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Zhao, YueAutomatic Prevention and Recovery of Aircraft Loss-of-Control by a Hybrid Control Approach
Doctor of Philosophy (PhD), Ohio University, 2016, Electrical Engineering & Computer Science (Engineering and Technology)
In this dissertation, an integrated automatic flight controller for fixed-wing aircraft Loss-of-Control (LOC) Prevention and Recovery (iLOCPR) is designed. The iLOCPR system comprises: (i) a baseline flight controller for six degrees-of-freedom (6DOF) trajectory tracking for nominal flight designed by trajectory linearization, (ii) a bandwidth adaption augmentation to the baseline controller for LOC prevention using the timevarying PD-eigenvalues to trade tracking performance for increased stability margin and robustness in the presence of LOC-prone flight conditions, (iii) a controller reconfiguration for LOC arrest by switching from the trajectory tracking task to the aerodynamic angle tracking in order to recover and maintain healthy flight conditions at the cost of temporarily abandoning the mission trajectory, (iv) a guidance trajectory designer for mission restoration after the successful arrest of a LOC upset, and (v) a supervisory discrete-eventdriven Automatic Flight Management System (AFMS) to autonomously coordinate the control modes (i) - (iv). Theoretical analysis and simulation results are shown for the effectiveness of the proposed methods.

Committee:

Jim Zhu (Advisor); Douglas Lawrence (Committee Member); Frank Van Grass (Committee Member); Robert Williams (Committee Member); Aili Guo (Committee Member); Sergiu Aizicovici (Committee Member)

Subjects:

Aerospace Engineering; Engineering

Keywords:

Aircraft Loss-of-control; hybrid; arrest; prevention; recovery; flight control system; arrest; guidance; trajectory linearization control; switching mode; reconfiguration; bandwidth adaptation; multiple-time-scale nested loop

Liu, YongNEURAL ADAPTIVE NONLINEAR TRACKING USING TRAJECTORY LINEARIZATION
Doctor of Philosophy (PhD), Ohio University, 2007, Electrical Engineering & Computer Science (Engineering and Technology)

Advanced nonlinear control design methods usually depend on an analytical plant model, which in many practical applications, is often inaccurate or unavailable. Neural networks are a powerful tool to enhance a dynamic model if one is available but inaccurate, or to build a system dynamic model from experimental data. Trajectory linearization control (TLC) is a nonlinear control design method, which combines nonlinear dynamic inversion and linear time-varying (LTV) feedback stabilization to achieve robust tracking control for a broad class of nonlinear dynamic systems. In this dissertation, the research goal is to develop theories and methodologies to improve the understanding and applicability of TLC for inaccurate or lack of dynamic models by using neural networks. To this end, the following research objectives have been achieved. First, rigorous stability robustness analyses of TLC subject to the regular perturbation and singular perturbation are established. Second, a continuous-time nonlinear system identification method using neural network is developed. Third, a neural network trajectory linearization control (NNTLC) design procedure with stability analysis is proposed. Fourth, an adaptive neural network trajectory linearization control (ANNTLC) scheme is presented, in which the neural network control compensates for the system uncertainty adaptively. Illustrative examples of nonlinear applications of TLC, NNTLC and ANNTLC are also presented.

Committee:

J. Zhu (Advisor)

Keywords:

Nonlinear Control; Neural Network; Adaptive Control; Trajectory Linearization Control; System Identification

Chen, YuanyanAutonomous Unmanned Ground Vehicle (UGV) Follower Design
Master of Science (MS), Ohio University, 2016, Electrical Engineering (Engineering and Technology)
A vehicle-to-vehicle follower design based on RC Unmanned Ground Vehicle (UGV) is presented in this thesis. To achieve the desired performance for two-vehicle leader-follower, a 3DOF path trajectory tracking controller with a close-loop guidance controller are used, which consider both the kinematics and the dynamics characteristics of the vehicle model. In our research, we use a Trajectory Linearization Control (TLC) to achieve the path tracking, and a PID controller to guide the preceding vehicle. To this end, the following objectives have been achieved. First, a 3DOF kinematics and dynamics vehicle model has been built. Second, an Adaptive Cruise Control (ACC) Trajectory Linearizaton Control (ACCTLC) scheme is presented. Third, a Vehicle-to-Vehicle following Trajectory Linearizaiton Control is proposed. MATLAB/SIMULINK simulation testing of 3DOF control algorithm is presented, which verifies the algorithm. Future work include implementing the current controller design by installing those algorithms to the real RC car; as well as adding lane constraint to the current work; and adding obstacle avoidance to develop fully autonomous ground vehicle.

Committee:

Michael Braasch (Advisor); Jim Zhu (Committee Co-Chair)

Subjects:

Electrical Engineering

Keywords:

UGV, Guidance; Navigation and Control; Trajectory Linearization Control; Adaptive Cruise Control; Line of Sight; Trajectory Tracking, PID

Garimella, SureshActuator Modeling and Control For a Three Degrees of Freedom Differential Thrust Control Testbed
Master of Science (MS), Ohio University, 2007, Electrical Engineering & Computer Science (Engineering and Technology)
This thesis presents an improvement in the performance of a three degrees of freedom differential thrust control testbed by considering the actuator dynamics. The testbed consists of three propellers that are used to produce thrust as well as attitude control for vertical takeoff and landing flight. Actuator dynamics consist of the motor-propeller dynamics and the nonlinear mapping relating the aerodynamic torques to the propeller speed. A previous controller was designed by neglecting the motor-propeller dynamics and the control allocation was done assuming a linear static relationship between aerodynamic torques and motor voltages. This work will determine the nonlinear control allocation mapping and model the motor-propeller dynamics as a first-order linear system. Simulation and real-time results showing an improvement in the performance of the testbed are presented by replacing the linear control allocation with nonlinear control allocation and by compensating for the motor-propeller dynamics. Further, the existing controller is redesigned considering the gyroscopic effects produced due to the spinning propellers.

Committee:

Jianchao Zhu (Advisor)

Keywords:

Nonlinear Control Allocation; Actuator Dynamics; DC Motor-propeller dynamics; Differential thrust control testbed; Trajectory Linearization Control; MATLAB/Simulink; Open-loop control; Closed-loop control