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The advent of GPS has afforded the aerospace controls engineer a powerful, new means of controlling air vehicles This work explores a new method of designing and implementing controllers and guidance systems for autonomous control of air vehicles utilizing a GPS integrated guidance, navigation and control system. This is a subject of considerable interest when realizing controllers to track reference trajectories given in an inertial reference frame. The design, implementation, and dynamic simulation of a precise tracking trajectory controller for an Unmanned Air Vehicle (UAV) is presented. This design provides a natural conversion of commands and other measured outputs (such as GPS signals) from an inertial reference frame to a body-fixed reference frame. This achieves automatic recruiting of the actuators while preserving the properties of the original design (linearization principle). Trajectory control, GNC, Linear Quadratic regulator, LQR, Kalman filter, Nonlinear simulation, D- Implementation, Bluebird, http://archive.org/details/designofgpsidedg1094526457, NA, U.S. Navy (U.S.N.) author.

Two theoretical methods and the development of a guidance, navigation and control rapid protoyping system address the issue of considering the integral participation of feedback early in the design process. The first method addresses the problem of sizing the horizontal tail on a statically unstable transport aircraft. Dynamic constraints including recovery from a severe angle of attack excursion and penetration of a vertical wind shear are formulated in terms of the solution to a convex minimization problem utilizing LMIs and used to size the horizontal control surfaces. The second method addresses the problem of tracking inertial trajectories with applications for unmanned air vehicles. This problem is posed and solved within the framework of gain scheduled control theory leading to a new technique for integrated guidance and control systems with guaranteed performance and robustness properties. Finally, a rapid prototyping system for the flight test of GNC algorithms for unmanned air vehicles is designed that affords a small team the ability to quickly take a new concept in guidance, navigation and control from initial conception to flight test, http://archive.org/details/onintegratedplan00hall, Lieutenant Commander, United States Navy, Approved for public release; distribution is unlimited.

The Air Force uses the TGP program as its primary tool for predicting store trajectories on the basis of wind tunnel grid data. The US Navy uses NAVSEP, which is based on an earlier version of the TGP code. Both codes are based on the AEDC Multi-Dimensional Interpolation Trajectory Generation Program, and have undergone numerous improvement/modifications during the past 15 years. The U.S. Navy and Air Force have also recently acquired Store Trajectory Estimation in a MATLAB Environment (STEME) code from the Australian Air Force. NAVAIR is in the process of preparing a flight clearance for the High Speed Antiradiation Demonstrator (HSAD) program. This clearance will be prepared using the conventional tool NAVSEP. The HSAD missile, however, utilizes an autopilot to move four movable control fins shortly after release as it rapidly accelerates in flight speed. It would be highly desirable to fully account for these elements in the release simulation using either NAVSEP, TGP, or STEME. NAVSEP, TGP have amassed a long lineage of wind tunnel data, flight simulation predictions, and flight test data with which to perform code validation and verification. Therefore, new developments should preserve as much backward compatibility as possible with existing TGP codes while improving and developing user interfaces and increasing functionality. In conjunction with the conventional NAVSEP approach to clear the HSAD missile, it would be desirable to test the TGP and STEME codes for the same case. It would be desirable to develop the general methodology to integrate the current 6 DOF analysis approaches with the proposed MATLAB/Simulink auto-pilot environment. The Navy is currently involved in developing the joint-service Joint Common Missile with Lockheed. Both FLIP TGP and NAVSEP codes coupled with MATLAB/Simulink will be tested for this particular missile. Recently, a Beggar approach with hard-coded autopilot has been practiced.

The past two decades have witnessed a dramatic increase in -8the use of Unmanned Air Vehicles (UAVs) by the armed forces, both in the US and abroad. More recently, many re- searchers in the academic community have realized the useful- ness of UAVs both as teaching and research tools. To develop UAVs and their flight control systems, a number of engineering problemsmustbeaddressedcoveringawiderangeofissuesthat include weight and energy restrictions, portability,risk factors, electronic interferences, vibrations and manpower. Further- more, the testing of new algorithms, sensor packages, and vehi- cles is a truly multidisciplinary effort that borrows from many branches of the engineering sciences that include aeronautic, electrical, and computer engineering. The process is costly and time consuming, and has the potential for catastrophic failure.

The use of an automatic flight augmentation system is commonplace on a modern aircraft. Its benefits include alleviation of undesirable flight characteristics, reduction of pilot workload, and increase in performance and fuel efficiency. Therefore, feedback (dynamic) considerations should be included in determining the sizes of aircraft control surfaces. Traditionally, only static con- straints have been used for control surface sizing. For example, in the case of a horizontal tail of a given volume, constraints are cal- culated that limit the fore and aft travel of the c.g. Constraints that limit the forward c.g. position include 1) sufficient nose-up pitch acceleration at the rotation speed (nose-wheel lift off) and 2) suf - cient nose-up pitch acceleration at the approach speed in the landing con guration (go-around). Constraints that limit aft c.g. position include 1) at brake release with maximum thrust suf cient weight on the nose gear (tip back), 2) pitch-up acceleration at the rota- tion speed (nose-wheel lift off), and 3) suf cient nose-down pitch accelerationatminimum yingspeeds.1 However,fortheaftc.g. locations at the approach ight condition of a supersonic transport aircraft, dynamic constraints may be more restrictive than the static ones.

A new methodology is introduced for integrated design of guidance and control systems for autonomous vehicles (AVs). The methodology proposed borrows from the theory of gain-scheduled control and leads to an efficient procedure for the design of controllers for AVs to accurately track reference trajectories defined in an inertial reference frame. The paper illustrates the application of this procedure to the design of a tracking controller for the unmanned air vehicle Bluebird. The design phase is summarized, and the performance of the resulting controller is assessed in simulation using dynamic models of the vehicle and its sensor suite.

Two theoretical methods and the development of a guidance, navigation and control rapid protoyping system address the issue of considering the integral participation of feedback early in the design process. The first method addresses the problem of sizing the horizontal tail on a statically unstable transport aircraft. Dynamic constraints including recovery from a severe angle of attack excursion and penetration of a vertical wind shear are formulated in terms of the solution to a convex minimization problem utilizing LMIs and used to size the horizontal control surfaces. The second method addresses the problem of tracking inertial trajectories with applications for unmanned air vehicles. This problem is posed and solved within the framework of gain scheduled control theory leading to a new technique for integrated guidance and control systems with guaranteed performance and robustness properties. Finally, a rapid prototyping system for the flight test of GNC algorithms for unmanned air vehicles is designed that affords a small team the ability to quickly take a new concept in guidance, navigation and control from initial conception to flight test.

Two theoretical methods and the development of a guidance, navigation and control rapid protoyping system address the issue of considering the integral participation of feedback early in the design process. The first method addresses the problem of sizing the horizontal tail on a statically unstable transport aircraft. Dynamic constraints including recovery from a severe angle of attack excursion and penetration of a vertical wind shear are formulated in terms of the solution to a convex minimization problem utilizing LMIs and used to size the horizontal control surfaces. The second method addresses the problem of tracking inertial trajectories with applications for unmanned air vehicles. This problem is posed and solved within the framework of gain scheduled control theory leading to a new technique for integrated guidance and control systems with guaranteed performance and robustness properties. Finally, a rapid prototyping system for the flight test of GNC algorithms for unmanned air vehicles is designed that affords a small team the ability to quickly take a new concept in guidance, navigation and control from initial conception to flight test

The advent of GPS has afforded the aerospace controls engineer a powerful, new means of controlling air vehicles This work explores a new method of designing and implementing controllers and guidance systems for autonomous control of air vehicles utilizing a GPS integrated guidance, navigation and control system. This is a subject of considerable interest when realizing controllers to track reference trajectories given in an inertial reference frame. The design, implementation, and dynamic simulation of a precise tracking trajectory controller for an Unmanned Air Vehicle (UAV) is presented. This design provides a natural conversion of commands and other measured outputs (such as GPS signals) from an inertial reference frame to a body-fixed reference frame. This achieves automatic recruiting of the actuators while preserving the properties of the original design (linearization principle). Trajectory control, GNC, Linear Quadratic regulator, LQR, Kalman filter, Nonlinear simulation, D- Implementation, Bluebird