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

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

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

Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework

Simplicity and flexibility of meta-heuristic optimization algorithms have attracted lots of attention in the field of optimization. Different optimization methods, however, hold algorithm-specific strengths and limitations, and selecting the best-performing algorithm for a specific problem is a tedious task. We introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme. SC-SAHEL explores performance of different EAs, such as the capability to escape local attractions, speed, convergence, etc., during population evolution as each individual EA suits differently to various response surfaces. The SC-SAHEL algorithm is benchmarked over 29 conceptual test functions, and a real-world hydropower reservoir model case study. Results show that the hybrid SC-SAHEL algorithm is rigorous and effective in finding global optimum for a majority of test cases, and that it is computationally efficient in comparison to algorithms with individual EA

Chaotic multi-objective optimization based design of fractional order PI{\lambda}D{\mu} controller in AVR system

In this paper, a fractional order (FO) PI{\lambda}D\mu controller is designed to take care of various contradictory objective functions for an Automatic Voltage Regulator (AVR) system. An improved evolutionary Non-dominated Sorting Genetic Algorithm II (NSGA II), which is augmented with a chaotic map for greater effectiveness, is used for the multi-objective optimization problem. The Pareto fronts showing the trade-off between different design criteria are obtained for the PI{\lambda}D\mu and PID controller. A comparative analysis is done with respect to the standard PID controller to demonstrate the merits and demerits of the fractional order PI{\lambda}D\mu controller.Comment: 30 pages, 14 figure

Multi-disciplinary robust design of variable speed wind turbines

This paper addresses the preliminary robust multi-disciplinary design of small wind turbines. The turbine to be designed is assumed to be connected to the grid by means of power electronic converters. The main input parameter is the yearly wind distribution at the selected site, and it is represented by means of a Weibull distribution. The objective function is the electrical energy delivered yearly to the grid. Aerodynamic and electrical characteristics are fully coupled and modelled by means of low- and medium-fidelity models. Uncertainty affecting the blade geometry is considered, and a multi-objective hybrid evolutionary algorithm code is used to maximise the mean value of the yearly energy production and minimise its variance