Research

Control Theory

Control theory is a multifaceted field that sits at the crossroads of engineering and mathematics. Its primary goal is to create a robust theoretical foundation that governs the stability of various systems. This intricate discipline doesn't just focus on system stability; it also delves into designing controllers and refining methods for estimating system states. To achieve these aims, control theory harnesses diverse mathematical tools, including differential equations, linear algebra, and optimization algorithms. The versatility of control theory extends across numerous domains, such as robotics, aerospace, industrial processes, and even biological systems, where its principles are applied to improve performance and enhance functionality. The primary areas of focus under investigation within the laboratory encompass:

Networked Control Systems

Networked control systems transmit control input and measurement signals through the network. Traditional sampled-data control systems sample signals periodically even when the signals do not vary, wasting communication resource. Event-triggered control systems sample signals aperiodically based on an event, saving communication resource and power. We develop convex optimization-based controller design that ensures stable control while efficiently utilizing communication resources.

Switched Systems

Switched systems, which is a class of hybrid systems, consist of a number of subsystems and a switching signal selecting a system. This type of structure has been widely used in control engineering because of its advantages in modelling complex physical systems and controlling nonlinear dynamical systems. However, guaranteeing the stability of such systems are complicated as the stability is influenced by not only the dynamics of subsystems but also switching signal's behaviors. We develop theories for stability analysis and controller design in various constraints such as modelling uncertainties, input saturation, and external disturbances.

Reset Control Systems

Reset control systems add an operation to reset the state variable of a linear time-invariant (LTI) controller, thereby achieving a control response condition that the existing linear time-invariant controller cannot achieve. For example, it is possible to achieve smaller overshoot and settling time than the LTI controller just by resetting the state variable of the LTI controller to zero at specific time instants. We develop stability analysis and controller design conditions for reset control systems based on the piecewise quadratic (PWQ) Lyapunov function, which is more flexible than a common Quadratic Lyapunov function.

Publications on Control Theory

J. Ban, M. Seo, S.W. Kim*, Y.J. Kim, “H2 Reset Controller Design for Linear Systems using Piecewise Quadratic Lyapunov Functions,” International Journal of Robust and Nonlinear Control, vol.33-6, pp.3731-3748, Jan. 2023 (Link)
S. Jeong, J. Ban*, "Robust Reset Controller Design for Switched Nonlinear Uncertain Systems," IEEE Access, vol.10, pp.129382-129393, Dec. 2022 (Link)
S. Jeong, J. Ban*, H. Lee, "Design of robust dynamic output feedback event-triggering controllers for nonlinear uncertain systems," IEEE Access, vol. 10, pp. 82132 - 82143, 2022 (Link)
S. Jeong, J. Ban*, "Robust Dynamic Output Feedback Event-triggering Synchronization for Complex Dynamical Networks," IEEE Access, vol. 10, pp. 1261 - 51271, 2022 (Link)
J. Ban, M. Seo, T. Goh, H. Jeong, S.W. Kim, “Improved co-design of event-triggered dynamic output feedback controllers for linear systems”, Automatica, 111, 2020 (Link)
J. Ban, W.K. Kwon, S.W. Kim, “Robust H∞ finite-time control for discrete-time polytopic uncertain switched linear systems”, Nonlinear Analysis: Hybrid Systems 29: 348-362, 2018 (Link)
W.K. Kwon, J. Ban, S.H. Han, C. Lee, S.C. Won, “Multicriteria adaptive observers for singular systems with unknown time-varying parameters”, Mathematical Problems in Engineering, 2017 (Link)

Control Applications

There are huge gap between control theory and its practical applications. We apply advanced control theories to practical systems such as steel making processes, electric power systems, and vapor compression cycle systems.

Smart Grids (Power Systems)

Control engineers in power system research focus on designing and optimizing control systems for electrical grids. They develop advanced algorithms to manage power flow, voltage, and frequency, integrating renewable energy sources efficiently. Their work involves creating protective systems against faults, ensuring grid resilience, and collaborating on interdisciplinary research for grid modernization and sustainability. Overall, they contribute significantly to enhancing grid efficiency, reliability, and stability.

Related Publications on Power Systems

D.H. Hyun, J. Ban*, Y.J. Kim* et al., "WLS-based behind-the-meter PV generation disaggregation using partial AMI measurements with temporal leveling," IEEE Access, vol. 13, pp.157933-157941, 2025 (Link)
J. Im, J. Ban*, Y.J. Kim*, J. Zhao, "PMU-based Distributed State Estimation to Enhance the Numerical Stability Using Equality Constraints," IEEE Transactions on Power Systems, vol.39-2, pp.4409-4421, Aug. 2023 (Link)
G.H. Lee, J.Y. Park, J. Ban, Y.J. Kim*, and J.P.S. Catalão, "Data-driven Modeling and Optimal Control of Hydrogen Energy Storage for Frequency Regulation," IEEE Transactions on Energy Conversion, vol.38-2, pp.1231-1245, June. 2023 (Link)
J.Y. Park, J. Ban, Y.J. Kim, J.P.S. Catalão, “Supplementary Feedforward Voltage Control in a Reconfigurable Distribution Network using Robust Optimization”, IEEE Transactions on Power Systems, vol.37-6, pp.4385-4399, Nov. 2022 (Link)
J. Ban, J.Y Park, Y.J. Kim*, J.P.S. Catalão, “AMI Data-driven Strategy for Hierarchical Estimation of Distribution Line Impedances,” IEEE Transactions on Power Delivery, vol.38-1, pp.513-527, July 2022 (Link)
H. Lee, J. Ban, S.W. Kim, “Microgrid Optimal Scheduling Considering Remaining Useful Life and Performance Degradation of Distributed Generators”, IEEE Access, vol. 10, pp. 39362 - 39375, 2022 (Link)
J.Y. Park, J. Ban, Y.J. Kim, X. Lu, “Supplementary Feedforward Control of DGs in a Reconfigurable Microgrid for Load Restoration”, IEEE Transactions on Smart Grid, vol.12-6, pp.4641-4654, 2021 (Link)
J. Ban, J. Im, Y.J. Kim, Z. Zhao, “Decentralization of Phasor-Aided State Estimation Using Local State Extension”, IEEE Transactions on Power Systems 36(5): 4645-4659, 2021 (Link)

AI and Robotics

Control engineers in robotics are responsible for designing control algorithms, managing forces and motion, integrating sensors, optimizing tasks, and enhancing human-robot interaction to enable precise, safe, and efficient operation of robotic arms. We also study AI-based robots to advance their control systems and functionalities for enhanced cooperation and interaction.

Related Publications on AI and Robotics

J. Jeong and J. Ban*, "Reinforcement Learning-based Friction Compensation of an Inverted Pendulum on a Cart," International Journal of Machine Learning and Cybernetics, Early access, 2025 (Link)
B. Lee, D. Hyun, J. Kim, S. Lee*, and J. Ban*, "Design of Robust H-infinity Guaranteed Cost Controller of Quadrotor UAVs for Set-point Tracking," IEEE Access, vol. 12, pp.126074-126087, 2024 (Link)

Motor Control, Estimation, and Fault Diagnosis

Control of motors like PMSM, BLDC, and SRMs is essential for efficiency, performance enhancement, and diverse applications across industries relying on motor-driven systems like electric vehicles and household appliances. Through developing multiple control strategies, we optimize motor performance, ensure stability, and integrate advanced technologies to boost the overall efficiency and functionality of electric motors.

Related Publications on Motor Control, Estimation, and Fault Diagnosis

S.Y. Park and J. Ban*, "Hybrid Reaching Law of Sliding Mode Position-sensorless Speed Control for Switched Reluctance Motors," Electronics, vol. 14-3, pp. 577, 2025 (Link)
H. Yoon, G. Kim, and J. Ban*, "Position-sensorless control of switched reluctance motors with converter faults using adaptive sliding mode observers," International Journal of Control, Automation, and Systems, vol. 23-2, pp.674-682, 2025 (Link)

Steel Making Process

The steelmaking process is a complex industrial operation that transforms raw materials such as iron ore, coal, and limestone into steel. One of the global leaders in this domain, POSCO stands at the forefront of innovative steel production techniques. As control engineers, we play a pivotal role in this process by implementing automated systems that regulate critical parameters such as temperature, pressure, thickness, and flatness, ensuring precision and efficiency throughout the production cycle.

Page updated

Google Sites

Report abuse