Conventional control theory has allowed man to control and automate his environment for centuries. Modern control techniques have allowed engineers to optimize the control systems they build for cost and performance. However, optimal control algorithms are not always tolerant to changes in the control system or the environment. Robust control theory is a method to measure the performance changes of a control system with changing system parameters. Application of this technique is important to building dependable embedded systems. The goal is to allow exploration of the design space for alternatives that are insensitive to changes in the system and can maintain their stability and performance. One desirable outcome is for systems that exhibit graceful degradation in the presence of changes or partial system faults.
Definition of Robust Control
From [Chandrasekharan, P., C., Robust Control of Linear Dynamical Systems, Academic Press, 1996.], “Robust control refers to the control of unknown plants with unknown dynamics subject to unknown disturbances”. Clearly, the key issue with robust control systems is uncertainty and how the control system can deal with this problem. Figure 2 shows an expanded view of the simple control loop presented earlier. Uncertainty is shown entering the system in three places. There is uncertainty in the model of the plant. There are disturbances that occur in the plant system. Also there is noise which is read on the sensor inputs. Each of these uncertainties can have an additive or multiplicative component.

Figure 1: Plant control loop with uncertainty

The figure above also shows the separation of the computer control system with that of the plant. It is important to understand that the control system designer has little control of the uncertainty in the plant. The designer creates a control system that is based on a model of the plant. However, the implemented control system must interact with the actual plant, not the model of the plant.

Our laboratory is especially interested in the following topics.

    Input nonlinearity

    • Yun, Sung Wook, Yun Jong Choi, and PooGyeon Park. “State‐feedback disturbance attenuation for polytopic LPV systems with input saturation.”International Journal of Robust and Nonlinear Control 20.8 (2010): 899-922.
    • Yun, Sung Wook, Yun Jong Choi, and PooGyeon Park. “Dynamic output-feedback guaranteed cost control for linear systems with uniform input quantization.” Nonlinear Dynamics 62.1-2 (2010): 95-104.
    • Park, Bum Yong, Sung Wook Yun, and PooGyeon Park. ” mathcal {H} _ {2} state-feedback control for LPV systems with input saturation and matched disturbance.” Nonlinear Dynamics 67.2 (2012): 1083-1096.
    • Park, Bum Yong, et al. “Multistage γ-level mathcal {H} _ { infty} control for input-saturated systems with disturbances.” Nonlinear Dynamics 73.3 (2013): 1729-1739.
    • Yun, Sung Wook, Yun Jong Choi, and PooGyeon Park. “H2 control of continuous-time uncertain linear systems with input quantization and matched disturbances.” Automatica 45.10 (2009): 2435-2439.


    • Kim, Sung Hyun, and PooGyeon Park. “State-feedback-control design for discrete-time fuzzy systems using relaxation technique for parameterized LMI.”Fuzzy Systems, IEEE Transactions on 18.5 (2010): 985-993.
    • Ko, J. W., and PooGyeon Park. “Further enhancement of stability and stabilisability margin for Takagi–Sugeno fuzzy systems.” IET Control Theory & Applications 6.2 (2012): 313-318.
    • Ko, Jeong Wan, Won Il Lee, and PooGyeon Park. “Stabilization for Takagi–Sugeno fuzzy systems based on partitioning the range of fuzzy weights.”Automatica 48.5 (2012): 970-973.

    Networked Control System

    • Kim, S. H., Pyeongyeol Park, and Cheol Jeong. “Robust ℋ∞ stabilisation of networked control systems with packet analyser.” IET control theory & applications 4.9 (2010): 1828-1837.