Robust Nonlinear Estimation and Control Applications using Synthetic Jet Actuators

Limit cycle oscillations (LCO), also known as utter, cause significant challenges in flight control of small unmanned aerial vehicles (SUAVs), and could potentially lead to structural damage and catastrophic failures. LCO can be described as vibrational motions in the rocking, pitching and plunging displacements of an aircraft wing. To address this, the use of synthetic jet actuators (SJAs) in UAV flight control systems is becoming popular as a practical alternative and to mechanical deflection surfaces. Synthetic jet actuators are promising tools for LCO suppression systems in small UAVs due to their small size, ease of operation, and low cost. Uncertainties inherent in the dynamics of the synthetic jet actuators present significant challenges in the synthetic jet actuator-based control design. Specifically, the input-output characteristic (voltage-virtual deflection angle relationship) of the synthetic jet actuators is nonlinear and contains parametric uncertainty. Further control design challenges exist in situations where multiple actuators lose effectiveness. This dissertation focuses on the suppression of limit cycle oscillations on small unmanned air vehicles using synthetic jet actuators. A brief description on how wind gust affects aircraft tracking control is presented. It shows an extension to a paper by adding the wind gust model to the system while also varying the uncertain synthetic jet actuator parameters using a Monte Carlo method. Next, a robust nonlinear control method is presented, which achieves simultaneous aircraft tracking control and limit cycle oscillation suppression using these synthetic jet actuators and a robust controller. Following that, a nonlinear LCO regulation method is presented, which uses a bank of dynamic filters to eliminate the need for pitching and plunging LCO rate measurements. Finally, an alternative method of LCO regulation control is presented, which utilizes a sliding mode observer in lieu of a bank of filters to estimate the pitching and plunging LCO rates

A New Model-Free Sliding Mode Control Method with Estimation of Control Input Error

A new type of sliding mode controller scheme, which requires no knowledge of system model, is derived in this work. The controller is solely based on previous control inputs and state measurements to generate the updated control input effort. The only knowledge required to derive the controller is the system order and the bounds of the control input gain, if one exists. The switching gain, which is required to drive the system states onto the sliding surface in the presence of disturbances and uncertainties, is derived using Lyapunov’s stability theorem, ensuring closed-loop asymptotic stability. The chattering effect, which is excited by the switching gain due to high activity of the control input, is reduced by using a smoothing boundary layer into the control law form. Simulations are performed, using first and second-order, linear and nonlinear systems, to test the performance of the new control law. In the last part of this work, the problem with state measurement noise is addressed. Results of the simulations validates the feasibility of the proposed control scheme

Air Force Institute of Technology Research Report 2007

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Dynamics and Control of a Multi-Rotor Aircraft

Despite the fact that aerodynamic loads (forces and moments) induced by airflow relative to a quadrotor vertical take-off and landing aircraft consist of both deterministic and stochastic components, all existing works on controlling the aircraft either ignore these loads or treat them as deterministic. This simplification deteriorates the control performance in a practical implementation. This thesis presents a constructive design of controllers for a quadrotor aircraft to track a reference path in three-dimensional space under both deterministic and stochastic aerodynamic loads

On particle filters in radar target tracking

The dissertation focused on the research, implementation, and evaluation of particle filters for radar target track filtering of a maneuvering target, through quantitative simulations and analysis thereof. Target track filtering, also called target track smoothing, aims to minimize the error between a radar target's predicted and actual position. From the literature it had been suggested that particle filters were more suitable for filtering in non-linear/non-Gaussian systems. Furthermore, it had been determined that particle filters were a relatively newer field of research relating to radar target track filtering for non-linear, non-Gaussian maneuvering target tracking problems, compared to the more traditional and widely known and implemented approaches and techniques. The objectives of the research project had been achieved through the development of a software radar target tracking filter simulator, which implemented a sequential importance re-sampling particle filter algorithm and suitable target and noise models. This particular particle filter had been identified from a review of the theory of particle filters. The theory of the more conventional tracking filters used in radar applications had also been reviewed and discussed. The performance of the sequential importance re-sampling particle filter for radar target track filtering had been evaluated through quantitative simulations and analysis thereof, using predefined metrics identified from the literature. These metrics had been the root mean squared error metric for accuracy, and the normalized processing time metric for computational complexity. It had been shown that the sequential importance re-sampling particle filter achieved improved accuracy performance in the track filtering of a maneuvering radar target in a non-Gaussian (Laplacian) noise environment, compared to a Gaussian noise environment. It had also been shown that the accuracy performance of the sequential importance re-sampling particle filter is a function of the number of particles used in the sequential importance re-sampling particle filter algorithm. The sequential importance re-sampling particle filter had also been compared to two conventional tracking filters, namely the alpha-beta filter and the Singer-Kalman filter, and had better accuracy performance in both cases. The normalized processing time of the sequential importance re-sampling particle filter had been shown to be a function of the number of particles used in the sequential importance re-sampling particle filter algorithm. The normalized processing time of the sequential importance re-sampling particle filter had been shown to be higher than that of both the alpha-beta filter and the Singer-Kalman filter. Analysis of the posterior Cramér-Rao lower bound of the sequential importance re-sampling particle filter had also been conducted and presented in the dissertation

Air Force Institute of Technology Research Report 2004

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

Air Force Institute of Technology Research Report 2009

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Air Force Institute of Technology Research Report 2011

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Machine vision applications in UAVs for autonomous aerial refueling and runway detection

This research focuses on the application of Machine Vision (MV) techniques and algorithms to the problems of Autonomous Aerial Refueling (AAR) and Runway Detection. In particular, real laboratory based hardware was used in a simulated environment to emulate real-life conditions for AAR. It was shown that the K-Means Clustering Algorithm solution to the Marker Detection problem could be executed at a frame rate of 30 Hz and it averaged a tracking error of less than one pixel while utilizing only 0.16% of the image. It was also shown that the solution to the Runway Detection problem could be executed at a frame rate of 20 Hz which is acceptable for use in an UAV performing reconnaissance work. Data from these tests suggest that both software schemes are suitable for applications in moving vehicles and that the accuracy of the measurements produced by the schemes make them suitable for UAV applications