116 research outputs found
Career: artificial learning control systems for performance critical applications
Issued as final reportNational Science Foundation (U.S.
Modeling and flight testing of differential thrust and thrust vectoring on a small UAV
The primary objectives of this research are to mathematically model the propulsion forces applied to the aircraft during nominal, differential thrust, and thrust vectored flight configurations, and verify this modeling through simulation and flight testing experiments. This thesis outlines the modeling process, simulator development, design, and implementation of a propulsion assisted control system for the WVU Flight Control Systems Lab (FCSL) research aircraft. Differential thrust and thrust vectoring introduce additional propulsive terms in the aircraft force equations that are not present when the thrust line passes through the center of gravity. These additional forces were modeled and incorporated into a simulator of the research aircraft. The effects from differential thrust were small and difficult to quantify. The thrust vectoring effects were also found to be small with the elevator having significantly more pitch control over the vectored motors at the simulated flight conditions.;Differential thrust was implemented using the on-board computer to command a different thrust level to each motor. The desired thrust differential was programed into a flight scheme based on simulation data, and activated during flight via a control switch on the transmitter. The thrust vectoring mechanism was designed using SolidWorksRTM, built and tested outside of the aircraft, and finally incorporated into the aircraft. A high torque servo was used to rotate the motor mounting bar and vector the motors to a desired deflection. Utilizing this mechanism, the thrust vectoring was flight tested, mimicking scenarios tested in simulation. The signal to noise ratio was very low, making it difficult to identify the small changes in the aircraft parameters caused by the vectored thrust
Intelligent control of a ducted fan VTOL UAV with conventional control surfaces
Utilizing UAVs for intelligence, surveillance, and reconnaissance (ISR) is beneficial in both military and civil applications. The best candidates for successful close range ISR missions are small VTOL UAVs with high speed capability. Existing UAVs suffer from the design tradeoffs that are usually required, in order to have both VTOL capability and high speed flight performance. In this thesis, we consider a novel UAV design configuration combining several important design elements from rotorcraft, ducted-fan, tail-sitter, and fixed-wing vehicles. While the UAV configuration is more towards the VTOL type, high speed flight is achieved by performing a transition maneuver from vertical attitude to horizontal attitude. In this unique approach, the crucial characteristics of VTOL and high speed flight are attained in a single UAV design. The capabilities of this vehicle come with challenges of which one of the major ones is the development an effective autonomous controller for the full flight envelope. Ducted-fan type UAVs are unstable platform with highly nonlinear behaviour, and with complex aerodynamic, which lead to inaccuracies in the estimation of the vehicle dynamics. Conventional control approaches have limitations in dealing with all these issues. A promising solution to a ducted-fan flight control problem is to use fuzzy logic control. Unlike conventional control approaches, fuzzy logic has the ability of replicating some of the ways of how humans make decisions. Furthermore, it can handle nonlinear models and it can be developed in a relatively short time, as it does not require the complex mathematics associated with classical control theory. In this study, we explore, develop, and implement an intelligent autonomous fuzzy logic controller for a given ducted-fan UAV through a series of simulations
Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization
Contemporary autopilot systems for unmanned aerial vehicles (UAVs) are far
more limited in their flight envelope as compared to experienced human pilots,
thereby restricting the conditions UAVs can operate in and the types of
missions they can accomplish autonomously. This paper proposes a deep
reinforcement learning (DRL) controller to handle the nonlinear attitude
control problem, enabling extended flight envelopes for fixed-wing UAVs. A
proof-of-concept controller using the proximal policy optimization (PPO)
algorithm is developed, and is shown to be capable of stabilizing a fixed-wing
UAV from a large set of initial conditions to reference roll, pitch and
airspeed values. The training process is outlined and key factors for its
progression rate are considered, with the most important factor found to be
limiting the number of variables in the observation vector, and including
values for several previous time steps for these variables. The trained
reinforcement learning (RL) controller is compared to a
proportional-integral-derivative (PID) controller, and is found to converge in
more cases than the PID controller, with comparable performance. Furthermore,
the RL controller is shown to generalize well to unseen disturbances in the
form of wind and turbulence, even in severe disturbance conditions.Comment: 11 pages, 3 figures, 2019 International Conference on Unmanned
Aircraft Systems (ICUAS
Development of Fault Tolerant Adaptive Control Laws for Aerospace Systems
The main topic of this dissertation is the design, development and implementation of intelligent adaptive control techniques designed to maintain healthy performance of aerospace systems subjected to malfunctions, external parameter changes and/or unmodeled dynamics. The dissertation is focused on the development of novel adaptive control configurations that rely on non-linear functions that appear in the immune system of living organisms as main source of adaptation. One of the main goals of this dissertation is to demonstrate that these novel adaptive control architectures are able to improve overall performance and protect the system while reducing control effort and maintaining adequate operation outside bounds of nominal design. This research effort explores several phases, ranging from theoretical stability analysis, simulation and hardware implementation on different types of aerospace systems including spacecraft, aircraft and quadrotor vehicles.
The results presented in this dissertation are focused on two main adaptivity approaches, the first one is intended for aerospace systems that do not attain large angles and use exact feedback linearization of Euler angle kinematics. A proof of stability is presented by means of the circle Criterion and Lyapunov’s direct method. The second approach is intended for aerospace systems that can attain large attitude angles (e.g. space systems in gravity-less environments), the adaptation is incorporated on a baseline architecture that uses partial feedback linearization of quaternions kinematics. In this case, the closed loop stability was analyzed using Lyapunov’s direct method and Barbalat’s Lemma. It is expected that some results presented in this dissertation can contribute towards the validation and certification of direct adaptive controllers
Robust Control of Vectored Thrust Aerial Vehicles via Variable Structure Control Methods
The popularity of Unmanned Aerial Vehicles (UAVs) has grown rapidly in many civil and military applications in the last few decades. Recent UAV applications include crop monitoring, terrain mapping and aerial photography, where one or several image sensors attached to the UAV provide important terrain information. A thrust vectoring aerial vehicle, a vehicle with the ability to change the direction of thrust generated while keeping the UAV body at a zero roll and pitch orientation, can serve well in such applications by allowing the sensors to capture stable image data without additional gimbals, reducing the payload and cost while increasing the flight endurance. Furthermore, thrust vectoring UAVs can perform fast forward flight as well as hover operations with non-zero pitch: features which can serve well in military applications. The first part of this research focuses on developing a comprehensive dynamic model and a low level attitude and position control structure for a tri-rotor UAV with thrust vectoring capability, namely the Vectored Thrust Aerial Vehicle.
Nonlinear dynamics of UAVs require robust control methods to realize stable flight. Special attention needs to be given to wind gust disturbances, and parametric uncertainties. Sliding Mode Control , a type of Variable Structure Controller, has served well over the years in controlling UAVs and other dynamic systems. However, conventional Sliding Mode Control results in a high frequency switching behavior of the control signal. Furthermore, Sliding Mode Control does not focus on fast set-point regulation or tracking, which can be advantageous for UAVs and many other robotic systems.
Taking these research gaps into account, this work presents an Adaptive Variable Structure Control method, which can acquire fast set-point regulation while maintaining robustness against external disturbances and uncertainties. The adaptive algorithm developed in this work is fundamentally different from current Adaptive Sliding Mode Control and other Variable Structure methods. Simulation and experimental results are provided to demonstrate the superiority of the proposed approach compared to Sliding Mode Control. The novel adaptive algorithm is applicable to many nonlinear dynamic systems including UAVs, robot arm manipulators and space robots.
The same adaptive concept is then utilized to develop an Adaptive Second Order Sliding Mode Controller. Compared to existing Second Order Sliding Mode Control methods, the proposed methodology is able to produce reduced sliding manifold reach times and consume less amount of control resources: features which are particularly advantageous for systems with limited control resources. Simulations are conducted to evaluate the performance of the proposed Adaptive Second Order Sliding Mode Control algorithm
Multi-rotor Aerial Vehicles in Physical Interactions: A Survey
Research on Multi-rotor Aerial Vehicles (MAVs) has experienced remarkable
advancements over the past two decades, propelling the field forward at an
accelerated pace. Through the implementation of motion control and the
integration of specialized mechanisms, researchers have unlocked the potential
of MAVs to perform a wide range of tasks in diverse scenarios. Notably, the
literature has highlighted the distinctive attributes of MAVs that endow them
with a competitive edge in physical interaction when compared to other robotic
systems. In this survey, we present a categorization of the various types of
physical interactions in which MAVs are involved, supported by comprehensive
case studies. We examine the approaches employed by researchers to address
different challenges using MAVs and their applications, including the
development of different types of controllers to handle uncertainties inherent
in these interactions. By conducting a thorough analysis of the strengths and
limitations associated with different methodologies, as well as engaging in
discussions about potential enhancements, this survey aims to illuminate the
path for future research focusing on MAVs with high actuation capabilities
Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)
The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones
Modeling, Simulation, and Maneuvering Control of a Generic Submarine
This work introduces two multi-level control strategies to address the
problem of guidance and control of underwater vehicles. An outer-loop
path-following algorithm and an outer-loop trajectory tracking algorithm are
presented. Both outer-loop algorithms provide reference commands that enable
the generic submarine to adhere to a three-dimensional path, and both use an
inner-loop adaptive controller to determine the required actuation commands.
Further, a reduced order model of a generic submarine is presented.
Computational fluid dynamics (CFD) results are used to create and validate a
model that includes depth dependence and the effect of waves on the craft. %The
model and the procedure to obtain its coefficients are discussed, and examples
of the data used to obtain the model coefficients are presented. An example of
operation following a complex path is presented and Results from the reduced
order model for each control strategy are compared.Comment: 12 pages, 14 figures, to be published in Control Engineering
Practice. arXiv admin note: substantial text overlap with arXiv:2212.0982
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