263 research outputs found
Thrust Vector Controller Comparison for a Finless Rocket
No full textThe paper focuses on comparing applicability, tuning, and performance of different controllers implemented and tested on a finless rocket during its boost phase. The objective was to evaluate the advantages and disadvantages of each controller, such that the most appropriate one would then be developed and implemented in real-time in the finless rocket. The compared controllers were Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), and Proportional Integral Derivative (PID). To control the attitude of the rocket, emphasis is given to the Thrust Vector Control (TVC) component (sub-system) through the gimballing of the rocket engine. The launcher is commanded through the control input thrust gimbal angle δ, while the output parameter is expressed in terms of the pitch angle θ. After deriving a linearized state–space model, rocket stability is addressed before controller implementation and testing. The comparative study showed that both LQR and LQG track pitch angle changes rapidly, thus providing efficient closed-loop dynamic tracking. Tuning of the LQR controller, through the Q and R weighting matrices, illustrates how variations directly affect performance of the closed-loop system by varying the values of the feedback gain (K). The LQG controller provides a more realistic profile because, in general, not all variables are measurable and available for feedback. However, disturbances affecting the system are better handled and reduced with the PID controller, thus overcoming steady-state errors due to aerodynamic and model uncertainty. Overall controller performance is evaluated in terms of overshoot, settling and rise time, and steady-state error
Sensors for missions
No full textAn onboard payload may be seen in most instances as the “Raison d’Etre” for a UAV. It will define its capabilities, usability and hence market value. Large and medium UAV payloads exhibit significant differences in size and computing capability when compared with small UAVs. The latter have stringent size, weight, and power requirements, typically referred as SWaP, while the former still exhibit endless appetite for compute capability. The tendency for this type of UAVs (Global Hawk, Hunter, Fire Scout, etc.) is to increase payload density and hence processing capability. An example of this approach is the Northrop Grumman MQ-8 Fire Scout helicopter, which has a modular payload architecture that incorporates off-the-shelf components. Regardless of the UAV size and capabilities, advances in miniaturization of electronics are enabling the replacement of multiprocessing, power-hungry general-purpose processors for more integrated and compact electronics (e.g., FPGAs).\ud
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Payloads play a significant role in the quality of ISR (intelligent, surveillance, and reconnaissance) data, and also in how quick that information can be delivered to the end user. At a high level, payloads are important enablers of greater mission autonomy, which is the ultimate aim in every UAV.\ud
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This section describes common payload sensors and introduces two examples cases in which onboard payloads were used to solve real-world problems. A collision avoidance payload based on electro optical (EO) sensors is first introduced, followed by a remote sensing application for power line inspection and vegetation management
Data fusion and tracking with multiple UAVs
No full textThis chapter describes decentralized data fusion algorithms for a team of multiple autonomous platforms. Decentralized data fusion (DDF) provides a useful basis with which to build upon for cooperative information gathering tasks for robotic teams operating in outdoor environments. Through the DDF algorithms, each platform can maintain a consistent global solution from which decisions may then be made. Comparisons will be made between the implementation of DDF using two probabilistic representations. The first, Gaussian estimates and the second Gaussian mixtures are compared using a common data set. The overall system design is detailed, providing insight into the overall complexity of implementing a robust DDF system for use in information gathering tasks in outdoor UAV applications
The safety risk management of unmanned aircraft systems
No full textThe safety risk management process describes the systematic application of management policies, procedures and practices to the activities of communicating, consulting, establishing the context, and identifying, analysing, evaluating, treating, monitoring and reviewing risk. This process is undertaken to provide assurances that the risks of a particular unmanned aircraft system activity have been managed to an acceptable level. The safety risk management process and its outcomes form part of the documented safety case necessary to obtain approvals for unmanned aircraft system operations. It also guides the development of an organisation’s operations manual and is a primary component of an organisation’s safety management system. The aim of this chapter is to provide existing risk practitioners with a high level introduction to some of the unique issues and challenges in the application of the safety risk management process to unmanned aircraft systems. The scope is limited to safety risks associated with the operation of unmanned aircraft in the civil airspace system and over inhabited areas. The structure of the chapter is based on the safety risk management process as defined by the international risk management standard ISO 31000:2009 and draws on aviation safety resources provided by International Civil Aviation Organization, the Federal Aviation Administration and U.S. Department of Defense. References to relevant aviation safety regulations, programs of research and fielded systems are also provided
Correction to the Euler Lagrange multirotor model with Euler angles generalized coordinates
No full textThis technical note proves analytically how the exact equivalence of the Newton-Euler and Euler-Lagrange modeling formulations as applied to multirotor UAVs is achieved. This is done by deriving a correct Euler-Lagrange multirotor attitude dynamics model. A review of the published literature reveals that the commonly adopted Euler-Lagrange multirotor dynamics model is equivalent to the Newton-Euler model only when it comes to the position dynamics, but not in the attitude dynamics. Step-by-step derivations and calculations are provided to show how modeling equivalence to the Newton-Euler formulation is proven. The modeling equivalence is then verified by obtaining identical results in numerical simulation studies. Simulation results also illustrate that when using the correct model for feedback linearization, controller stability at high gains is improved
Koopman-based Model Predictive Control of quadrotors
No full textA novel formulation of model predictive control (MPC) coupled with Koopman operator theory is presented and tested for the trajectory tracking problem of a quadrotor UAV. The analytical derivation of Koopman observables allows for the quadrotor model to be written as a fully-actuated quasi-linear system which enables the control problem to be posed as a linear control problem. In fact, the adopted approach embeds the quadrotor nonlinear dynamics into a quasi-linear form through the evolution of the Koopman op-erator generalized eigenfunctions, a special kind of Koopman observables. Hence, the linear MPC formulation in Koopman coordinates is equivalent to a nonlinear implementation in the original state space. Moreover, in an enhancement from the standard feedback linearization, the Koopman based quadro-tor model does not present underactuation, which drastically simplifies the computational requirement for the solution of the MPC optimization problem. The presented methodology is tested through detailed numerical simulations and results are compared to single-loop nonlinear MPC (NMPC). The satisfactory tracking performance are additionally enhanced by the obtained computational speedup which is crucial for real time implementation of flight controllers
Reinforcement Learning-Based PD Controller Gains Prediction for Quadrotor UAVs
No full textThis paper presents a reinforcement learning (RL)-based methodology for the online fine-tuning of PD controller gains, with the goal of bridging the gap between simulation-trained controllers and real-world quadrotor applications. As a first step toward real-world implementation, the proposed approach applies a Deep Deterministic Policy Gradient (DDPG) algorithm—an off-policy actor–critic method—to adjust the gains of a quadrotor attitude PD controller during flight. The RL agent was initially trained offline in a simulated environment, using MATLAB/Simulink 2024a and the UAV Toolbox Support Package for PX4 Autopilots v1.14.0. The trained controller was then validated through both simulation and experimental flight tests. Comparative performance analyses were conducted between the hand-tuned and RL-tuned controllers. Our results demonstrate that the RL-based tuning method successfully adapts the controller gains in real time, leading to improved attitude tracking and reduced steady-state error. This study constitutes the first stage of a broader research effort investigating RL-based PID, LQR, MRAC, and Koopman-integrated RL-based PID controllers for real-time quadrotor control
Scalable Approximate Optimization of Objective Functions Represented by Random Forests
No full textThe problem of global optimization of an objective function represented by a Random Forest (RF) is considered. A method to obtain an approximate solution at low computational complexity is proposed, resorting to the inherent structure of an RF, which is a non-parametric model that partitions the feature space in convex polytopes according to the training data. The approach selects the optimal solution inside the polytopes corresponding to the best data points. It is shown that the proposed approximate method is significantly more efficient, thus applicable at large scale, than extensive global search algorithms, such as gridding and Mixed Integer Linear Programming (MILP), which in turn provide exact solutions. The efficiency and sub-optimality of the approach are evaluated on RFs trained on a dataset generated by sampling a bivariate, discontinuous and non-convex benchmark function from the literature
Computer Vision Onboard UAVs for Civilian Tasks
No full textComputer visión is much more than a technique to sense and recover environmental information from an UAV. It should play a main role regarding UAVs' functionality because oí the big amount oí information that can be extracted, its possible uses and applications, and its natural connection to human driven tasks, taking into account that visión is our main interface to world understanding. Our current research's focus lays on the development of techniques that allow UAVs to maneuver in spaces using visual information as their main input source. This task involves the creation of techniques that allow an UAV to maneuver towards features of interest whenever a GPS signal is not reliable or sufficient, e.g. when signal dropouts occur (which usually happens in urban áreas, when flying through terrestrial urban canyons or when operating on remote planetary bodies), or when tracking or inspecting visual targets—including moving ones—without knowing their exact UMT coordinates. This paper also investigates visual servoing control techniques that use velocity and position of suitable image features to compute the references for flight control. This paper aims to give a global view of the main aspects related to the research field of computer visión for UAVs, clustered in four main active research lines: visual servoing and control, stereo-based visual navigation, image processing algorithms for detection and tracking, and visual SLAM. Finally, the results of applying these techniques in several applications are presented and discussed: this study will encompass power line inspection, mobile target tracking, stereo distance estimation, mapping and positioning
Hierarchical Time-Extended Petri Nets (H-EPNs) for Integrated Control and Diagnostics of Multilevel Systems
No full textRamaswamy, Srinivasan, B.S., University of Madras, May 1989 Master of Science, University of Southwestern Louisiana, July 1992 Doctor of Philosophy, May 1994 Ttile of Dissertation: Hierarchical Time-Extended Petri Nets (H-EPNs) for Integrated Control and Diagnostics of Multilevel Systems Dissertation Directed by Drs. Kimon P. Valavanis & Padmini Srinivasan Pages in Dissertation, 157; Words in Abstract 300 The objective of this research is to develop a model-based approach to the modeling, analysis and design of multilevel systems. Petri Nets are used as the basic modeling tool because of their versatility in the modeling and analysis of dynamic system behavior. Moreover, Petri Nets have a strong mathematical background and Petri Net analysis techniques are well established. Hierarchical Time-Extended Petri Nets (H-EPNs) are proposed as a hybrid modeling and analysis tool for the derivation of the coordination level model [205, 231] of hierarchically decomposable systems, viewe..
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