Traditional proportional-integral-derivative (PID) controllers may not achieve the control performance desired for large time delay processes. Meanwhile, PI-PD controller is an improved version of PID controller, which can effectively improve the control performance of large time delay plants. However, it still has difficulty in parameter tuning. Even if its parameters can be tuned, the PI-PD may not ensure desired performance because of the large time delay and uncertainties of processes.

Recently, predictive function control (PFC) has been widely applied in practice since it is suitable for effectively coping with large time delays and uncertainties. Therefore, combining PFC with PI-PD control may be a good choice.

Kang Chung Hyok, a researcher at the Faculty of Metallic Engineering, designed a PI-PD controller using predictive functional control (PFC) based on the state space model of processes in order to solve the problem of complex parameter tuning. In order to further improve the performance of the controller, he used an improved grasshopper optimization algorithm (GOA) to optimize the weighting matrix of cost function that has a great impact on the performance of the state space PFC.

The simulation results showed that the proposed design method is far superior to other methods in terms of set-point tracking, disturbance rejection and robustness.

You can find more information in his paper “A Novel PI-PD Controller Design Based on Extended State Space Predictive Functional Control using Improved Grasshopper Optimization Algorithm” in “Proceedings of KUTIC-2025”.

Recently, relay protection devices using digital signal processing techniques have been widely used in power systems to improve the reliability of power supply. In these devices, voltage and current signals are converted into discrete data and appropriate algorithms are used to calculate the results for protection. To detect faults accurately and quickly, voltage and current signals need to be acquired at a high speed and many input data must be processed simultaneously.

Many advanced protection algorithms are implemented in microprocessor-based relay protection devices in the form of software. All conventional microprocessor-based relay protection devices implement protection algorithms by using the continuity of software architecture, which takes up some time for CPU and thus limits the operating speed of protection.

Due to the characteristics of inherent parallel hardware routing architecture, FPGAs (field programmable gate arrays) have many applications in industrial and mass manufacturing systems. Some researchers proposed a scheme to implement the function of relay protection by utilizing the characteristics of FPGA.

Im Jung Bin, a researcher at the Faculty of Electronics, proposed a novel FPGA-based data acquisition and processing system to realize fast operation of protection.

Using the characteristics of parallel operation of FPGA, the proposed digital relay protection device can simultaneously judge multi-protection logic by controlling ADCs that simultaneously sample and transform the voltage and current signals of multiple channels and by processing discrete data in real time with full cycle discrete Fourier transform (FCDFT) algorithm. The proposed data acquisition and processing module is configured in the Altera Cyclone III EP3C40Q240CN8 to realize high-speed operation, the main requirement of relay protection.

The proposed digital relay protection device can operate within 1.1 cycles of power system frequency.

For more information, please refer to his paper “FPGA-Based Data Acquisition and Processing System for Digital Relay Protection” in “Proceedings of KUTIC-2025”.

Recently, the ecological environment is deteriorating due to various natural disasters caused by global warming and domestic waste. Therefore, it is important to develop a reliable USV control approach to collect river or sea waste under the challenging environment.

Kim Chung Il, a researcher at the Faculty of Naval Architecture and Ocean Engineering, established a mathematical model of USV motion and a wind model in the presence of mass variation, and then designed a fuzzy neural adaptive sliding mode controller to estimate uncertain mass and wind disturbance simultaneously when collecting sea waste.

First, he devised a mathematical model of USV motion and a wind model to consider added mass and wind variation when collecting sea waste. Then, he used a fuzzy neural method to determine varying wind effect during the mass variation and designed a nonlinear disturbance observer to estimate uncertain disturbances involving mass variation. Finally, he designed an adaptive sliding mode controller to consider varying displacement and wind effect.

The simulation results showed that this methodology enables USV to deal with added mass and varying wind disturbance during a transient period of time to collect sea waste.

You can find more information in his paper “Fuzzy Neural Adaptive SMC of an Unmanned Surface Vehicle for Sea Waste Collection with Varying Displacement and Wind Effect” in “Proceedings of KUTIC-2025”.