Hyochoong Bang

Large angle attitude control of spacecraft with actuator saturation

This paper presents spacecraft large angle attitude control problem with actuator saturation limit. Traditional approach for control design for the spacecraft large angle maneuver is a fixed gain proportional plus integral plus derivative (PID) controller using quaternion attitude variables with stability guarantee. Unwanted external disturbance inputs may induce excessively large error by the PID control. Anti-windup control and intelligent integrator are effective in reducing the rapid build up of the control signal due to, in particular, integral control action. Application of the anti-windup and intelligent integrator which already have been studied extensively in other areas is made to the large angle feedback controller minimizing reaction wheel type actuator saturation. Conventional PID controller is modified by augmenting additional control actions for the purpose of performance improvement.

Cooperative. Task Assignment/Path Planning of Multiple Unmanned Aerial Vehicles Using Genetic Algorithms

This research was supported by the Korea Aerospace Research Institute for a program of development of the Communications, Navigation, Surveillance/Air Traffic Management system for the next generation.

Optimal Arrival Flight Sequencing and Scheduling Using Discrete Airborne Delays

An algorithm for optimal arrival flight sequencing and spacing in a near-terminal area is proposed. The optimization problem and algorithm proposed in this paper are developed for a decision-support tool for air-traffic control, which uses discrete delay times as optimization variables. The algorithm is applicable to various scenarios with situational and operational constraints such as maximum position shift (MPS) constraints or different sets of discrete delay times, depending on aircraft types or approaching routes. The proposed algorithm is based on a branch-and-bound algorithm with linear programming (LP) and Lagrangian dual decomposition. We formulate the sequencing and scheduling problem as LP with linear matrix inequalities (LMIs), which allows computing the lower bound of the cost for the best first search in the branch-and-bound algorithm and propose Lagrangian dual decomposition for computational efficiency. The proposed algorithm is analyzed and validated through illustrative air-traffic scenarios with various operational constraints, and the simulation results show that the computation time can be significantly reduced using the proposed Lagrangian dual-decomposition method.

Three-Dimensional Ascent Trajectory Optimization for Stratospheric Airship Platforms in the Jet Stream

The authors would like to thank the Korea Ministry of Commerce, Industry, and Energy for their support of this project and for the permission to publish this work.

Cooperative Control of Multiple Unmanned Aerial Vehicles Using the Potential Field Theory

This research was supported by the Korea Airspace Research Institute (KARI) for a program of development of the CNS/ATM system for the next generation. We truly appreciate their financial support.

Design and analysis of optimal controller for fuzzy systems with input constraint

In this paper, we present a design method of the optimal and robust controller subject to the constraint on control inputs for continuous-time Takagi-Sugeno (TS) fuzzy systems. In order to establish this design method, we consider an optimal and robust control problem for nonlinear dynamic systems. For this problem, we present an analytic way which can provide the optimal controller for nonlinear dynamic systems by the dynamic programming approach and the inverse optimal approach. Moreover, we analyze the robustness property of the proposed optimal controller with respect to a class of input uncertainties by the passivity approach. Then, based on the theoretical results presented in this paper, we formulate the design problem of the optimal and robust controller with input constraint for continuous-time TS fuzzy systems as the semidefinite programming problem, and find the controller by solving it. The usefulness of the proposed approach is illustrated by considering the three-axis attitude stabilization problem of rigid spacecraft.

Star Pattern Identification Technique by Modified Grid Algorithm

A star pattern identification algorithm, called modified grid algorithm, for attitude determination of spacecraft is addressed. The proposed algorithm is closely connected to a general pattern recognition technique. Each star is characterized by a well-defined pattern that can be determined by the surrounding stars. The so-called grid algorithm is one of the star identification algorithms based upon the pattern recognition approach. The proposed algorithm, is motivated by the conventional grid algorithm. To enhance the performance of the conventional grid algorithm, a modified method using polar grid, virtual grid, and multi-references is proposed. Simulation study is conducted for the demonstration of the new algorithm. The proposed modified grid approach turns out to make the grid algorithm more robust and reliable.

Standoff Target Tracking using a Vector Field for Multiple Unmanned Aircrafts

This paper presents strategies for standoff target tracking by a team of unmanned aircrafts using vector field. Many methods to the vector field approach were investigated in other papers, but a modified vector field is introduced to obtain new interesting characteristics in this paper. The modified vector field satisfies more constraints. We introduce two guidance modes to track a target: one is a capturing mode and the other one is a loitering mode. In the capturing mode, aircrafts can arrive at desired positions and time, i.e., unmanned aircrafts can capture a target simultaneously from all sides. After a target is captured, the guidance mode is changed to the loitering mode. Then the relative spacing among aircrafts is controlled by a standoff distance command and a speed command. Hence, they track a target with a desired formation.

Airship control using neural network augmented model inversion

Airship is a highly nonlinear plant which requires gain scheduled controller for various flight conditions. Model inversion control is augmented with neural network to control the airship. The input-output feedback linearization allows one to design a linear controller from a nonlinear system. Neural network is used to compensate the nonlinearities resulting from this linearization. The control technique is applied to two different airship flight conditions. The results shows that control technique is successful in controlling both conditions.

Nonlinear attitude control for a rigid spacecraft by feedback linearization

Attitude control laaw design for spacecraft large angle maneuvers is investigated in this paper. The feedback linearization technique is applied to the design of a nonlinear tracking control law. The output function to be tracked is the quaternion attitude parameter. The designed control law turns out to be a combination of attitude and attitude rate tracking commands. The attitude-only output function, therefore, leads to a stable closed-loop system following the given reference trajectory. The principal advantage of the proposed method is that it is relatively easy to produce reference trajectories and associated controller.