B-plane targeting method for orbit maneuver using low thrust

International Journal of Control, Automation and Systems - In this research, a new control law for the trajectory correction maneuver (TCM) is proposed for a spacecraft assumed to be actuated by a...     

Attitude maneuver control of flexible spacecraft by observer-based tracking control

A constraint equation-based control law design for large angle attitude maneuvers of flexible spacecraft is addressed in this paper. The tip displacement of the flexible spacecraft model is prescribed in the form of a constraint equation. The controller design is attempted in the way that the constraint equation is satisfied throughout the maneuver. The constraint equation leads to a two-point boundary value problem which needs backward and forward solution techniques to satisfy terminal constraints. An observer-based tracking control law takes the constraint equation as the input to the dynamic observer. The observer state is used in conjunction with the state feedback control law to have the actual system follow the observer dynamics. The observer-based tracking control law eventually turns into a stabilized system with inherent nature of robustness and disturbance rejection in LQR type control laws.     

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.     

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.     

Unmanned Aircraft Vector Field Path Following with Arrival Angle Control

This paper focuses on unmanned aircraft guidance laws for a straight path and a circular orbit following using the vector field approach. The vector fields introduced in this paper can be applied to not only path following, but also other purposes such as arrival position, angle, and time control. Therefore, they could be applied to various missions providing advantages over other previous vector fields. Stability and performance of the path following has been proved and analyzed using the classical control theory. Simulation using a six degrees-of-freedom aircraft model shows that these guidance laws are effective for the missions even under the existence of wind disturbance. Furthermore, flight experiments with a blended-wing-body type unmanned aircraft are performed to evaluate the proposed vector field guidance algorithm.     

Performance Comparison of Gyro-Based and Gyroless Attitude Estimation for CubeSats

In this paper, a comparison study between gyro-based and gyroless approaches for spacecraft attitude estimation is presented. Due to its vulnerability to the model errors, the gyroless approach has not been widely focused on and there are only few comparison studies available. However, this conventional wisdom might not directly apply to CubeSat attitude estimation, where noisy MEMS gyro is usually implemented. Although the noise density can be improved by low-pass filtering, it sacrifices the bandwidth so that it can induce a discretization error when spacecraft rotates in high speed. This paper outlines expected pros and cons of gyroless attitude estimation with respect to cost and miniaturization, rotational agility, and model errors. Additionally, linearized system models for both of the attitude estimation methods are formulated and a simple guideline for tuning process noise against the model errors is proposed. Numerical results for a realistic earth observation scenario are presented to quantitatively compare the benefits and drawbacks of each attitude estimation method.

     

Proportional navigation-based collision avoidance for UAVs

A collision avoidance algorithm for unmanned aerial vehicles (UAVs) based on the conventional proportional navigation (PN) guidance law is investigated. The proportional navigation guidance law being applied to a wide range of missile guidance problems is tailored to the collision avoidance of UAVs. This can be accomplished by guiding the relative velocity vector of the aircraft to a vector connecting the current aircraft position to the safety boundary of the target aircraft. Stability of the proposed algorithm is also studied using the circle criterion. The stability condition can be established by choosing the navigation coefficient within a certain bound. The guidance law is extended to 3-dimensional maneuver problems. Inherent simplicity and robustness of the PN guidance law provides satisfactory collision avoidance performance with different initial conditions. Recommended by Editorial Board member Sangdeok Park under the direction of Editor Hyun Seok Yang. This research was performed for the Smart UAV Development Program, one of 21st Century Frontier R&D Programs funded by the Ministry of Science and Technology of Korea. Su-Cheol Han received the B.S. degree from Korea Airforce Academy, Korea, in 1997, and the M.S. degree from Korea Advanced Institute of Science and Technology, Korea, in 2005. At present, he is serving as a pilot in Korea Airforce. His research interests are UAV guidance and control, especially collision avoidance. Hyochoong Bang received the B.S. and M.S. degrees in aeronautical engineering from Seoul National University in 1985 and 1987, respectively. He also received the Ph.D. degree in 1992 from Texas A&M University. From 1992 to 1994, he worked as a Research Assistant Professor at the U.S. Naval Postgraduate School (NPS) conducting spacecraft attitude control research. From 1995 to 1999, he worked for Korea Aerospace Research Institute. Since 2001 he has been a Professor at Korea Advanced Institute of Science and Technology. His current research interest include spacecraft attitude control, spacecraft guidance, UAV guidance and control. Chang-Sun Yoo received the B.S. degree from Korea Aerospace University, Korea, in 1987, the M.S. degree from Korea Advanced Institute of Science and Technology, Korea, in 1991 and the Ph.D. degree from Chungnam National University, Korea, in 2003. Since 1991, he has been a Research Engineer in Korea Aerospace Research Institute, Korea. His research interests are flight simulation, flight control system, inertial and GPS navigation.     

Adaptive nonlinear control using input normalized neural networks

An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small.