Development of flight control system for 2D differential geometric guidance and control problem

This paper discusses the design and application method of flight control actuation system models based Modelica. According to the physical topology layout of aircraft flight control system, the model development of flight control system is decomposed from top to bottom, and all component parts of flight control system are developed by using development tool based on Modelica language, and the tree structure model library of flight control system is set up. Based on the established model library resources, the flying control system of specific aircraft can be integrated and encapsulated in accordance with the requirements. The model verification work is made through several levels of model verification to ensure that the function of the model has reached the development requirement. The confirmed flight control system models, which can be used to analyze the influence of flight control system on different user cases of aircraft ‘flight scenes, the models also can be used in real-time simulation of human in the loop with visual virtual scene. In the upgrading plan of an engineering simulator, the flight control system models are transformed into FMU models of FMI interface standard and loading into the engineering simulator to run successfully.

U NMANNED Aerial Vehicles (UAVs) hold good promise for autonomously carrying out complex civilian and military operations. However, many of these missions require them to fly at low altitudes, making them vulnerable to collision with both stationary as well as moving obstacles. Hence, it is vital that UAVs are equipped with autonomous capability to sense and avoid collisions, especially for the pop-up threats. When such a threat is sensed and a collision is predictedwithin a short time ahead, theUAV should be able to react and maneuver away quickly so that the collision is avoided. An algorithmwhich can assure such amaneuver is called a “reactive collision avoidance algorithm.” Since the available reaction time in such a scenario is usually is small and UAVs are usually limited by computational resources, such an algorithm should also be computationally very efficient (it should preferably be noniterative). It is also required that whilemaneuvering away, it should notmaneuver toomuch away from the obstacle either. This is both to avoid collision from other nearby obstacles and not to compromise on the overall mission objective. There are various attempts in the literature to develop algorithms for collision avoidance purpose, many of which are inspired from global path planning algorithms. The artificial potential field method is such an approach where the motion of the vehicle is guided under the influence of a potential field. The potential field (which is essentially a cost function) is designed in such a way that obstacles have repulsive fields while the destination has an attractive field. The safe path of the UAV is then found by optimizing the carefully selected cost function. To tune this basic philosophy for reactive collision avoidance, a model predictive control-based algorithm has been proposed in the literature. This algorithm essentially assures path following under safe conditions (i.e. if no collision is predicted in the near future) and invokes the potential field function when new collisions are sensed. However, in potential field based techniques the associated optimization process is typically done in an iterative manner. Because of this they are usually computationally intensive and hence are not suitable for reactive collision avoidance of airborne UAVs in general. A promising algorithm in collision avoidance and global path planning is the philosophy of rapidly-exploring random tree (RRT) [1], which has also been used for reactive collision avoidance. However, there are many concerns about the RRT approach, which can largely be attributed to the random nature of the algorithm. For example, the path predicted by RRT is usually a sting of connected straight lines that does not reflect the path followed by a vehicle with nonholonomic constraints. More important, it is a probabilistic approach and hence there is no guarantee of finding a feasible path within a limited finite time. Other graph search algorithms such as best-first search are also implemented for reactive collision avoidance [2]. However, this is not systematic approach and could result in the algorithm searching far too many nodes under some conditions. Moreover, precomputing motion primitives and saving them in a lookup table is infeasible for UAVs, which are usually resource-limited. An interesting perspective to collision avoidance problem is the minimum effort guidance [3], where an optimal control-based approach has been proposed after applying the collision cone philosophy to detect collisions. This method is computationally nonintensive as a closed form solution has been proposed. Even though this is an interesting idea, by minimizing the lateral acceleration, perhaps it imposes unwanted extra constraint on the problem formulation as reactive collision avoidance problems do not necessarily have to be carried out with minimum lateral acceleration. More important, one can observe that this formulation only assures position guarantee and no constraint is imposed on the velocity vector. Hence, even though it guides the vehicle to a carefully selected target point on the safety boundary (we call it the “aiming point”), it causes the vehicle to maneuver until this point. This can be risky as the vehicle may enter the safety ball before reaching the aiming point. Even though the collision cone based aiming point philosophy is a very good idea, the authors of this Note strongly believe that instead of only position guarantee, rather the velocity vector should be aligned towards the aiming point as soon as a collision is detected (which will automatically lead to position guarantee as well). Towards this objective, two new nonlinear guidance laws are proposed in this Note, which are named as nonlinear geometric guidance (NGG) and differential geometric guidance (DGG). These guidance laws are inspired by the philosophy of “aiming point guidance” (APG) [4], which has been proposed in missile guidance literature. It turns out that the APG is a simplified case of the NGG where the associated since function is replaced by its linear approximation (hence, for a systematic discussion, it is renamed as linear geometric guidance (LGG) in this Note). Both of the guidance algorithms proposed in this Note quickly align the velocity vector of the UAValong the aiming point within a part of the available time-togo, which ensures quick reaction and hence safety of the vehicle. The main feature of this philosophy is that they effect high maneuvering at the beginning, causing the velocity vector of the UAV to align with the aiming point direction quickly and then settling along it. Therefore there is no need to maneuver all the way until the aiming point is reached and hence the chance of the UAV entering into the safety ball is minimized. Using the point of closest approach (PCA) [5], the proposed NGG andDGGalgorithms have also been extended for collision avoidance with moving obstacles in both cooperative as well as ignorant scenarios. Mathematical correlations between the guidance laws have also been established, which show that the NGG and DGG are exactly correlated to each other with appropriate gain selections, while the LGG is an approximation of DGG. A “sphere-tracking algorithm” is also proposed in this Note where the UAV is guided to track the surface of the safety spherewhenever a brief violation of the safety boundary occurs after reaching the aiming point because of the location of the next aiming point (which may include the target in Presented as Paper 2010-8315 at the AIAA Guidance, Navigation and Control, Toronto, 2–5August 2010; received 26May 2010; revision received 4 October 2010; accepted for publication 6 October 2010. Copyright © 2010 by Radhakant Padhi. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0731-5090/11 and $10.00 in correspondence with the CCC. ∗Former Project Assistant, Department of Aerospace Engineering; anushamujumdar87@gmail.com. Associate Professor, Department of Aerospace Engineering; padhi@ aero.iisc.ernet.in. JOURNAL OF GUIDANCE, CONTROL, AND DYNAMICS Vol. 34, No. 1, January–February 2011

The wing on the NASA F-111 transonic aircraft technology airplane was modified to provide flexible leading and trailing edge flaps. This wing is known as the mission adaptive wing (MAW) because aerodynamic efficiency can be maintained at all speeds. Unlike a conventional wing, the MAW has no spoilers, external flap hinges, or fairings to break the smooth contour. The leading edge flaps and three-segment trailing edge flaps are controlled by a redundant fly-by-wire control system that features a dual digital primary system architecture providing roll and symmetric commands to the MAW control surfaces. A segregated analog backup system is provided in the event of a primary system failure. This paper discusses the design, development, testing, qualification, and flight test experience of the MAW primary and backup flight control systems.

The flight control with wind disturbance predictive model for the University of Glasgow Singapore (UGS) Lynxmotion Hunter Y-shape V-Tail 500 are presented. The V-Tail has the capability of high directional manoeuvre by the tilted (angled) hind rotors arms. However, the drawbacks of this design are that it causes instability during strong crosswind and structural instability at the rear. As such, the development of a new flight control system with wind correction is required to assist in making this platform stable. The previous phase of this work was to understand the needs and objectives of this project, source for the right configuration and constructing of the V-Tail for the investigation of its flight behaviour. The subsequent phase was to recreate a flight control firmware using Simulink with analysis and discuss the simulation results. The current phase is to integrate the wind correction model. With the new developed wind disturbance control, the model can be flown on course despite strong wind. This can be used for future research into tiltrotor initiatives or even use it to recreate a robust location correction in auto mode without the use of GPS localization.

Unmanned Aerial Systems (UAS) have great potential to be used in a wide variety of civil applications such as environmental applications, emergency situations, surveillance tasks and more. The development of Flight Control Systems (FCS) coupled with the availability of other Commercial Off-The Shelf (COTS) components is enabling the introduction of UAS into the civil market. The sophistication of existing FCS is also making these systems accessible to end users with little aeronautics expertise. However, much work remains to be done to deliver systems that can be properly integrated in standard aeronautical procedures used by manned aviation. In previous research advances have been proposed in the flight plan capabilities by offering semantically much richer constructs than those present in most current UAS autopilots. The introduced flight plan is organized as a set of stages, each one corresponding to a different flight phase. Each stage contains a structured collection of legs inspired by current practices in Area Navigation (RNAV). However, the most critical parts of any flight, the depart and approach operations in an integrated airspace remain mostly unexplored. This paper introduces an assessment of both operations for UAS operating in VFR and IFR modes. Problems and potential solutions are proposed, as well as an automating strategy that should greatly reduce pilot workload. Although the

This paper describes the development and flight testing of an autonomous flight control system that allows for different levels of pilot interaction in both hover and forward flight. Three modes of operations are described, fully coupled autonomy, additive control, and piloted decoupled attitude-command. These modes are demonstrated on the Rotorcraft Aircrew Systems Concepts Airborne Laboratory (RASCAL) JUH-60A Black Hawk integrated with Obstacle Field Navigation (OFN) and Safe Landing Area Determination (SLAD) algorithms that both use a line scanning LADAR as their primary sensor. The paper will describe the control system, its performance, and demonstrate how it was used to transition into these modes while flying mission scenarios through mountainous terrain at speeds from hover to 100 kts.

In this paper, to resolve linkage restrictions and to provide extended adaptability under various UAV application challenges, unique features oriented from the advanced IT and communication technology are tightly integrated in the flight control system and auxiliary avionics development for the rotary type UAV autopilot system. First, this work considers a commercial cellular network and IP backbone networks for achieving the data linkage between FCS and GCS. Second, we suggest a central application entity for managing flight control data and various mission data between network terminal nodes. Additionally, this work includes a user-friendly interface for the UAV command and control, as well as simple graphical demonstration of the flight and mission information. The proposed system is applied to the target vehicle of a small sized, electrically powered, rotary UAV system.

T HE development of aircraft guidance, navigation, and control systems has been a long-standing research area. Numerous methods relating to the enhancement of aircraft performance under various mission parameters have been developed in response to a need for more reliable and robust guidance systems. Current guidance systems applied to commercial, civilian, and unmanned aircraft rely on the knowledge of a flight path, specified bywaypoints located in inertial space. Most missions are considered successful when the vehicle reaches the designatedwaypoint at which new commands are issued to the vehicle to proceed to the next waypoint. Two common types of conventional aircraft guidance are the direct-to-waypoint (DTW) and track-to-waypoint (TTW) methods in relation to pathfollowing between designated waypoints. The DTWmethod simply issues heading commands to the vehicle based on the angular difference between thewaypoint and vehicle.When thevehicle reaches the waypoint, the control system issues a new command to guide the aircraft to the next waypoint. The TTW method aims to follow the track betweenwaypoints. In this guidancemethod the control system aims to minimize the lateral offset between the prescribed flight path and the aircraft’s position, issuing heading commands that return the vehicle to the nominal flight path. The track method therefore places the additional constraint on a flight path that the vehicle must follow in order to reach the waypoint, rather than simply reaching the waypoint. However, both methods are far from optimal. This is evident in how the aircraft transitions between flight paths after reaching awaypoint. During theseflight-path transitions, the aircraft will often overshoot the desiredflight path to correct its track, particularlywhen the flight-path transition angle is acute. Various control strategies have been investigated to alleviate or minimize flight-path deviations. Such strategies include applyingmodern control methods such as receding-horizon control [1,2] and model predictive control [3] to anticipate flight-path changes and take control action before reaching a goal while maintaining adequate vehicle flight performance. Missile guidance and control systems operate on similar principles to commercial, civilian, and unmanned aircraft guidance and control algorithms. The primary mission for missile systems is to intercept a moving target using information about the relative position and velocity between the pursuer and target. One of thefirstmethods used in missile guidance was pursuit guidance (PG) [4–7]. The method operates by forcing the angular displacement error between a pursuer and its target to zero. Control commands scaled by a proportional factor of the current error are then issued to direct the pursuer along the line of sight (LOS) between the pursuer and target. PG solutions, however, do not consider the path taken or the levels of system performance required by the pursuer in reaching the target, resulting in a far-from-optimal solution. To address this problem of suboptimality, additional parameters have been introduced to enhance missile performance. One method includes taking into account the motion of the commanded line of sight between the pursuer and target [4,7–10], issuing lateral acceleration commands based on tracking error and tracking error rate to the target. This approach has been shown to improve overall interceptor performance compared with conventional PG [4,7]. Another suchmethod aims tomodify the level of control the guidance algorithm possesses over the vehicle by adjusting the level of proportional gain. This is achieved by gain scheduling [11] to select gain values based on current interceptor states. In addition to modifying internal missile guidance and control parameters such as variable gains and LOS rate estimation, mission performance can be enhanced by manipulating the trajectory taken by the pursuer to the targets. A good example of such a method is discussed in [12], in which a missile aims to exploit the aerodynamic benefits of high-altitude flight by tracking a virtual target at some initially high altitude that is not necessarily along the trajectory to the true target. This Note discusses the development of a guidance law fusing the virtual-target concepts with those of pursuit guidance for implementation into an aircraft guidance system. This Note develops a path-following aircraft guidance algorithm that pursues synthetic waypoints using only a small set of guidance parameters, extending the virtual-target concept to complete aircraft guidance. The path is defined by the track between a minimal set of waypoints at specified locations, removing the need for a smooth path to be defined or the need for complicated path-switching logic or trajectory planning when awaypoint is reached. The synthetic waypoint travels along the path between waypoints, with the trailing aircraft traveling a smooth path generated through its own dynamics in following the synthetic waypoint. The guidance law is tested by varying guidance parameters, thus assessing vehicle sensitivity to and overall system performance of parameter variations. The following sections discuss the basic concepts of missile and aircraft guidance and provide a detailed description of the structure of the synthetic-waypoint guidance algorithm. A discussion on the implementation of the algorithm into the underlying aircraft control system will also be presented, followed by an analysis of the performance of the guidance algorithm in nonlinear simulation. Received 30 June 2009; revision received 3 November 2009; accepted for publication 9November 2009.Copyright©2009 by theAmerican Institute of Aeronautics and Astronautics, Inc. All rights reserved. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0731-5090/10 and $10.00 in correspondence with the CCC. ∗Graduate Research Student, School of Aerospace, Mechanical and Mechatronic Engineering; e.medagoda@aeromech.usyd.edu.au. Senior Lecturer, School of Aerospace, Mechanical and Mechatronic Engineering; pwg@aeromech.usyd.edu.au. JOURNAL OF GUIDANCE, CONTROL, AND DYNAMICS Vol. 33, No. 2, March–April 2010

This paper describes the development of a Sense and Avoid (S&A) algorithm that can ensure an Unmanned Aerial Vehicle (UAV) being able to avoid collisions autonomously. Firstly, the Closest Point of Approach (CPA) can predict the conflict. Secondly, the developed path planner generates the feasible path using differential geometric guidance and flatness techniques, where the constraints of UAV dynamics are considered. The effectiveness of the developed scheme is demonstrated by flight tests carried out at the Marinvent© Flight Simulator.

This paper presents an integrated effort by the different universities in the state of Kansas on the development of an open source modular wireless avionics system for both fixed and rotary-wing aircraft. Development of hardware and software to enhance the airborne processing performance at a reduced price is discussed. Also discussed are a flight management system implemented on a fixed wing UAV and the software development and modeling of INS/GPS. Methods to incorporate the flight management system to the modular avionics system are presented. The paper also highlights the development of the dynamic model for UAV helicopters. The dynamic model will be used for the development of an automatic flight control system that will be integrated with the modular avionics systems. Various aspects of communication related problems are also discussed.

The increasing usage of Unmanned Aerial Vehicles (UAVs) in diverse applications necessitates the development of effective flight control systems. A major difficulty, nevertheless, is maintaining robust flight control under complex and dynamic environmental conditions, such as obstacle and non-flying zones, wind disturbances, and sensor noise. Traditional control techniques fail to achieve required flight control in these environments. To address this point, an adaptive control strategy is proposed based on a Deep Reinforcement Learning (DRL) model to enhance the flight performance of quadcopters. The learning and adaptation of the DRL-based control strategy are implemented in real-time through continuous interaction with the environment. This is to improve flight control and achieve consistent UAV performance under varying conditions. Proximal policy optimization with a reward function is used to minimize positional errors, ensure collision-free flight paths, and reduce energy consumption. The developed DRL model for quadcopters is trained in a simulated environment and then tested in three complex environmental scenarios, including urban, forest and mountain terrains. Experimental results demonstrate remarkable improvements in UAV flight performance. In the training phase, the reported training reward increased from 10 to 110 and the train loss is dropped from 0.85 to 0.05, which indicated successful model learning. Also, during system verification, rewards increased from 12 to 115 and UAV flight path deviations were decreased from 0.5 to 0.08m. The proposed controller outperforms conventional approaches in urban environments by lowering average trajectory deviations to 0.2m from 0.35m for MPC and 0.6m for PID. Also, the developed DRL-based controller outperformed the PID and MPC controllers, with path deviations of 0.18m in mountains and 0.12m in forests. In addition, fewer collision rates with obstacles are achieved with the model, 3% in forest, 1.8% in urban areas, and 4.5% in mountains. Furthermore, the consumed energy is reduced to 950J as compared to 1200J for PID and 1050J for MPC. The results show the strength of deploying the proposed controller in following the intended UAV flight path with high precision, effectively avoiding detected conflicts and minimizing consumed energy by the UAV.

The development of hypersonic flight control system is a challenging task, because of the tightly coupled, highly nonlinear and notoriously uncertain nature of hypersonic vehicle(HSV) dynamics. During the hypersonic flight, any state which does not meet the constraint condition may lead to the system instability. In this paper, differential flatness method is applied to the linearization of the model of HSV. Since all the time-varying states and control inputs can be obtained after the trajectory planning, the desired state can be protected from exceeding the limit before flight simulation. After the linearization, a state feedback controller is designed and discrete linear tracking differentiator(TD) is used to extract high order differential signal of the system, which can avoid the high order differential signal polluted in the numerical calculation. A case study is presented to illustrate the capability of the proposed methodology.

This paper presents the conceptual development of advanced flight control systems (flight management systems), where the objective is to attain real-time identification, design, and controller reconfiguration. The identification and control methods developed use a complete nonlinear aircraft model derived from the Lagrange equations of motion. The generalized performance functional is employed. Using the Hamilton-Jacobi theory, bounded controllers are designed applying the first- and second-order necessary criteria for optimality. The sufficient conditions are studied to analyze stability of the closed-loop systems with bounded control. In particular, the admissibility concept, which is based on the Lyapunov stability theory, is used. It is shown that innovative procedures guarantee real-time identification, constrained optimization, and controller reconfiguration. The concept reported can be applied to advanced highly maneuverable aircraft, as well as to agile missiles and interceptors.

The control algorithm includes: attitude control, position control, flight velocity control and flight trajectory control. The basic flight control algorithm is composed of two parts: orientation controller based on the model for rotation dynamics and a robust position controller based on the model for a double integrator. Then, the mathematical model of a small size helicopter is described at first. After that we present simple but effective MTC (Model and Trajectory based Controller, named by the author for convenience.) control algorithm. It is used to achieve translation position and velocity control and no stopping multi-way-point flight trajectory control. Simulation results and results achieved in real flight experiments demonstrated that the presented controller is effective and practical.

This chapter introduces a number of the issues which must be addressed during the design and development of an automatic flight control system (AFCS). It does not make a comparison of potential system architectures nor does it suggest an optimum approach because each AFCS is different, depending upon the airframe and application. However, there is a common theme associated with an AFCS development programme that can be introduced and used to familiarise the reader with some of the concepts which must be addressed during the design evolution.