Browse Topic: Automatic pilots
Future vertical lift (FVL) missions will be characterized by increased agility, degraded visual environments (DVE) and optionally piloted vehicles (OPVs). Increased agility will induce more frequent variations of linear and angular accelerations, while DVE will reduce the structure and quality of the out-the-window (OTW) scene (i.e. optical flow). As helicopters become faster and more agile, pilots are expected to navigate at low altitudes while traveling at high speeds. In nap of the earth (NOE) flights, the perception of self-position and orientation provided by visual, vestibular, and proprioceptive cues can vary from moment to moment due to visibility conditions and body alignment as a response to gravitoinertial forces and internally/externally induced perturbations. As a result, erroneous perceptions of the self and the environment can arise, leading ultimately to spatial disorientation (SD). In OPV conditions, the use of different autopilot modes implies a modification of pilot role from active pilot to systems supervisor. This shift in paradigm, where pilotage is not the primary task, and where feedback from the controls is no more available, is not without consequences. Of importance is the evidence that space perception and its geometric properties can be strongly modulated by the active or passive nature of the displacement in space. An experiment was conducted using the vertical motion simulator (VMS) at the NASA Ames Research Center that examined the contributions of gravitoinertial cueing and visual cueing in a task where the pilot was not in control of the aircraft but was asked to perform altitude monitoring in a simulated UH-60 Black Hawk helicopter with a simulated autopilot (AP) mode. Within the altitude monitoring task, the global optical density (OD), flow rate and visual level of detail (LOD) were manipulated by the introduction of an 18ft vertical drift, upward or downward that simulates a vertical wind shift. Seven pilots were tested in two visual meteorological conditions, good visual environment (GVE) and degraded visual environment (DVE) and two gravitoinertial conditions, where platform motion was either ON or OFF. The results showed that both the good quality of the visual environment and the presence of gravitoinertial cues improved altitude awareness and reduced detection/ reaction times. The improvement of the tracking performance in the visuo-vestibular setting as compared to a visual only setting when the visual cues were poor indicated some level of multisensory integration. Task-dependent limitations of a popular aeronautics metric called DIMSS-PM (Dynamic Interface Modeling and Simulation System Product Metric) and its sub-components were shown, and recommendations for OPV operations were formulated.
ABSTRACT Accurate characterization of fleet and individual aircraft usage spectrums would allow component retirement times to be based on actual aircraft usage rather than on an assumed worst case usage spectrum used in a traditional time-based maintenance approach. A key enabling technology for such a Usage or Condition Based Maintenance (UBM/CBM) program is Regime Recognition (RR). The development and validation of such algorithms commonly employs flight loads survey data, which captures critical regimes and the corner points of the operational envelope. Such maneuver examples are flown precisely and don’t necessarily capture how fleet aircraft are flown. The maneuver generation approach presented herein presents the foundational elements of a methodology to augment existing flight loads survey data with statistically independent maneuvers that address the desire to capture the variation that would be observed in the fleet as a result of different pilots, vehicle load-out, and environmental conditions via a simulation based autopilot. Such a capability allows developers to cost-effectively conduct comprehensive sensitivity analyses and verification of RR algorithms and the end-to-end process, while reserving high-cost flight test data for true blind validation.
This document recommends criteria for the design and installation of Autopilot, Flight Director and Autothrust Systems. These three systems are highly interrelated and will be referred to generically as an Integrated Flight Guidance System (IFGS).
This paper describes the design, development and flight testing of a meso-scale cyclocopter. Weighing only 29 grams, the present vehicle is the smallest cycloidal rotor based aircraft ever built. Unlike the previous cyclocopters, the current prototype utilizes a novel, light weight (3 grams) cycloidal rotor design, with cantilevered blades, having semi-elliptical planform shape and no exposed rotor shaft. To minimize bending deflections the blades use a unique, lightweight (0.15 grams each) but high strength-to-weight ratio unidirectional carbon-fiber based structural design and are fabricated using a specialized manufacturing process. The cycloidal rotor design was chosen through systematic performance measurements conducted using a custom-built miniature three-component force balance. Based on experimental parametric studies, a 4-bladed rotor and symmetric blade kinematics with pitch amplitude of 45° provided the highest thrust and power loading (thrust/power) and was used in the final rotor design. The airframe is fabricated using a combination of carbon-fiber and state-of-the-art 3D printing techniques. The attitude control strategy utilizes a combination of rpm-control of the two cycloidal-rotors/tail-rotor and thrust vectoring of the cycloidal rotors. The control strategy is implemented on a custom-built 1.3 gram autopilot, which uses a closed-loop proportional-derivative controller for hover stability. The vehicle has been systematically flight tested by tuning the feedback gains and has demonstrated stable hovering flight.
This paper presents the first ever linear system identification of the flight dynamics of a hover-capable robotic hummingbird which utilizes only two wings for flying as well as for all its control and stabilization. The vehicle was developed in-house, using state-of-the-art materials, electronics, and innovative design/fabrication techniques, and a description of its development is provided. Systematic experimental studies were conducted to develop flexible, aeroelastically tailored wings, along with novel wing kinematic modulation mechanisms for controlling roll, pitch and yaw. Additionally, a custom, lightweight, autopilot implementing PID control was developed, and after a series of rigorous flight testing, the trim and feedback gains were determined which allowed stable, hovering flight. Once this was achieved, a motion capture camera system was used to track the position and attitude of the vehicle during flight tests which involved providing a series of inputs to excite the vehicle modes and measuring the response. A linearized six degree of freedom state-space model for hovering flight was then extracted from this data using time domain system identification. An analysis of the eigenstate of the model reveals four modes present: an unstable mode that excites all the vehicle states; a stable mode that excites yaw and translational motion; and two oscillatory modes, one stable and one marginally unstable, both responsible for translational and rotational excitation about all axes. This finally demonstrates experimentally for the first time the unstable nature of two-winged, hover-capable flapping flight. These four modes, which show a strong coupling between the longitudinal and lateral dynamics, suggest that assumptions which decouple longitudinal and lateral degrees of freedom may not be valid for this type of flight. Additionally, this unstable, coupled dynamics demonstrates definitively the superior agility inherent in this type of aerial locomotion.
Autopilot analysis is a very complex stage in the design of an airplane or a helicopter. In addition to providing maneuverability criteria, the autopilot must be robust to uncertainties or changes in physical parameters changes. Using the LQR (Linear Quadratic Regulator) theory, a genetic algorithm and the guardian map theory, a methodology is described for designing an internal controller to be used by an the autopilot which satisfies accurate handling qualities while remaining robust. The algorithms were developed in Matlab® and the simulations were realized with Simulink®. A full nonlinear model of the Cessna Citation X and six linear models of the Lynx Helicopter for different speeds are used to show the results and evaluate the efficiency of the methodology.
These recommendations cover the mechanical and electrical installation and installation test procedures for automatic pilots of the type normally used in transport type aircraft. The material in this ARP does not supercede any airworthiness requirement in the Civil Air Regulations.
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