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Research Area
Robust<br />
Control

Robust
Control

Robust control is a branch of control theory whose approach to controller design explicitly deals with uncertainty.
Time Delay<br />
System

Time Delay
System

Systems with time-delay are called time-delay systems. Controlling time delay systems is more complicated compared to that of ordinary systems.
Adaptive<br />
Filter

Adaptive
Filter

An adaptive filter is a system with a linear filter that has a transfer function controlled by variable parameters and means to adjust those parameters according to an optimization algorithm.
Machine<br />
Learning

Machine
Learning

Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions.
LATEST publication
  • Stability analysis for systems with time-varying delay via orthogonal-polynomial-based integral inequality

    JunMin Park, PooGyeon Park

    IFAC-PapersOnLine

    Abstract This paper proposes an integral inequality related to the state vector for systems with time-varying delay and exploits component vectors of the proposed inequality for constructing a Lyapunov-Krasovskii functional. The proposed inequality is based on orthogonal-polynomial-based integral inequality. The component vectors of the proposed inequality have the relation in terms of time-varying delay with those of the orthogonal-polynomial-based integral inequality. Also, the time-derivative of the component vectors of the proposed inequality are represented by those of the orthogonal-polynomial-based integral inequality. The Lyapunov-Krasovskii functional is constructed by utilizing the component vectors of the proposed inequality and the orthogonal-polynomial-based integral inequality. Based on the the Lyapunov-Krasovskii functional, a stability criterion is derived in terms of linear matrix inequalities. Simulation results show that the proposed criterion is less conservative than the criteria in the literature.
  • Dynamic output‐feedback control for continuous‐time singular Markovian jump systems

    Chan‐eun Park, Nam Kyu Kwon, PooGyeon Park

    International Journal of Robust and Nonlinear Control

    Abstract This paper considers a dynamic output‐feedback control for continuous‐time singular Markovian jump systems, whereas the existing research studies in literature focused on state‐feedback or static output‐feedback control. While they have only provided the sufficient conditions, this paper successfully obtains the necessary and sufficient condition for the existence of the dynamic output‐feedback control. Furthermore, this condition is expressed with linear matrix inequalities by the so‐called replacement technique. Two numerical examples show the validity of the resulting control.
  • Adaptive regularisation for normalised subband adaptive filter: mean-square performance analysis approach

    JaeWook Shin, JinWoo Yoo, PooGyeon Park

    IET Signal Processing

    Abstract The normalised subband adaptive filter (NSAF) is a useful adaptive filter, which improves the convergence rate compared with the normalised least mean-square algorithm. Most analytical results of the NSAF set the regularisation parameter set to zero or present only steady-state mean-square error performance of the regularised NSAF (ε-NSAF). This study presents a mean-square performance analysis of ε-NSAF, which analyses not only convergence behaviour but also steady-state behaviour. Furthermore, a novel adaptive regularisation for NSAF (AR-NSAF) is also developed based on the proposed analysis approach. The proposed AR-NSAF selects the optimal regularisation parameter that leads to improving the performance of the adaptive filter. Simulation results comparing the proposed analytical results with the results achieved from the simulation are presented. In addition, these results verify that the proposed AR-NSAF outperforms the previous algorithms in a system-identification and acoustic echo-cancellation scenarios.
LATEST project
ABOUT LABORATORY

Signal Processing and Control Laboratory has been organized since 1996. Our current research interests include convex optimization theories and their applications in control, estimation and signal processing.
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