A near-optimal feedback guidance algorithm is applied to several different applications in the Circular-Restricted Three Body Problem and in proximity operations in LEO modeled by Keplerian motion. In both scenarios gravitational perturbations are introduced in order to assess the algorithm's robustness. Two forms of the guidance algorithm are studied: a zero-effort miss/zero-effort velocity feedback control law and a zero-effort miss/zero-effort velocity feedback control law augmented with a sliding mode. Both guidance laws have previously been applied to the problems of planetary landing, asteroid intercept, and close-proximity maneuvers near an asteroid. This study is motivated by the growing interest in spacecraft autonomy for proximity operations and in cases where a high frequency of open-loop commanded maneuvers is not practical. Results demonstrate that nominal zero-effort miss/zero-effort velocity feedback guidance is suboptimal in all test cases, but performance can be improved through the addition of waypoints and tuning of guidance law parameters. Additionally, the application of a sliding-mode is shown to overcome limitations introduced by gravitational perturbations in some instances.

This dissertation investigates advanced concepts in terminal missile guidance. The terminal phase of missile guidance usually lasts less than ten seconds and calls for very accurate maneuvering to ensure intercept. Technological advancements have produced increasingly sophisticated threats that greatly reduce the effectiveness of traditional approaches to missile guidance. Because of this, terminal missile guidance is, and will remain, an important and active area of research. The complexity of the problem and the desire for an optimal solution has resulted in researchers focusing on simplistic, usually linear, models. The fruit of these endeavors has resulted in some of the world's most advanced weapons systems. Even so, the resulting guidance schemes cannot possibly counter the evolving threats that will push the system outside the linear envelope for which they were designed. The research done in this dissertation greatly extends previous research in the area of optimal missile guidance. Herein it is shown that optimal missile guidance is fundamentally a pairing of an optimal guidance strategy and an optimal control strategy. The optimal guidance strategy is determined from a missile's information constraints, which are themselves largely determined from the missile's sensors. The optimal control strategy is determined by the missile's control constraints, and works to achieve a specified guidance strategy. This dichotomy of missile guidance is demonstrated by showing that missiles having different control constraints utilize the same guidance strategy so long as the information constraints are the same. This concept has hitherto been unrecognized because of the difficulty in developing an optimal control for the nonlinear set of equations that result from control constraints. Having overcome this difficulty by indirect means, evidence of the guidance strategy paradigm emerged. The guidance strategy paradigm is used to develop two advanced guidance laws. The new guidance laws are compared qualitatively and quantitatively with existing guidance laws.

No / This paper presents an investigation into the challenges in implementing a hard real-time optimal non-stationary system using general regression neural network (GRNN). This includes investigation into the dynamics of the problem domain, discretisation of the problem domain to reduce the computational complexity, parameters selection of the optimization algorithm, convergence guarantee for real-time solution and off-line optimization for real-time solution. In order to demonstrate these challenges, this investigation considers a real-time optimal missile guidance algorithm using GRNN to achieve an accurate interception of the maneuvering targets in three-dimension. Evolutionary Genetic Algorithms (GAs) are used to generate optimal guidance training data set for a large missile defense space to train the GRNN. The Navigation, Constant of the Proportional Navigation Guidance and the target position at launching are considered for optimization using GAs. This is achieved by minimizing the. miss distance and missile flight time. Finally, the merits of the proposed schemes for real-time accurate interception are presented and discussed through a set of experiments. (C) 2012 Elsevier Ltd. All rights reserved.

Missile guidance and autopilot have been active fields of research since the second world war. There are lots of literature on the subjects, but the bulk of which are confined to overly simplified models, and therefore the publications of the methods applied to more realistic models are scarce. In this report a nonlinear 6 DOF simulation model of a tail-controlled air-to-air missile is considered. Through several assumptions and simplifications a linearized approximation of the plant is obtained, which then is used in the implementation of 5 guidance laws and 2 autopilots. The guidance laws are all based on a linearized collision geometry, and the autopilots are based on model predictive control (MPC). Both autopilots use linear quadratic MPC (LQMPC), and one is more robust to modelling errors than the conventional LQMPC. The guidance laws and autopilots are then evaluated with respect to performance in terms of miss distance in 4 interception scenarios with a moving target. The results show that the in this model the autopilots perform equally well, and that the guidance laws with more information about the target generally exhibit smaller miss distances, but at the cost of a considerably larger flight time for some scenarios. The conclusions are that the simplifying assumptions in the modelling are legitimate and that the challenges of missile control probably does not lie in the guidance or autopilot, but rather in the target tracking. Therefore it is suggested that future work include measurement noise and process disturbances in the model. / Det har forskats kring styrlagarna och styrautomaterna för robotar sedan an-dra världskrigets. Det finns mycket litteratur på områdena, men merparten av de publicerade resultaten behandlar enbart grovt förenklade modeller, och därför är tillgången på publikationer där metoderna applicerats i en mer realistisk modell begränsat. I denna rapport behandlas en olinjär simuleringsmodell av en jaktrobot som styrs med stjärtfenor och har sex frihetsgrader. Genom en rad antaganden och förenklingar erhålls en linjäriserad modell av missilen, vilket sedan används för implementering av fem styrlagar och två styrautomater. Styr-lagarna är alla baserade på en linjäriserad kollisionsgeometri och styrautomaterna är baserade på modellprediktiv styrning (MPC). Båda styrautomaterna använder linjärkvadratisk MPC, där den ena påstås vara mer robust gentemot modellfel. Styrlagarna och -automaterna utvärderas ur ett prestandaperspektiv med fokus på bomavstånd i fyra realistiska genskjutningsscenarier med ett rörligt mål. Resultaten visar att båda styrautomaterna presterar lika bra, och att de styrlagar med mer information om målets position/hastighet/acceleration generellt presterar bättre, men att de för vissa skjutfall får en väsentligt längre flygtid. Slutsatserna är att förenklingarna och antagandena i linjäriseringen är välgrundade, och att utmaningarna i missilstyrning inte ligger i utformning av styrlag/-automat, utan förmodligen i målsökningen. Därför föreslås det slutligen att framtida arbete bl. a. inkluderar mätbrus och störningar.

The objective of this thesis is to design a relative navigation and guidance system for unmanned aerial vehicles (UAVs) for vision-based control applications. The vision-based navigation, guidance and control has been one of the most focused on research topics for the automation of UAVs. This is because in nature, birds and insects use vision as the exclusive sensor for object detection and navigation. In particular, this thesis studies the monocular vision-based navigation and guidance. Since 2-D vision-based measurements are nonlinear with respect to the 3-D relative states, an extended Kalman filter (EKF) is applied in the navigation system design. The EKF-based navigation system is integrated with a real-time image processing algorithm and is tested in simulations and flight tests. The first closed-loop vision-based formation flight has been achieved. In addition, vision-based 3-D terrain recovery was performed in simulations. A vision-based obstacle avoidance problem is specially addressed in this thesis. A navigation and guidance system is designed for a UAV to achieve a mission of waypoint tracking while avoiding unforeseen stationary obstacles by using vision information. A 3-D collision criterion is established by using a collision-cone approach. A minimum-effort guidance (MEG) law is applied for a guidance design, and it is shown that the control effort can be reduced by using the MEG-based guidance instead of a conventional guidance law. The system is evaluated in a 6 DoF flight simulation and also in a flight test. For monocular vision-based control problems, vision-based estimation performance highly depends on the relative motion of the vehicle with respect to the target. Therefore, this thesis aims to derive an optimal guidance law to achieve a given mission under the condition of using the EKF-based relative navigation. Stochastic optimization is formulated to minimize the expected cost including the guidance error and the control effort. A suboptimal guidance law is derived based on an idea of the one-step-ahead (OSA) optimization. Simulation results show that the suggested guidance law significantly improves the guidance performance. Furthermore, the OSA optimization is generalized as the n-step-ahead optimization for an arbitrary number of n, and their optimality and computational cost are investigated.

Various unmanned missions deploy vehicles such as missiles, torpedoes, ground robots, and unmanned aerial vehicles. Guidance strategies for these vehicles aim to intercept a target point and satisfy additional objectives such as specifications on impact angle and interception time. Certain impact angles are crucial for a greater warhead effectiveness, and minimizing the interception time is important for vehicles with limited endurance time and for reducing the probability of detection. This thesis considers the time-optimal impact angle constrained guidance problem for interception of moving targets. In the first part of the thesis, a Dubins paths–based guidance methodology for minimum-time lateral interception of a moving and non-maneuvering target is designed. The existence and the time-optimality of the paths are established for impact angle constrained interception of moving targets. The capture regions are analyzed and a classification of the initial geometries is developed for deducing the time-optimal path type. The corresponding guidance command for optimal interception can be generated from the information of initial engagement geometry and target’s speed. In the next part of the thesis, the concept of equivalent virtual target is introduced to address the problem of impact along a general direction. An algorithm is developed to obtain the optimal interception point for generalized interception scenarios. A proof of convergence is presented for the proposed algorithm. Achieving different impact angles, the interceptor often takes sharp turns. Following such curved trajectories, the interceptor may fail to keep the target inside the seeker field-of-view. In the next part of the thesis, the field-of-view characteristics of the proposed optimal guidance strategies are analyzed. Closed-form expressions are derived for the interceptor’s look-angle to the target. Satisfying field-of-view condition at endpoints of the path segments that constitute the optimal path is proven to guarantee target motion inside the field-of-view throughout the engagement. The stationary target case is also analyzed as a specific scenario. The last part of the thesis presents a method to extend the proposed guidance strategies to maneuvering target scenarios.