Computational optimal control of the terminal bunt manoeuvre

This work focuses on a study of missile guidance in the form of trajectory shaping of a generic cruise missile attacking a fixed target which must be struck from above. The problem is reinterpreted using optimal control theory resulting in two formulations: I) minimum time-integrated altitude and 2) minimum flight time. Each formulation entails nonlinear, two-dimensional missile flight dynamics, boundary conditions and path constraints. Since the thus obtained optimal control problems do not admit analytical solutions, a recourse to computational optimal control is made. The focus here is on informed use of the tools of computational optimal control, rather than their development. Each of the formulations is solved using a three-stage approach. In stage I, the problem is discretised, effectively transforming it into a nonlinear programming problem, and hence suitable for approximate solution with the FORTRAN packages DIRCOL and NUDOCCCS. The results of this direct approach are used to discern the structure of the optimal solution, i.e. type of constraints active, time of their activation, switching and jump points. This qualitative analysis, employing the results of stage I and optimal control theory, constitutes stage 2. Finally, in stage 3, the insight of stage 2 are made precise by rigorous mathemati cal formulation of the relevant two-point boundary value problems (TPBVPs), using the appropriate theorems of optimal control theory. The TPBVPs obtained from this indirect approach are then solved using the FORTRAN package BNDSCO and the results compared with the appropriate solutions of stage I. For each formulation (minimum altitude and minimum time) the influence of boundary conditions on the structure of the optimal solution and the performance index is investigated. The results are then interpreted from the operational and computational perspectives. Software implementation employing DIRCOL, NUDOCCCS and BNDSCO, which produced the results, is described and documented. Finally, some conclusions are drawn and recommendations made

Multi-objective trajectory optimization of Space Maneuver Vehicle using adaptive differential evolution and modified game theory

Highly constrained trajectory optimization for Space Manoeuvre Vehicles (SMV) is a challenging problem. In practice, this problem becomes more difficult when multiple mission requirements are taken into account. Because of the nonlinearity in the dynamic model and even the objectives, it is usually hard for designers to generate a compromised trajectory without violating strict path and box constraints. In this paper, a new multi-objective SMV optimal control model is formulated and parameterized using combined shooting-collocation technique. A modified game theory approach, coupled with an adaptive differential evolution algorithm, is designed in order to generate the pareto front of the multi-objective trajectory optimization problem. In addition, to improve the quality of obtained solutions, a control logic is embedded in the framework of the proposed approach. Several existing multi-objective evolutionary algorithms are studied and compared with the proposed method. Simulation results indicate that without driving the solution out of the feasible region, the proposed method can perform better in terms of convergence ability and convergence speed than its counterparts. Moreover, the quality of the pareto set generated using the proposed method is higher than other multi-objective evolutionary algorithms, which means the newly proposed algorithm is more attractive for solving multi-criteria SMV trajectory planning problem

Parameter Optimization of Genetic Algorithm Utilizing Taguchi Design for Gliding Trajectory Optimization of Missile

The present study aims to establish a genetic algorithm (GA) method to optimize gliding trajectory of a missile. The trajectory is optimized by discretizing the angle of attack (AOA) and solving optimal control problem to achieve maximum gliding range. GA is employed to resolve the optimal control problem to achieve optimized AOA. A Taguchi’s design of experiments was proposed contrary to full factorial method to ascertain the GA parameters. The experiments have been designed as per Taguchi’s design of experiments using L27 orthogonal array. Systematic reasoning ability of Taguchi method is exploited to obtain better selection, crossover and mutation operations and consequently, enhance the performance of GA for gliding trajectory optimization. The effects of GA parameters on gliding trajectory optimization are studied and analysis of variance (ANOVA) is carried out to evaluate significance factors on the results. Crossover function and population size are observed as highly impacting parameter in missile trajectory optimization accompanied by selection method, crossover fraction, mutation rate and number of generations. Artificial neural network (ANN) method was also applied to predict the significance of GA parameters. The results show that the gliding range is maximized after GA parameter tuning. Simulation results also portrayed that with optimal AOA, gliding distance of missile is improved compared to earlier one. The numerical simulation shows the efficiency of proposed procedure via various test scenarios

An on-board near-optimal climb-dash energy management

On-board real time flight control is studied in order to develop algorithms which are simple enough to be used in practice, for a variety of missions involving three dimensional flight. The intercept mission in symmetric flight is emphasized. Extensive computation is required on the ground prior to the mission but the ensuing on-board exploitation is extremely simple. The scheme takes advantage of the boundary layer structure common in singular perturbations, arising with the multiple time scales appropriate to aircraft dynamics. Energy modelling of aircraft is used as the starting point for the analysis. In the symmetric case, a nominal path is generated which fairs into the dash or cruise state. Feedback coefficients are found as functions of the remaining energy to go (dash energy less current energy) along the nominal path

Optimal pilot decisions and flight trajectories in air combat

The thesis concerns the analysis and synthesis of pilot decision-making and the design of optimal flight trajectories. In the synthesis framework, the methodology of influence diagrams is applied for modeling and simulating the maneuvering decision process of the pilot in one-on-one air combat. The influence diagram representations describing the maneuvering decision in a one sided optimization setting and in a game setting are constructed. The synthesis of team decision-making in a multiplayer air combat is tackled by formulating a decision theoretical information prioritization approach based on a value function and interval analysis. It gives the team optimal sequence of tactical data that is transmitted between cooperating air units for improving the situation awareness of the friendly pilots in the best possible way. In the optimal trajectory planning framework, an approach towards the interactive automated solution of deterministic aircraft trajectory optimization problems is presented. It offers design principles for a trajectory optimization software that can be operated automatically by a nonexpert user. In addition, the representation of preferences and uncertainties in trajectory optimization is considered by developing a multistage influence diagram that describes a series of the maneuvering decisions in a one-on-one air combat setting. This influence diagram representation as well as the synthesis elaborations provide seminal ways to treat uncertainties in air combat modeling. The work on influence diagrams can also be seen as the extension of the methodology to dynamically evolving decision situations involving possibly multiple actors with conflicting objectives. From the practical point of view, all the synthesis models can be utilized in decision-making systems of air combat simulators. The information prioritization approach can also be implemented in an onboard data link system.reviewe

Large Scale Constrained Trajectory Optimization Using Indirect Methods

State-of-the-art direct and indirect methods face significant challenges when solving large scale constrained trajectory optimization problems. Two main challenges when using indirect methods to solve such problems are difficulties in handling path inequality constraints, and the exponential increase in computation time as the number of states and constraints in problem increases. The latter challenge affects both direct and indirect methods. A methodology called the Integrated Control Regularization Method (ICRM) is developed for incorporating path constraints into optimal control problems when using indirect methods. ICRM removes the need for multiple constrained and unconstrained arcs and converts constrained optimal control problems into two-point boundary value problems. Furthermore, it also addresses the issue of transcendental control law equations by re-formulating the problem so that it can be solved by existing numerical solvers for two-point boundary value problems (TPBVP). The capabilities of ICRM are demonstrated by using it to solve some representative constrained trajectory optimization problems as well as a five vehicle problem with path constraints. Regularizing path constraints using ICRM represents a first step towards obtaining high quality solutions for highly constrained trajectory optimization problems which would generally be considered practically impossible to solve using indirect or direct methods. The Quasilinear Chebyshev Picard Iteration (QCPI) method builds on prior work and uses Chebyshev Polynomial series and the Picard Iteration combined with the Modified Quasi-linearization Algorithm. The method is developed specifically to utilize parallel computational resources for solving large TPBVPs. The capabilities of the numerical method are validated by solving some representative nonlinear optimal control problems. The performance of QCPI is benchmarked against single shooting and parallel shooting methods using a multi-vehicle optimal control problem. The results demonstrate that QCPI is capable of leveraging parallel computing architectures and can greatly benefit from implementation on highly parallel architectures such as GPUs. The capabilities of ICRM and QCPI are explored further using a five-vehicle constrained optimal control problem. The scenario models a co-operative, simultaneous engagement of two targets by five vehicles. The problem involves 3DOF dynamic models, control constraints for each vehicle and a no-fly zone path constraint. Trade studies are conducted by varying different parameters in the problem to demonstrate smooth transition between constrained and unconstrained arcs. Such transitions would be highly impractical to study using existing indirect methods. The study serves as a demonstration of the capabilities of ICRM and QCPI for solving large-scale trajectory optimization methods. An open source, indirect trajectory optimization framework is developed with the goal of being a viable contender to state-of-the-art direct solvers such as GPOPS and DIDO. The framework, named beluga, leverages ICRM and QCPI along with traditional indirect optimal control theory. In its current form, as illustrated by the various examples in this dissertation, it has made significant advances in automating the use of indirect methods for trajectory optimization. Following on the path of popular and widely used scientific software projects such as SciPy [1] and Numpy [2], beluga is released under the permissive MIT license [3]. Being an open source project allows the community to contribute freely to the framework, further expanding its capabilities and allow faster integration of new advances to the state-of-the-art

Simulation and Application of GPOPS for Trajectory Optimization and Mission Planning Tool

Rapid trajectory generation is crucial to prompt global warfare. To meet the USAF’s objective of Persistent and Responsive Precision Engagement, a rapid mission planning tool is required. This research creates the framework for the mission planning tool and provides a sample optimal trajectory which is solved using the GPOPS software package. GPOPS employs a Gaussian pseudospectral method to solve the non-linear equations of motion with both end conditions and path constraints. By simultaneously solving the entire trajectory based on an initial guess and small number of nodes, this method is ideal for generating rapid solutions. The sample case is a multi-phase minimum time, optimal control problem which is used to validate the planning tool. The developed framework includes different atmospheric models, gravity models, inclusion of no-flyzones and waypoints, and the ability to create a library of sample cases. This versatile tool can be used for either trajectory generation or mission analysis. The results of this research show the complexities in solving an optimal control problem with states that change from one phase of the problem to another. At the conclusions of this research multiple phases were successfully connected and solved as a single optimal control problem. However, the entire trajectory solution from launch to impact solved simultaneously, is still an objective yet to be demonstrated. The results found should be a solid foundation for a future mission planning tool

Trajectory optimization using indirect methods and parametric scramjet cycle analysis

This study investigates the solution of time sensitive regional strike trajectories for hypersonic missiles. This minimum time trajectory is suspected to be best performed by scramjet powered hypersonic missiles which creates strong coupled interaction between the flight dynamics and the performance of the engine. Comprehensive engine models are necessary to gain better insight into scramjet propulsion. Separately, robust and comprehensive trajectory analysis provides references for vehicles to fly along. However, additional observation and understanding is obtained by integrating the propulsion model inside the trajectory framework. Going beyond curve fitted thrusting models, an integrated scramjet cycle analysis offers rapid trade studies on engine parameters and enables the identification of the most significant and optimal engine parameters for the mission as a whole. Regularization of bang-bang control problems by use of the Epsilon-Trig regularization method has created the possibility to preserve the original equations of motion while still solving these problems through indirect methods. Indirect methods incorporate mathematical information from the optimal control problem to provide high quality, integrated solutions. The minimum time optimal trajectory of a rocket propelled missile is compared to that of a scramjet powered missile to determine the advantages of scramjet technology in this application

ORBITALLY DELIVERED KINETIC MISSILE OPTIMUM TRAJECTORY AND KINEMATIC ALGORITHM SELECTION

In military operations, speed and accuracy are of great importance, especially when it comes to weapons systems like a missile delivered from orbit. Determining the desired path, or trajectory, requires identifying individual points between the starting position and ending position of an individual vehicle or object constrained by the laws of motion and other dynamic constraints placed on that vehicle. Additionally, modern application of kinematics often assumes rotation about the local wing of an aerospace vehicle is the pitch angle, and makes similar assumptions regarding the roll and yaw angles. These assumptions prevent precise expression of motion in coordinates of rotating reference frames. To make this expression precise necessitates transformation between reference frames, and one such transformation is embodied in the Direction Cosine Matrices formed by a sequence of three successive frame rotations. This thesis tackles these complex problems and produces a candidate trajectory that accurately strikes the desired target with a flight time of 2 hours, 34 minutes, and 46 seconds while impacting with a velocity of 11.54 km/sec. It will also be demonstrated that a specific rotation is the most accurate sequence with an average error of 0.14° and a computational run time of 0.013 seconds, demonstrating a low computational burden. This results in a 97.95% and 99.84% increase in accuracy over two commonly used rotation sequences.Major, United States Marine CorpsApproved for public release. Distribution is unlimited