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.

  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.

 

 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.

- Related journals

   1. Input nonlinearity

1) 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.

2) 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.

3) 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.

4) 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.

5) 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.

   2. Fuzzy

1) 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. 

2) 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.

3) 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.

   3. Networked control system

1) 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.

  Time delays are inevitable phenomenon in the practical systems and they result in performance degradation and system instability. Therefore, stability analysis of time delay systems has attracted considerable attention in recent decades. The main purpose of stability analysis of time delay systems is to obtain maximum delay bound that ensures the asymptotic stability for the concerned systems.  For the stability analysis of time delay systems, the Lyapunov-Krasovskii functional (LKF) method is an efficient way and this method consists of two essential aspects to improve conservatism: the construction of a proper LKF and the estimation of the LKF derivation.

- Related journals

1) Ko, Jeong Wan, and Poo Gyeon Park. “Delay-dependent stability criteria for systems with asymmetric bounds on delay derivative.” Journal of the Franklin Institute 348.9 (2011): 2674-2688.

2) Park, PooGyeon, Jeong Wan Ko, and Changki Jeong. “Reciprocally convex approach to stability of systems with time-varying delays.” Automatica 47.1 (2011): 235-238.

3) Jeong, Changki, PooGyeon Park, and Sung Hyun Kim. “Improved approach to robust stability and H∞ performance analysis for systems with an interval time-varying delay.” Applied Mathematics and Computation 218.21 (2012): 10533-10541.

4) Lee, Won Il, and PooGyeon Park. “Second-order reciprocally convex approach to stability of systems with interval time-varying delays.” Applied Mathematics and Computation 229 (2014): 245-253.

5) Lee, Won Il, Seok Young Lee, and PooGyeon Park. “Improved criteria on robust stability and H∞ performance for linear systems with interval time-varying delays via new triple integral functionals.” Applied Mathematics and Computation 243 (2014): 570-577.

6) Park, PooGyeon, Won Il Lee, and Seok Young Lee. “Auxiliary function-based integral inequalities for quadratic functions and their applications to time-delay systems.” Journal of the Franklin Institute 352.4 (2015): 1378-1396.

7) Lee, Won Il, Seok Young Lee, and PooGyeon Park. “Improved stability criteria for recurrent neural networks with interval time-varying delays via new Lyapunov functionals.” Neurocomputing 155 (2015): 128-134.

  An adaptive filter is a system with a linear filter that has a transfer function controlled by variable parameters and a means to adjust those parameters according to an optimization algorithm. Because of the complexity of the optimization algorithms, most adaptive filters are digital filters. Adaptive filters are required for some applications because some parameters of the desired processing operation (for instance, the locations of reflective surfaces in a reverberant space) are not known in advance or are changing. The closed loop adaptive filter uses feedback in the form of an error signal to refine its transfer function.

  Generally speaking, the closed loop adaptive process involves the use of a cost function, which is a criterion for optimum performance of the filter, to feed an algorithm, which determines how to modify filter transfer function to minimize the cost on the next iteration. The most common cost function is the mean square of the error signal. As the power of digital signal processors has increased, adaptive filters have become much more common and are now routinely used in devices such as mobile phones and other communication devices, camcorders and digital cameras, and medical monitoring equipment.

  Machine learning (ML), a subset of artificial intelligence, is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. Specifically, its algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. Moreover, machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning as well. Nowadays, machine learning algorithms are being used in a wide variety of applications. We conduct several researches based on machine learning as follows: machine learning algorithms for system control, modeling, and system optimization.

          

                   

- Related projects

1.      1) POSCO – 3열연 조업 데이터를 활용한 캠버제어용 AI 모델 개발
2.      2) AI를 이용한 FM Stand Leveling 최적 Pattern 모델 개발

     3) AI 를 활용한 캠버 현상 모델링 및 제어 알고리즘 개발

     4) 삼성 – 기계학습을 이용한 빅데이터 기반의 생산 공정 모델링 개발 및 공정 분석

     5) 반도체 Wafer OCD 측정 데이터 활용 실제 계측 샘플 최적화 (Sampling optimization)

     6) OES (Time-wavelength-intensity) 데이터 Signal-Noise-Outlier 구분 (Distinguish noise)