Background: Networked control systems (NCSs) are used to control industrial and
medical plants via data communication networks. These systems have many wide applications in a
broad range of areas such as remote surgery, industrial and space sciences. Two important challenging
problems in these systems are stochastic time delays and packet dropouts. Classic proportional-
integral controllers due to their simple inherent design and implementation have many applications
in controlling industrial and medical plants. However, these simple controllers do not
have high performance in NCS because of communication networks induced time-varying delays
and so this causes instability in NCS. In this paper, an adaptive proportional-integral controller is
proposed using an online estimation of network time delay technique in a node application layer.
The coefficients of this new controller change according to the values of estimated time delays
online. Therefore, the proposed controller causes stability in NCS loop. The performance of the
proposed method is simulated for a DC motor that can be used in remote surgery. The simulation
results show the proposed controller is better at least about 1000 times according to IAE performance
index rather than a classic proportional-integral controller. Also, the results of practical implementation
show that the proposed controller causes the stability of NCS.
Objective: The study intends to analyze and design an online adaptive approach that can stabilize
networked control systems having important applications such as remote surgery.
Methodology: The article proposes an online adaptive proportional-integral controller that can be
used in NCS and has applications such as remote surgery. The coefficients of the newly proposed
controller are changed online based on the estimation of network time delay. In the proposed controller
firstly, the variable time delay value is estimated online, then the coefficients of the PI controller
is updated based on this estimated value.
Results: The proposed method causes the controller to generate and transmit the suitable control
signal according to different and random conditions of the network. Adaption of coefficients compensates
time-varying delay effect on system performance and causes increasing stability that is
necessary for medical applications.
Conclusion: The proposed system performs better than the traditional approach in terms of measuring
the average value of the error, recall, and ITAE. According to simulation and practical results,
when the network average time delay is about 40ms, the performance index for an online
adaptive PI controller is equal to 4.4236, and value for a classic PI controller is 4409. Thus, the
performance of an online adaptive PI controller has been improved about 1000 times rather than a
classic one. Therefore, the proposed controller in real network time delay has proper performance
and keeps the stability of the control loop.