Research

My Research Interest

Learning in Uncertain Systems.

Modeling in physical system is based on known assupmtions. This assumption is to simplify the problem. However, many uncertains in the system are uncovered in modeling. Dynamically learning by interaction in advanced explores any possible uncertain in the system [1, 2]. Development such learning algorithm must be robust and adaptive enough covering uncertain systems [3], which are in nature nonlinear and multivariate.

References

1. S. Syafiie, F. Tadeo, E. Martinez, , T. Alvarez, Model-free control based on reinforcement learning for a wastewater treatment problem, Applied Soft Computing Volume 11, Issue 1, January 2011, Pages 73 – 82
2. Krzysztof Stebel, Practical aspects of the model-free learning control initialization, 20th International Conference on Methods and Models in Automation and Robotics, MMAR 2015, Mie¸dzyzdroje, Poland, August 24-27, 2015
3. Zhongsheng Hou, Shangtai Jin, Model Free Adaptive Control: Theory and Applications, CRC Press

Control Theory in Pharmacology.

Currently practice, drug dosing is on using pharmacokinetic and pharmacodynamic modeling. Pharmacokinetics is the kinetics study of the concentration in various tissues as a function of time and elimination. Pharmacodynamics is the study of drug effect and concentration. It is clear that developing techniques relating dose to resultant drug concentration (pharmacokinetics), and concentration to effect (pharmacodynamics), are a model drug dosing for biological systems [1]. From this point of view, it can be analysed, designed and optimized the drug dose into biological systems based on advanced control theory [2].

References

1. Chang, Jing Jing, S. Syafiie, Raja Kamil, and Thiam Aun Lim. "Automation of anaesthesia: a review on multivariable control." Journal of clinical monitoring and computing 29, no. 2 (2015): 231-239.
2. James M. Bailey and Wassim M. Haddad, Drug Dosing Control in Clinical Pharmacology: Paradigms, benefits, and challenges, Control Systems, IEEE 25.2 (2005): 35-51.

Control Theory in Finance.

The growing awareness of complex decision making in financial bodies is greatly emerged in searching for solving optimal planning. The decision must protect economy activities against the adverse effects of financial risk factors such as movements in exchange rates, interest rates, commodity prices, etc. Thus financial parameters can be seen as inputs into systems for which the stability optimal outputs are the searching. As such, this technique of making decision in finance can be considered as a subsystem of control engineering [1, 2].

References

1. Suresh Sethi and Gerald L. Thompson, Application of Mathematical Control Theory to Finance: Model Simple Dynamic Cash Balance Problems, Journal of Financial and Quantitative Analysis, Vol. 5, No. 4/5, pp. 381 – 394, 1970.
2. Richard C. Laughlin, External Financial Control Systems: Theory and Application, Managerial Finance, Vol.6, No. 1, pp. 32 – 51, 1980.