Evolutionary design of a full–envelope flight control system for an unstable fighter aircraft

The use of an evolutionary algorithm in the framework of H∞ control theory is being considered as a means for synthesizing controller gains that minimize a weighted combination of the infinite-norm of the sensitivity function (for disturbance attenuation requirements) and complementary sensitivity function (for robust stability requirements) at the same time. The case study deals with the stability and control augmentation of an unstable high-performance jet aircraft. Constraints on closed-loop response are also enforced, that represent typical requirements on airplane handling qualities, that makes the control law synthesis process more demanding. Gain scheduling is required, in order to obtain satisfactory performance over the whole flight envelope, so that the synthesis is performed at different reference trim conditions, for several values of the dynamic pressure, Q, used as the scheduling parameter. Nonetheless, the dynamic behaviour of the aircraft may exhibit significant variations when flying at different altitudes h, even for the same value of the dynamic pressure, so that a trade-off is required between different feasible controllers synthesized for a given value of Q, but different h. A multi-objective search is thus considered for the determination of the best suited solution to be introduced in the scheduling of the control law. The obtained results are then tested on a longitudinal nonlinear model of the aircraft

Testing approaches for global optimization of space trajectories

In this paper, the procedures to test global search algorithms applied to space trajectory design problems are discussed. Furthermore, a number of performance indexes that can be used to evaluate the effectiveness of the tested algorithms are presented. The performance indexes are then compared and the actual significance of each one of them is highlighted. Three global optimization algorithms are tested on three typical space trajectory design problems

Multidisciplinary design of a micro-USV for re-entry operations

Unmanned Space Vehicles (USV) are seen as a test-bed for enabling technologies and as a carrier to deliver and return experiments to and from low-Earth orbit. USV's are a potentially interesting solution also for the exploration of other planets or as long-range recognisance vehicles. As test bed, USV's are seen as a stepping stone for the development of future generation re-usable launchers but also as way to test key technologies for re-entry operations. Examples of recent developments are the PRORA-USV, designed by the Italian Aerospace Research Center (CIRA) in collaboration with Gavazzi Space, or the Boeing X-37B Orbital Test Vehicle (OTV), that is foreseen as an alternative to the space shuttle to deliver experiments into Earth orbit. Among the technologies to be demonstrated with the X-37 are improved thermal protection systems, avionics, the autonomous guidance system, and an advanced airfram

Comparative study on the application of evolutionary optimization techniques to orbit transfer maneuvers

Orbit transfer maneuvers are here considered as benchmark cases for comparing performance of different optimization techniques in the framework of direct methods. Two different classes of evolutionary algorithms, a conventional genetic algorithm and an estimation of distribution method, are compared in terms of performance indices statistically evaluated over a prescribed number of runs. At the same time, two different types of problem representations are considered, a first one based on orbit propagation and a second one based on the solution of Lambert’s problem for direct transfers. In this way it is possible to highlight how problem representation affects the capabilities of the considered numerical approaches

Robust design of a reentry unmanned space vehicle by multifidelity evolution control

This paper addresses the preliminary robust design of a small-medium scale re-entry unmanned space vehicle. A hybrid optimization technique is proposed that couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. Uncertainties on the aerodynamic forces and vehicle mass are integrated in the design process and the hybrid algorithm searches for geometries that a) minimize the mean value of the maximum heat flux, b) maximize the mean value of the maximum achievable distance, and c) minimize the variance of the maximum heat flux. The evolutionary part handles the system design parameters of the vehicle and the uncertain functions, while the direct transcription method generates optimal control profiles for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are used to approximate the aerodynamic forces required by the direct transcription method. The artificial neural networks are trained and updated by means of a multi-fidelity, evolution control approach

Multi-objective design of robust flight control systems

A multi–objective evolutionary algorithm is used in the framework of H1 control theory to find the controller gains that minimize a weighted combination of the infinite–norm of the sensitivity function (for disturbance attenuation requirements) and complementary sensitivity function (for robust stability requirements). After considering a single operating point for a level flight trim condition of a F-16 fighter aircraft model, two different approaches will then be considered to extend the domain of validity of the control law: 1) the controller is designed for different operating points and gain scheduling is adopted; 2) a single control law is designed for all the considered operating points by multiobjective minimisation. The two approaches are analyzed and compared in terms of effectiveness of the design method and resulting closed loop performance of the system

Multi-objective design of robust flight control systems

The aim of this work is to demonstrate the capabilities of evolutionary methods in the design of robust controllers for unstable fighter aircraft in the framework of H1 control theory. A multi–objective evolutionary algorithm is used to find the controller gains that minimize a weighted combination of the infinite–norm of the sensitivity function (for disturbance attenuation requirements) and complementary sensitivity function (for robust stability requirements). After considering a single operating point for a level flight trim condition of a F-16 fighter aircraft model, two different approaches will then be considered to extend the domain of validity of the control law: 1) the controller is designed for different operating points and gain scheduling is adopted; 2) a single control law is designed for all the considered operating points by multiobjective minimisation. The two approaches will be analysed and compared in terms of efficacy and required human and computational resources

Optimal power harness routing for small-scale satellites

This paper presents an approach to optimal power harness design based on a modified ant colony optimisation algorithm. The optimisation of the harness routing topology is formulated as a constrained multi-objective optimisation problem in which the main objectives are to minimise the length (and therefore the mass) of the harness. The modified ant colony optimisation algorithm automatically routes different types of wiring, creating the optimal harness layout. During the optimisation the length, mass and bundleness of the cables are computed and used as cost functions. The optimisation algorithm works incrementally on a finite set of waypoints, forming a tree, by adding and evaluating one branch at a time, utilising a set of heuristics using the cable length and cable bundling as criteria to select the optimal path. Constraints are introduced as forbidden waypoints through which digital agents (hereafter called ants) cannot travel. The new algorithm developed will be applied to the design of the harness of a small satellite, with results highlighting the capabilities and potentialities of the code

Robust aerodynamic design of variable speed wind turbine rotors

This study focuses on the robust aerodynamic design of the bladed rotor of small horizontal axis wind turbines. The optimization process also considers the effects of manufacturing and assembly tolerances on the yearly energy production. The aerodynamic performance of the rotors so designed has reduced sensitivity to manufacturing and assembly errors. The geometric uncertainty affecting the rotor shape is represented by normal distributions of the pitch angle of the blades, and the twist angle and chord of their airfoils. The aerodynamic module is a blade element momentum theory code. Both Monte Carlo-based and the Univariate ReducedQuadrature technique, a novel deterministic uncertainty propagationmethod, are used. The performance of the two approaches is assessed both interms of accuracy and computational speed. The adopted optimization method is based on a hybrid multi-objective evolutionary strategy. The presented results highlight that the sensitivity of the yearly production to geometric uncertainties can be reduced by reducing the rotational speed and increasing the aerodynamic blade loads