Evolutionary design of a full-envelope full-authority flight control system for an unstable high-performance aircraft

The use of an evolutionary algorithm in the framework of H1 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 a complete full-authority longitudinal control system for an unstable high-performance jet aircraft featuring (i) a stability and control augmentation system and (ii) autopilot functions (speed and altitude hold). Constraints on closed-loop response are enforced, that representing 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, used as the scheduling parameter. Nonetheless, the dynamic behaviour of the aircraft may exhibit significant variations when flying at different altitudes, even for the same value of the dynamic pressure, so that a trade-off is required between different feasible controllers synthesized at different altitudes for a given equivalent airspeed. A multiobjective 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 non-linear model of the aircraft

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

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

An aerothermodynamic design optimization framework for hypersonic vehicles

In the aviation field great interest is growing in passengers transportation at hypersonic speed. This requires, however, careful study of the enabling technologies necessary for the optimal design of hypersonic vehicles. In this framework, the present work reports on a highly integrated design environment that has been developed in order to provide an optimization loop for vehicle aerothermodynamic design. It includes modules for geometrical parametrization, automated data transfer between tools, automated execution of computational analysis codes, and design optimization methods. This optimization environment is exploited for the aerodynamic design of an unmanned hypersonic cruiser flying at M∞=8 and 30 km altitude. The original contribution of this work is mainly found in the capability of the developed optimization environment of working simultaneously on shape and topology of the aircraft. The results reported and discussed highlight interesting design capabilities, and promise extension to more challenging and realistic integrated aerothermodynamic design problems

An Innovative Mission Management System for Fixed-Wing UAVs

This paper presents two innovative units linked together to build the main frame of a UAV Mis- sion Management System. The first unit is a Path Planner for small UAVs able to generate optimal paths in a tridimensional environment, generat- ing flyable and safe paths with the lowest com- putational effort. The second unit is the Flight Management System based on Nonlinear Model Predictive Control, that tracks the reference path and exploits a spherical camera model to avoid unpredicted obstacles along the path. The control system solves on-line (i.e. at each sampling time) a finite horizon (state horizon) open loop optimal control problem with a Genetic Algorithm. This algorithm finds the command sequence that min- imizes the tracking error with respect to the ref- erence path, driving the aircraft far from sensed obstacles and towards the desired trajectory

Application of Cargo Distribution Computation in Airbus A330 Cargo Aircraft with Optimization Algorithms

Weight and balance problems are one of the main reasons for cargo aircraft accidents including around 30% of accidents that are due to Center of Gravity (CG). Because the pilots often calculate CG index using Load & Trim Sheets manually or use a set of simple formulas, in these calculations, it is only checked whether CG index is within the safe zone instead of determining the ideal value. In order for the safety and fuel economy to be maximized in an aircraft, CG index should be calculated at the ideal value given in the Aircraft Handling Manual. Due to safety and cost concerns, airline companies prefer non-commercial optimization solutions. Therefore, we proposed new heuristic approaches that have been motivated by a real-world application for a major airline company. First, we applied standard GA, WSA, PSO algorithms to obtain a solution that is as close as possible to the ideal CG index in an Airbus A330 cargo plan. Then, we modified standard WSA and PSO algorithms to decrease the error value and to better achieve the ideal CG index. These proposed heuristic solutions have the potential to help the pilots flying cargo aircraft with maximum safety and minimum fuel consumption

Can trained monkeys design flight controllers for hypersonic vehicles?

The supersonic combustion ramjet is an as yet unproven propulsion system for hypersonic flight. Provided it can be developed into a practical vehicle, the ultimate success of sustained hypersonic flight will depend on configuring a robust and stable airframe-propulsion-control combination. To design the longitudinal flight controller for this inherently unstable vehicle we have applied a genetic algorithm, hence the trained monkeys metaphor in the title. Being a nondeterministic search method, there is no guarantee of generating a useful solution, yet given a little direction and enough time it is able to solve hard problems. The controller is built using fuzzy logic rules, directed at manipulating the vehicle's angle of attack through the actuation of symmetric elevators. A preset structure for the rules is used whereby the design task is to configure the control surface through selection of the rule consequents. To direct the search for a controller design, the genetic algorithm uses simulated flight responses to a range of initial conditions, without linearization of the vehicle model and dynamics. Results for the genetic algorithm designed controller show longitudinal stability and disturbance rejection

Multiobjective optimization of electromagnetic structures based on self-organizing migration

Práce se zabývá popisem nového stochastického vícekriteriálního optimalizačního algoritmu MOSOMA (Multiobjective Self-Organizing Migrating Algorithm). Je zde ukázáno, že algoritmus je schopen řešit nejrůznější typy optimalizačních úloh (s jakýmkoli počtem kritérií, s i bez omezujících podmínek, se spojitým i diskrétním stavovým prostorem). Výsledky algoritmu jsou srovnány s dalšími běžně používanými metodami pro vícekriteriální optimalizaci na velké sadě testovacích úloh. Uvedli jsme novou techniku pro výpočet metriky rozprostření (spread) založené na hledání minimální kostry grafu (Minimum Spanning Tree) pro problémy mající více než dvě kritéria. Doporučené hodnoty pro parametry řídící běh algoritmu byly určeny na základě výsledků jejich citlivostní analýzy. Algoritmus MOSOMA je dále úspěšně použit pro řešení různých návrhových úloh z oblasti elektromagnetismu (návrh Yagi-Uda antény a dielektrických filtrů, adaptivní řízení vyzařovaného svazku v časové oblasti…).This thesis describes a novel stochastic multi-objective optimization algorithm called MOSOMA (Multi-Objective Self-Organizing Migrating Algorithm). It is shown that MOSOMA is able to solve various types of multi-objective optimization problems (with any number of objectives, unconstrained or constrained problems, with continuous or discrete decision space). The efficiency of MOSOMA is compared with other commonly used optimization techniques on a large suite of test problems. The new procedure based on finding of minimum spanning tree for computing the spread metric for problems with more than two objectives is proposed. Recommended values of parameters controlling the run of MOSOMA are derived according to their sensitivity analysis. The ability of MOSOMA to solve real-life problems from electromagnetics is shown in a few examples (Yagi-Uda and dielectric filters design, adaptive beam forming in time domain…).

Aerodynamic Shape Optimisation of a Proprotor and Its Validation by Means of CFD and Experiments

The aerodynamic shape design of a proprotor for a tiltrotor aircraft is a very complex and demanding task because it has to combine good hovering capabilities with high propeller efficiency. The aim of the present work is to describe a two-level procedure and its results for the aerodynamic shape design of a new rotor blade for a high-performance tiltwing tiltrotor aircraft taking into account the most important flight conditions in which the aircraft can operate. Span-wise distributions of twist, chord and aerofoil were chosen making use of a multi-objective genetic optimiser that worked on three objectives simultaneously. A non-linear sweep angle distribution along the blade was designed to reduce the power losses due to compressibility effects during axial flight at high speed. During the optimisation process, the aerodynamic performance of the blade was evaluated with a classical two-dimensional strip theory solver. The optimised blade was than analysed by means of a compressible Navier-Stokes solver and calculations were validated comparing numerical results with experimental data obtained from wind-tunnel tests of a scaled model of the proprotor