Matthew A. Garratt

Approaches for a tether-guided landing of an autonomous helicopter

In this paper, we address the design of an autopilot for autonomous landing of a helicopter on a rocking ship, due to rough sea. A tether is used for landing and securing a helicopter to the deck of the ship in rough weather. A detailed nonlinear dynamic model for the helicopter is used. This model is underactuated, where the rotational motion couples into the translation. This property is used to design controllers which separate the time scales of rotation and translation. It is shown that the tether tension can be used to couple the translation of the helicopter to the rotation. Two controllers are proposed in this paper. In the first, the rotation time scale is chosen much shorter than the translation, and the rotation reference signals are created to achieve a desired controlled behavior of the translation. In the second, due to coupling of the translation of the helicopter to the rotation through the tether, the translation reference rates are created to achieve a desired controlled behavior of the attitude and altitude. Controller A is proposed for use when the helicopter is far away from the goal, while Controller B is for the case when the helicopter is close to the ship. The proposed control schemes are proved to be robust to the tracking error of its internal loop and results in local exponential stability. The performance of the control system is demonstrated by computer simulations. Currently, work is in progress to implement the algorithm using an instrumented model of a helicopter with a tether.

An overview of insect-inspired guidance for application in ground and airborne platforms

Abstract Flying insects provide a clear demonstration that living organisms can display surprisingly competent mechanisms of guidance and navigation, despite possessing relatively small brains and simple nervous systems. Consequently, they are proving to be excellent organisms in which to investigate how visual information is exploited to guide locomotion and navigation. Four illustrative examples are described here, in the context of navigation to a destination. Bees negotiate narrow gaps by balancing the speeds of the images in the two eyes. Flight speed is regulated by holding constant the average image velocity as seen by the two eyes. This automatically ensures that flight speed is reduced to a safe level when the passage narrows. Smooth landings on a horizontal surface are achieved by holding image velocity constant as the surface is approached, thus automatically ensuring that flight speed is close to zero at touchdown. Roll and pitch are stabilized by balancing the signals registered by three visual organs, the ocelli, that view the horizon in the left, right and forward directions respectively. Tests of the feasibility of these navigational strategies, by implementation in robots, are described.

Velocity Control of a UAV using Backstepping Control

This paper presents novel backstepping based velocity control method for unmanned helicopters. The algorithm is developed from a similar backstepping control for a general rigid body. This approach has enabled a new control scheme and it has made the development of the control algorithm very clear. This approach has enabled the inclusion of flapping dynamics. Further work is needed to include servo dynamics in the control algorithm

Platform Enhancements and System Identification for Control of an Unmanned Helicopter

This paper discusses a common approach to systems integration and automation for two different sizes of rotary wing UAVs. A linear model has been identified using time domain methods for both platforms. The identified models will help to evaluate parameters for designing nonlinear control systems for automatic landing of UAVs on moving platforms. In this paper, the innovation is the enhancement of experimental platforms to better suit experimental research in the design of controllers

Neural Network Based System Identification for Autonomous Flight of an Eagle Helicopter

Abstract Neural Network Identification (NNID) for modeling the dynamics of a miniature Eagle helicopter is presented in this paper. Off-line and on-line identification is carried out for both coupled and decoupled dynamics of the helicopter from the flight test data. For both the cases, identification results and the error statistics are provided. The off-line identification performs better due to sufficient training time and data. Results indicate neural network based black-box method is suitable for modeling the nonlinear dynamics of the helicopter. This can be further applied for the design of Automatic Flight Control System (AFCS).

Flight control of a rotary wing UAV - a practical approach

This paper presents a novel application of the two-time scale controller for the full envelop flight control of a Rotary wing Unmanned Aerial Vehicle (RUAV). In this paper flapping and servo dynamics, important from a practical point of view, is included in the RUAV model. The two-time scale controller takes advantage of the `decoupling¿ of the nonlinear translational and rotation dynamics of the rigid body, resulting in a two-level hierarchical control scheme. The inner loop controller (attitude control) tracks the attitude commands and sets the main rotor thrust vector, while the outer loop controller (position control) tracks the reference position and control the flapping angles and the tail rotor thrust vector. High fidelity RUAV simulation results are used to demonstrate the control performance. Simulation results show acceptable performance of the proposed two-time scale controller. The comparison of control inputs between the proposed two-time scale controller and an already implemented PID controller show that this controller is suitable for practical implementation.

Visual–Inertial Navigation Systems for Aerial Robotics: Sensor Fusion and Technology

In this paper, we comprehensively discuss the current progress of visual–inertial (VI) navigation systems and sensor fusion research with a particular focus on small unmanned aerial vehicles, known as microaerial vehicles (MAVs). Such fusion has become very topical due to the complementary characteristics of the two sensing modalities. We discuss the pros and cons of the most widely implemented VI systems against the navigational and maneuvering capabilities of MAVs. Considering the issue of optimum data fusion from multiple heterogeneous sensors, we examine the potential of the most widely used advanced state estimation techniques (both linear and nonlinear as well as Bayesian and non-Bayesian) against various MAV design considerations. Finally, we highlight several research opportunities and potential challenges associated with each technique.

Hover flight control of a small helicopter using robust backstepping and PID

In this paper, a robust control strategy applying on a small helicopter is proposed. The controller is designed using the backstepping approach based on Lyapunov function. In control design, a hierarchical inner-outer loop based structure is proposed to control the hover flight in the presence of external wind gusts. The outer loop (position control) employs robust backstepping controller to control the translational trajectory, while the inner loop (attitude control) controller is designed by means of PID controller that allow the stabilization of the attitude of a small helicopter. This new method combines the advantages of both robust backstepping and PID, particularly it is simple and easy to implement and tune in future real flight test. Finally, a computer simulation is conducted to show the hover flight control performance of the proposed controller in a gusty environment.

Robust altitude control of an unmanned autonomous helicopter using backstepping

In this paper, a nonlinear robust control technique is proposed to control heave motion for hover as well as vertically take-off/landing of an unmanned autonomous helicopter in the presence of external wind gusts. A heave motion model of a small helicopter is considered to derive the proposed controller for the purposes of capturing dynamic variations of thrust due to the external disturbances. A recursive (backstepping) design procedure is used to design the robust controller for vertical dynamics based on Lyapunov approach. To show the effectiveness of the proposed control method and its ability to cope with the external uncertainties in the vertical dynamics, results are compared with a classical PD controller. Comparative studies demonstrate that the proposed robust backstepping control method greatly enhance the performance over the classical PD controller and it is applied to RUAV hovering condition as well as vertical take-off/landing.

Prediction of vertical motions for landing operations of UAVs

This paper outlines a novel and feasible procedure to predict vertical motions for safe landing of unmanned aerial vehicles (UAVs) during maritime operations. In the presence of stochastic sea state disturbances, dynamic relationship between an observer and a moving deck is captured by the proposed identification model, in which system order is specified by a new order-determination principle based on Bayes Information Criterion (BIC). In addition, the resulting system model is extended to develop accurate multi-step predictors for estimation of vertical motion dynamics. Simulation results demonstrate that the proposed prediction approach substantially reduces the model complexity and exhibits excellent prediction performance, making it suitable for integration into ship-helicopter approaches and landing guidance systems.