Sliding Mode Control

 

In control system, sliding mode control, or SMC, is a nonlinear control method that alters the dynamics of a nonlinear system by application of a discontinuous control signal that forces the system to slide along a cross-section of the system's normal behavior. The state-feedback control law is not a continuous function of time. Instead, it can switch from one continuous structure to another based on the current position in the state space. Hence, sliding mode control is a variable structure control method.

 

 

Attitude and Tracking Control of Quadrotor

 

Sensing and actuating technologies developments make, nowadays, the study of mini unmanned air vehicles (UAVs) very interesting. Among many kinds of UAV, the quadrotor, generally called drone, is the most utilized instrument. In this work, we are studying the behaviour of the quadrotor. This flying robot presents the main advantage of having quite simple dynamic features. Indeed, the quadrotor is a small vehicle with four propellers placed around a main body. The main body includes power source, sensors and control hardware. The four rotors are used to controlling the vehicle. The rotational speeds of the four rotors are independent. Thanks to this independence, it’'s possible to control the pitch, roll and yaw attitude of the vehicle. Then, its displacement is produced by the total thrust of the four rotors whose direction varies according to the attitude of the quadrotor. The vehicle motion can thus be controlled.

 

 

Seismographical Analysis and Research

 

These days the Korean peninsula is no longer a safety zone from large scale earthquake. Seismometers are instruments that measure motion of the ground, including those of seismic waves generated by earthquakes, volcanic eruptions, and other seismic sources. Seismograph is more applicable to the older instruments in which the measuring and recording of ground motion were combined than to modern systems, in which these functions are separated. The purpose of this research are the prediction of seismic magnitude using the initial data, the classification of artificial/natural earthquake, and precise location estimation of the epicenter.

 

 

Location Estimation of Emitter

 

Geolocation of a emitter is a significant problem in many industrial fields. There are many schemes of geolocation such as time of arrival (TOA), time difference of arrival (TDOA), frequency difference of arrival (FDOA), angle of arrival (AOA) and reveived signal strength (RSS) methods. In geolocation methods, there are two kinds of factors which deteriorate the accuracy of location estimation: nonline-of-sight (NLOS) noise and measurement noise. NLOS are radio transmissions across a path that is partially obstructed and cause the delay of signal arrival. The aim of this research are the compensation of these kinds of noise and precise estimation of emitter's location.

 

 

Mathematical Optimization

 

In mathematics, computer science and operations research, mathematical optimization is the selection of a best element (with regard to some criterion) from some set of available alternatives. An optimization problem consists of maximizing or minimizing a objective function by systematically choosing input variables from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics. More generally, optimization includes finding "best available" values of some objective function given a defined domain, including a variety of different types of objective functions and different types of domains.

 

 

Optimal and Robust Control

 

Mathematical models of systems are typically incomplete and/or imprecise, and real systems operate in the presence of a variety of external partially unknown or unmeasurable disturbances. Nevertheless, one would like to design high performance control systems in the presence of significant and large uncertainty in the system models and external disturbances. Such designs would be based on quantifying performance objectives in terms of frequency and/or time domain specifications, as well as modeling of uncertain system dynamics. These issues are addressed in this research in the context of linear, nonlinear, and distributed parameter systems. An overall goal is to gain an understanding of the limits of optimal performance of control systems in the presence of significant uncertainty, as well as to develop convergent algorithms for computing these limits and the associated optimally robust controllers.

 

 

Minimax Control and Dynamic Games

 

This project is aimed at developing a time-domain based theory for derivation of minimax (worst-case) identifiers and controllers for nonlinear systems with norm-bounded and partially stochastic uncertainties, and analyzing their robustness with respect to modeling inaccuracies and model reduction. Robustness is imposed here on the top of minimaxity, and introduces a further classification of minimax estimators and controllers according to their admissibility, or sensitivity to inaccuracies in plant modeling. Of particular emphasis here is the inaccuracy that results from model simplifications due to time-scale separation (such as presence of unmodeled fast dynamics) or weak coupling of several subsystems. A further topic of study is the characterization of robust controllers for parameterized families of plants and under multiple criteria. The general approach adopted is that of dynamic or differential game theory.