent control.
ACKNOWLEDGMENT
The research received funding from the ERAF Post-
doctoral Research Support Program project Nr.
1.1.1.2/16/I/001 Research application “Wireless
sensor networks for a building’s energy efficiency
evaluation and data exchange with the building’s
management systems” Nr. 1.1.1.2./VIAA/1/16/228.
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