Browse Topic: Automatic pilots
A new guidance optimization scheme for spacing waypoints on spline trajectories is proposed. This scheme, the bounded area minimization algorithm, examines sequences of 3 waypoints that sample a given spline trajectory at its constituent knot locations and moves the interior waypoint to a location on the spline trajectory that minimizes the bounded area, computed using Green’s theorem, between the trajectory and the straight-line paths (legs) that connect adjacent waypoints. For spline trajectories defined by more than 3 knots, the algorithm can be applied sequentially to cover the entire chain of knots. Five motion primitives were chosen to test the performance of the optimization scheme on piecewise cubic polynomial spline trajectories. Two of these motion primitives (sinusoid and exponential) as well as two real-world trajectories that have been flight-tested on an MD530F platform were then simulated in a full nonlinear rotorcraft flight dynamics simulator to quantify and compare the effects of optimized versus baseline waypoint spacing. The bounded area minimization algorithm was extremely effective at reducing cumulative cross track error when the waypoint spacing was large enough that the aircraft trajectories closely matched the straight-line waypoint legs used in the algorithm. A method of determining the smallest waypoint spacing at which the optimization algorithm is still beneficial was proposed based on varying the wavelength of the ADS-33 Slalom MTE. For densely sampled spline trajectories that do not have knot spacings large enough to realize the benefits of the optimization algorithm, knot removal can sometimes reduce the number of waypoints required to represent the trajectory while maintaining the cumulative cross track error. However, there is no guarantee that subsequent bounded area minimization results in better performance than the baseline waypoint spacing. Determination of waypoint spacing for densely sampled spline trajectories is a recommended area of future work. A practical benefit of the bounded area minimization algorithm is that it requires very little modification to in-service coupled waypoint guidance flight director and autopilot schemes on existing aircraft, which improves the likelihood of its adoption by reducing the effort required for formal qualification and certification.
Artificial intelligence (AI) has become prevalent in many fields in the modern world, ranging from vacuum cleaners to lawn mowers and commercial automobiles. These capabilities are continuing to evolve and become a part of more products and systems every day, with numerous potential benefits to humans. AI is of particular interest in autonomous vehicles (AVs), where the benefits include reduced cognitive workload, increased efficiency, and improved safety for human operators. Numerous investments from academia and industry have been made recently with the intent of improving the enabling technologies for AVs. Google and Tesla are two of the more well-known examples in industry, with Google developing a self-driving car and Tesla providing its Full Self-Driving (FSD) autopilot system. Ford and BMW are also working on their own AVs.
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. As rotorcrafts become faster and more agile, pilots are expected to navigate at low altitudes while traveling at high speeds. In contour terrain flight, 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 transforms the pilot's 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 not available, has important consequences. Indeed, space perception can be strongly modulated by the nature of the displacement in space. Considering the relationships between the level of automation (LOA) and sense of agency (SoA), it is of particular interest to examine whether motor control mechanisms can modulate the level of visual-vestibular integration in tasks of movement perception vs. movement control. An experiment was conducted using the NASA AMES vertical motion simulator (VMS) to evaluate the effects of optical and gravitoinertial cues in the assessment of altitude in contour terrain flight. Seven U.S. Army pilots participated in the experiment. The aim of the proposed research was a) to establish the relative contribution of visual and gravitoinertial cues as a function of the quality of the visual cues (good vs. degraded) and the presence or absence of gravitoinertial cues; b) to determine the role of manual control vs. supervisory monitoring control on the estimation of altitude, and c) study the interactions between the nature and the quality of the sensory cues and the type of control. For the supervisory control condition, the results showed that the gravitoinertial component played a significant role in the estimation of ground height, but only in the case where the optical structure did not efficiently specify the actor-environment interaction. Meanwhile, the results for the manual control task provided evidence, at multiple levels, that the acceleration information, specified by the variations of the gravitoinertial field, has a relative character. Altogether, these results are in line with the Sensory Weighted Approach of perception, which proposes that each sensory cue is weighted depending on this reliability: gravitoinertial information is attenuated when the visual information is relevant while it enhances performance when the visual information is poor.
The development of connected and autonomous vehicles (CAVs) is progressing fast. Yet, safety and standardization-related discussions are limited due to the recent nature of the sector. Despite the effort that is initiated to kick-start the study, awareness among practitioners is still low. Hence, further effort is required to stimulate this discussion. Among the available works on CAV safety, some of them take inspiration from the aviation sector that has strict safety regulations. The underlying reason is the experience that has been gained over the decades. However, the literature still lacks a thorough association between automation in aviation and the CAV from the safety perspective. As such, this paper motivates the adoption of safe-automation knowledge from aviation to facilitate safer CAV systems. The authors briefly elaborate on the widely discussed aviation themes, including autopilot and auto-throttle malfunctions, flight management system, human factors, and suggests how this knowledge can improve the safety of road CAVs use-case. Besides, the differences between the safety consideration in the two fields are also denoted. In summary, the main aim of this paper is to highlight the potential benefits of adopting aviation automation safety knowledge into safe CAV development. With the advances in the CAV, the authors are convinced that this subject could serve software developers and engineers in developing safe and standardized CAV technology.
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.
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.
Level 2 (L2) partially automated vehicle systems require the driver to continuously monitor the driving environment and be prepared to take control immediately if necessary. One of the main challenges facing developers of these systems is how to ensure that drivers understand their role and stay alert as the systems require. With little real world data, it has been difficult to understand user attitudes and behaviors toward the implementation and use of partially automated vehicles. At the time of this study, Tesla was one of the few OEMs with a partially automated vehicle feature available on the market; Autopilot. In order to understand how customers interact with a partially automated vehicle, a study was conducted to observe people driving their own Tesla vehicles while autopilot was engaged. Sixteen Tesla owners (14 males and 2 females) between ages 25 to 60 had their vehicles instrumented with video/audio data collection systems for three consecutive days. These owners were dedicated autopilot users who used the feature daily and primarily on highways. Results from the study show that (i) participants’ eye off-road glance behavior while operating a partially automated vehicle was somewhat similar to eye off-road glance behavior in radio tuning task while driving manually, though substantially more variable, (ii) eyes on/off road glance behavior had no relation to whether drivers kept their hands on the steering wheel or not, (iii) drivers exhibit a bi-modal behavior when operating a partially automated vehicle: those who mostly kept their hands on steering wheel while autopilot was active (i.e. active drivers) verses those who mostly kept their hands off the steering wheel while autopilot was active (i.e. supervisors).
Today's vehicles are being more often equipped with systems, which are autonomously influencing the vehicle behavior. More systems of the kind and even fully autonomous vehicles in regular traffic are expected by OEMs in Europe around year 2025. Driving is highly multitasking activity and human errors emerge in situations, when he is unable to process and understand the essential amount of information. Future autonomous systems very often rely on some type of inter-vehicular communication. This shall provide the vehicle with higher amount of information, than driver uses in his decision making process. Therefore, currently used 1-D quantity TTC (time-to-collision) will become inadequate. Regardless the vehicle is driven by human or robot, it’s always necessary to know, whether and which reaction is necessary to perform. Adaptable autonomous vehicle systems will need to analyze the driver’s situation awareness level. Such knowledge can be enhanced by 2-D quantity, so called reaction space, and its entropy. The new approach defines a limit space, where ego vehicle or other vehicles can be present in the future specified by an amount of time. This enables the option of counting not only with braking time, but mitigation by changing direction is feasible. Opposed to TTC, considering time as an input is appreciated especially when switching from autonomous to manual driving. For such situation we observe two kinds of reaction spaces – one, connected with the requirements of autopilot, and second, resulting from the expected human reaction. Effects of entropy in 2-D reaction space are presented in the paper.
Switching controls are those that can switch between control or plant modes to perform their functions. They have the advantage of being simpler to design than an equivalent control system with a single mode. However, the transients between those modes can introduce steps or overshootings in the state variables, and this can degrade the performance or even damage the control or the plant. So, the smoothing of such transients is vital for their reliability and mantainability. This is can be of extreme importance in the aerospace and automotive fields, plenty of switchings between manual and autopilot modes via relays, or among gears via clutches, for example. In this work, we present a first strategy for smoothing transients in switching controls of aerospace and automotive systems. To do that, we review the literature, present and adopt a criterion to determine the coefficients of a control system which should optimize the trajectory of the control signal during the switching between two modes. The chosen criteria are the classical integral of the time times the square of the error (ITSE), and the integral of the time times the module of the error (ITME). Effectively, each transition will be done by a subsystem specific for it, according to the selected criterion. The system will be chosen from relevant cases of the literature. The simulations will be made in MATRIXx@ or MATLAB@. The results obtained so far suggest that the proposed strategy effectively reduces the steps or overshootings in the transients between those switching modes and can contribute for the reliability and mantainability of aerospace and automotive systems.
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.
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.
There are many applications for Autonomous Seaborne Vessels (ASVs). The seaborne cargo shipping industry moves over 9 billion tons of cargo per year, is worth $375 billion, and is responsible for 90 percent of world trade. Autonomous cargo ships could reduce the operating expenses of cargo ships by 44%. ASVs can also be used by the military for surveillance, and for autopilot of pleasure ships.
A long endurance high efficiency Unmanned Aerial Vehicle (UAV) is being developed by a group of researchers and students in the Mechanical Engineering Technology program at Algonquin College, Ottawa, ON, Canada. The design is based on a tailless, staggered tandem wing configuration, with a carbon fiber frame and electric propulsion. The developed aircraft has a maximum weight of 12.5 kg, well within the 25 kg limit outlined by Transport Canada for permission-free operation. The UAV was designed to fly missions exceeding 24 hours, performing surveillance and oil pipeline monitoring and inspection, either autonomously or under radio control from a ground station, with medium to high payload capacity. This paper describes the process of designing, manufacturing and testing the developed configuration. The operational requirements are delineated as conceptualized by the development team. A description of the prototype development, including aerodynamics, structural and stability performance, and stability derivatives is included, based on data generated through analytical analysis, simulations in the aerodynamics and stability design software, XFLR5, and testing. The configuration was developed and modeled in the software package SolidWorks, leading to the manufacturing plans for the prototype. As this UAV performs radio controlled and autonomous flights, it makes use of the APM Arduplane, which is an open source hardware and software autopilot package. In order to achieve PID tuning, Hardware in the Loop (HIL) simulations were performed between the autopilot board and two computers simultaneously running the Mission Planner software and the flight simulator X-Plane 10 software. A description of this tuning process is provided. The paper concludes with the results of performed flight tests, documenting performance achievements, and an outline of recommendations for further development.
Control systems that can switch between control or plant modes have the advantage of being simpler to design than an equivalent system with a single mode. However, the transition between these modes can introduce steps or overshootings in the state variables, and this can degrade the performance or even damage the system. This is can be of extreme importance in fields such as aerospace and automobilistic, as the switching between manual and autopilot modes or the switching of gears In this work, we will use integral criteria in original ways, to determine a coefficient on the system which should optimize the trajectory of the control signal, during the switching between two modes. Effectively, each transition will be done by a subsystem specific for it, according to the selected criterion. The simulations will be made in MATRIXx, MatLab or both, using models chosen from aerospace or automobilistic fields.
The dynamics in the vicinity of small bodies are highly nonlinear. Trajectory design in small-body environments requires accurate gravity and solar radiation pressure models to guarantee the satisfaction of spacecraft operational constraints such as thruster silent times, state, and control constraints. The G-PROX guidance algorithm generates fuel-optimal trajectories in the vicinity of asteroids and small bodies. The non-convexity in the control constraints is handled with the lossless convexification technique, which is a convex relaxation of the control constraints. G-PROX uses sequential convex programming and solves a convergent sequence of convex optimization problems generated via sequential linearization of both the dynamics and control bounds, synergistically combined with lossless convexification. The sequence of convex optimization problems converges to a locally optimal solution of the original nonlinear non-convex problem.
ABSTRACT 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.
In this paper, we study a problem of control system design for small-scale helicopter that has been applied to a robotic helicopter project. The structure of the mathematical models of single-rotor helicopter and the description of its constituent elements are presented. The general mathematical model of a helicopter is a complex multivariable system. This model consists of nonlinear differential equations of the helicopter dynamics, the kinematics and auxiliary equations. The control forces and moments, and also the external disturbances, that affecting on helicopter flight, are in the right side of the dynamic equations. It is necessary to have experimental data for helicopter flight parameters to get adequate auxiliary equations. Those equations have been applied to associate the control forces and moments, to control positions of actuators. In this paper we present the experimental results, estimation algorithms and data-processing. The experimental study includes a series of test flights combining with sets of basic flight configurations, such as take-off/landing, take-off/hovering and etc. The control signals have been recorded during flight tests. There are a set of signals connected to actuators of collective/cyclic pitch mechanism, tail rotor and engine. In postprocessing procedure we are suggesting approaches of data smoothing and filtering. The procedure includes estimation algorithms of the helicopter mass center, forces and moments that affecting on the helicopter. Those estimations are based on helicopter dynamics. This paper presents the results of helicopter mathematical model study, algorithms of helicopter control system synthesis and autopilot structure. The helicopter control system is based on algorithms of position-trajectory control. The hardware and software parts of autopilot are described in the paper. This paper shows results of hardware-in-the-loop (HIL) simulation of helicopter autopilot performance including the mathematical model and external wind disturbances.
Building upon the recently certified Wide Area Augmentation System (WAAS) enabled Localizer Performance with Vertical guidance (LPV) approaches for helicopters, including the Bell Model 429, Bell Helicopter is actively developing technologies to significantly improve helicopter operational capabilities in low-visibility Instrument Flight Rules (IFR) conditions. In 2011, Bell developed and flight tested autopilot-coupled LPV decelerating approaches to a hover, below the current Federal Aviation Administration (FAA) Decision Altitude (DA) standard of at least 200–250 ft. Through use of the WAAS infrastructure, low-speed sensors, and a 4-axis autopilot on a Bell Model 429, Bell has flown specially developed LPV procedures, which could enable safe flight in more congested areas and in low-visibility conditions directly to and from the helipad. This broader envelope of operations would enable more flights which would otherwise be canceled due to inclement weather – resulting in a safer airspace infrastructure and increased efficiency and mission capability for helicopter operators.
Remotely piloted air vehicles are getting smaller in size as technology permits previously unattainable performance from smaller packages. It was inevitable that emerging technologies combining composite lightweight materials, electric motors, long-life battery power, and miniaturized electronic onboard systems would cascade the benefits of UAVs down the operational requirements chain so that real-time aerial imagery and positional data could be supplied to small combat units in the field, and even to individual soldiers. Seeing over the next hill, or around the corner of the next building, is no longer dependent on calling in valuable and scarce air assets; instead, it can be achieved by launching and monitoring the results using small localized UAVs, often looking more like radio-controlled model airplanes than serious military assets.
In the development process, the test phase is considered the most important challenge for designers of safety critical avionic systems found in modern helicopters. Indeed, these Test Systems often operate in uncertain conditions and they must provide safety, fault tolerance, and deterministic timing guarantees. Due to the ever-changing face of technology, the Eurocopter research department leads to the development of Pro- Active Test Systems. In the current state-of-the-art industrial practice, different test benches are used for the verification of various helicopter ranges (EC175, EC135, etc.) and Unit(s)-Under-Test (UUTs) (automatic pilot, navigation, etc.). Each test bench relies on a specific hardware architecture and software tools. This is due to the heterogeneity of the helicopter parts (which are under test) in terms of computing requirements and handled data structures. In general, several specialised CPU boards are needed to satisfy real time constraints which lead to sophisticated synchronization and communication schemes. In addition to this, dedicated avionic I/O boards (Arinc 429, 1553, etc.) are required depending on the UUTs. This test methodology calls for separate teams with different domain experts in order to achieve the test of each part. The overall avionic system verification is done through the first prototype of the helicopter. Today, this test process is very complex and expensive to perform. Our paper addresses the above test methodology limitations and calls for an innovative avionic test environment. Our main objective is to build up a generic test environment by the means of offering more flexibility regarding the selection of the suitable target avionic system. An efficient test methodology favors the reuse and the interoperability of hardware and software models while switching between different scenarios.
In this paper a control system design for robotic airship is developed. The nonlinear multilinked mathematic model of airship is considered. The results of aerodynamic analysis, parametric and structure disturbances estimation, nonlinear control algorithms are presented. Airship motion simulator is developed and successfully applied. Airship is implemented on experimental robotic mini-airship.
The S-61 helicopter is enjoying new life as a result of the performance improvements that have derived from a new main rotor developed by Carson Helicopters, Inc. The success of these composite rotor blades has led to consideration of additional ways to enhance the efficiency and usefulness of this rotorcraft. This paper discusses other modifications and upgrades currently in progress that will continue to make this aircraft viable and competitive into the future. Two of efforts are highlighted in the paper, an engine upgrade and a new stabilization system/autopilot.
This paper outlines an autonomous flight system developed onboard the ONERA ReSSAC unmanned helicopter for an air-to-ground target tracking mission in an unkown urban environment. The system consists of the following three components: i) an image processor which detects the target and estimates ground motion around the detected target position, ii) an integrated vision/inertial navigation filter which simultaneously localizes the target and the own-ship UAV with an occasional loss of GPS, and iii) a guidance law which achieves target tracking as well as obstacle avoidance. Those algorithms are implemented in the Orocos robotic architecture, integrated with the auto-pilot system of the ReSSAC helicopter, and tested in closed-loop flight of vision-based target tracking.
The classical autopilot control law is extensively used for trimming rotorcraft models in comprehensive analysis codes. It consists of a simple control law that constructs a map relating the inputs and outputs of the system, based on a static approximation to its behavior. It is then easy to compute suitable filter time constants and control gains such that a closed loop controller will steer the system to its trimmed configuration with a desired performance. However, when this control law is used to steer complex rotorcraft models, such as those used in comprehensive analysis codes, stable behavior is only observed for judiciously chosen values of the controller parameters. Three major sources of error are responsible for the observed discrepancy: in the design of the controller, 1) the dynamic characteristics of the plant are ignored, 2) the non-linear behavior of the plant is not taken into account, and 3) the Jacobian of the system is assumed to be known exactly. This work focuses on the implications of these three assumptions on the behavior of the classical autopilot, by studying their effect through both numerical closed-loop experiments on a realistic UH-60 multibody rotor model (the plant), and eigenvalue analysis of the closed-loop characteristics of different reduced order models of the full plant.
Initial CH-47F Digital AFCS development and flight testing focused on advanced lowspeed modes (ACAH, TRC, PH and altitude hold) for enhanced capability and safety when hovering in poor visibility. Following successful fielding of the advanced lowspeed modes, a program immediately ensued to improve the coupled Flight Director (autopilot) performance beyond what was possible with the legacy analog AFCS. These improvements were realized with only software changes. Using a systems-engineering approach, the Boeing and Rockwell Collins design team worked with the U.S. Army customer to define and prioritize new system requirements. Solutions to all the requirements were developed using a high-fidelity integration lab, allowing a compressed program schedule of 8 months from program start to first flight while simultaneously minimizing developmental flight test time. The U.S. Army Aviation Technical Test Center conducted the developmental flight testing with on-site support from both Boeing and Rockwell Collins. The redesigned autopilot exhibited excellent performance in flight test. Workload reduction was measured quantitatively using the Subjective Workload Analysis Technique (SWAT). Coupling the Flight Director to the Digital AFCS was shown to greatly reduce cockpit workload, which not only reduces pilot fatigue on long missions but also frees pilot capacity to focus on mission objectives and evolving threats. By pairing advanced lowspeed modes with the coupled Flight Director, the CH-47F enhances capability and reduces pilot workload in all flight regimes. This paper describes the CH-47F coupled Flight Director development process, some highlights of the system design, its advanced capabilities, and presents flight test results.
A two-processor autopilot control system for an unmanned aerial vehicle (UAV) has been proposed and partly developed. Relative to prior such systems, this would be a lightweight, inexpensive autopilot system offering enhanced computational power and flexibility that would enable the use of the system in a variety of advanced UAVs. The two-processor architecture represents a significant departure from most prior single-processor UAV-autopilot architectures. Moreover, because this particular two-processor architecture is an open one, based on the use of commercial- off-the-shelf (COTS) processors and other COTS electronic subsystems, the system could easily be upgraded to take advantage of available state-of-the-art equipment.
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