1,372 research outputs found
Aerial Vehicles
This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space
Accurate Tracking of Aggressive Quadrotor Trajectories using Incremental Nonlinear Dynamic Inversion and Differential Flatness
Autonomous unmanned aerial vehicles (UAVs) that can execute aggressive (i.e.,
high-speed and high-acceleration) maneuvers have attracted significant
attention in the past few years. This paper focuses on accurate tracking of
aggressive quadcopter trajectories. We propose a novel control law for tracking
of position and yaw angle and their derivatives of up to fourth order,
specifically, velocity, acceleration, jerk, and snap along with yaw rate and
yaw acceleration. Jerk and snap are tracked using feedforward inputs for
angular rate and angular acceleration based on the differential flatness of the
quadcopter dynamics. Snap tracking requires direct control of body torque,
which we achieve using closed-loop motor speed control based on measurements
from optical encoders attached to the motors. The controller utilizes
incremental nonlinear dynamic inversion (INDI) for robust tracking of linear
and angular accelerations despite external disturbances, such as aerodynamic
drag forces. Hence, prior modeling of aerodynamic effects is not required. We
rigorously analyze the proposed control law through response analysis, and we
demonstrate it in experiments. The controller enables a quadcopter UAV to track
complex 3D trajectories, reaching speeds up to 12.9 m/s and accelerations up to
2.1g, while keeping the root-mean-square tracking error down to 6.6 cm, in a
flight volume that is roughly 18 m by 7 m and 3 m tall. We also demonstrate the
robustness of the controller by attaching a drag plate to the UAV in flight
tests and by pulling on the UAV with a rope during hover.Comment: To be published in IEEE Transactions on Control Systems Technology.
Revision: new set of experiments at increased speed (up to 12.9 m/s), updated
controller design using quaternion representation, new video available at
https://youtu.be/K15lNBAKDC
Advanced control for miniature helicopters : modelling, design and flight test
Unmanned aerial vehicles (UAV) have been receiving unprecedented development during the past two decades. Among different types of UAVs, unmanned helicopters exhibit promising features gained from vertical-takeoff-and-landing, which make them as a versatile platform for both military and civil applications. The work reported in this thesis aims to apply advanced control techniques, in particular model predictive control (MPC), to an autonomous helicopter in order to enhance its performance and capability. First, a rapid prototyping testbed is developed to enable indoor flight testing for miniature helicopters. This testbed is able to simultaneously observe the flight state, carry out complicated algorithms and realtime control of helicopters all in a Matlab/Simulink environment, which provides a streamline process from algorithm development, simulation to flight tests. Next, the modelling and system identification for small-scale helicopters are studied. A parametric model is developed and the unknown parameters are estimated through the designed identification process. After a mathematical model of the selected helicopter is available, three MPC based control algorithms are developed focusing on different aspects in the operation of autonomous helicopters. The first algorithm is a nonlinear MPC framework. A piecewise constant scheme is used in the MPC formulation to reduce the intensive computation load. A two-level framework is suggested where the nonlinear MPC is combined with a low-level linear controller to allow its application on the systems with fast dynamics. The second algorithm solves the local path planning and the successive tracking control by using nonlinear and linear MPC, respectively. The kinematics and obstacle information are incorporated in the path planning, and the linear dynamics are used to design a flight controller. A guidance compensator dynamically links the path planner and flight controller. The third algorithm focuses on the further reduction of computational load in a MPC scheme and the trajectory tracking control in the presence of uncertainties and disturbances. An explicit nonlinear MPC is developed for helicopters to avoid online optimisation, which is then integrated with a nonlinear disturbance observer to significantly improve its robustness and disturbance attenuation. All these algorithms have been verified by flight tests for autonomous helicopters in the dedicated rapid prototyping testbed developed in this thesis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Adaptive and Optimal Motion Control of Multi-UAV Systems
This thesis studies trajectory tracking and coordination control problems for single and multi unmanned aerial vehicle (UAV) systems. These control problems are addressed for both quadrotor and fixed-wing UAV cases. Despite the fact that the literature has some approaches for both problems, most of the previous studies have implementation challenges on real-time systems. In this thesis, we use a hierarchical modular approach where the high-level coordination and formation control tasks are separated from low-level individual UAV motion control tasks. This separation helps efficient and systematic optimal control synthesis robust to effects of nonlinearities, uncertainties and external disturbances at both levels, independently. The modular two-level control structure is convenient in extending single-UAV motion control design to coordination control of multi-UAV systems. Therefore, we examine single quadrotor UAV trajectory tracking problems to develop advanced controllers compensating effects of nonlinearities and uncertainties, and improving robustness and optimality for tracking performance. At fi rst, a novel adaptive linear quadratic tracking (ALQT) scheme is developed for stabilization and optimal attitude control of the quadrotor UAV system. In the implementation, the proposed scheme is integrated with Kalman based reliable attitude estimators, which compensate measurement noises. Next, in order to guarantee prescribed transient and steady-state tracking performances, we have designed a novel backstepping based adaptive controller that is robust to effects of underactuated dynamics, nonlinearities and model uncertainties, e.g., inertial and rotational drag uncertainties. The tracking performance is guaranteed to utilize a prescribed performance bound (PPB) based error transformation. In the coordination control of multi-UAV systems, following the two-level control structure, at high-level, we design a distributed hierarchical (leader-follower) 3D formation control scheme. Then, the low-level control design is based on the optimal and adaptive control designs performed for each quadrotor UAV separately. As particular approaches, we design an adaptive mixing controller (AMC) to improve robustness to varying parametric uncertainties and an adaptive linear quadratic controller (ALQC). Lastly, for planar motion, especially for constant altitude flight of fixed-wing UAVs, in 2D, a distributed hierarchical (leader-follower) formation control scheme at the high-level and a linear quadratic tracking (LQT) scheme at the low-level are developed for tracking and formation control problems of the fixed-wing UAV systems to examine the non-holonomic motion case. The proposed control methods are tested via simulations
and experiments on a multi-quadrotor UAV system testbed
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
There exists an increasing demand for a flexible and computationally
efficient controller for micro aerial vehicles (MAVs) due to a high degree of
environmental perturbations. In this work, an evolving neuro-fuzzy controller,
namely Parsimonious Controller (PAC) is proposed. It features fewer network
parameters than conventional approaches due to the absence of rule premise
parameters. PAC is built upon a recently developed evolving neuro-fuzzy system
known as parsimonious learning machine (PALM) and adopts new rule growing and
pruning modules derived from the approximation of bias and variance. These rule
adaptation methods have no reliance on user-defined thresholds, thereby
increasing the PAC's autonomy for real-time deployment. PAC adapts the
consequent parameters with the sliding mode control (SMC) theory in the
single-pass fashion. The boundedness and convergence of the closed-loop control
system's tracking error and the controller's consequent parameters are
confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's
efficacy is evaluated by observing various trajectory tracking performance from
a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing
micro aerial vehicle called hexacopter. Furthermore, it is compared to three
distinctive controllers. Our PAC outperforms the linear PID controller and
feed-forward neural network (FFNN) based nonlinear adaptive controller.
Compared to its predecessor, G-controller, the tracking accuracy is comparable,
but the PAC incurs significantly fewer parameters to attain similar or better
performance than the G-controller.Comment: This paper has been accepted for publication in Information Science
Journal 201
Explicit non-linear model predictive control for autonomous helicopters
Trajectory tracking is a basic function required for autonomous helicopters, but it also poses challenges to control design due to the complexity of helicopter dynamics. This article introduces an explicit model predictive control (MPC) to solve this problem, which inherits the advantages of non-linear MPC but eliminates time-consuming online optimization. The explicit solution to the non-linear MPC problem is derived using Taylor expansion and exploiting the helicopter model. With the explicit MPC solution, the control signals can be calculated instantaneously to respond to the fast dynamics of helicopters and suppress disturbances immediately. On the other hand, the online optimization process can be removed from the MPC framework, which can accelerate the software development and simplify onboard hardware. Due to these advantages of the proposed method, the overall control framework has a low complexity and high reliability, and it is easy to deploy on small-scale helicopters. The proposed explicit non-linear MPC has been successfully validated in simulations and in actual flight tests using a Trex-250 small-scale helicopter
Optimal control of telecommunication aeroplatform in the area of emergency
This paper addresses to a method for increasing of mobile ad-hoc networks throughput based on the placement control of unmanned aerial vehicles in the area of emergency situation. A further development of this method, namely the improvement of UAV flight control subsystem, that will allow operative implementation of obtained in the previous stage location coordinates while minimizing energy consumption for control, is proposed. The proposed approach will improve network throughput by 15β20% while reducing fuel costs by an average of 13β15%
Optimal control of a helicopter unmanned aerial vehicle (UAV)
This thesis addresses optimal control of a helicopter unmanned aerial vehicle (UAV). Helicopter UAVs may be widely used for both military and civilian operations. Because these helicopters are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This thesis presents an optimal controller design via both state and output feedback for trajectory tracking of a helicopter UAV using a neural network (NN). The state and output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers while the output feedback approach uses an observer in addition to these controllers. The online approximator-based dynamic controller learns the Hamilton-Jacobi-Bellman (HJB) equation in continuous time and calculates the corresponding optimal control input to minimize the HJB equation forward-in-time. Optimal tracking is accomplished with a single NN utilized for cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking. A description of the hardware for confirming the theoretical approach, and a discussion of material pertaining to the algorithms used and methods employed specific to the hardware implementation is also included. Additional attention is devoted to challenges in implementation as well as to opportunities for further research in this field. This thesis is presented in the form of two papers --Abstract, page iv
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