Clearance of Nonlinear Flight Control Laws Using Hybrid Evolutionary Optimization

Abstract

Abstract-The application of two evolutionary optimization methods, namely, differential evolution and genetic algorithms, to the clearance of nonlinear flight control laws for highly augmented aircraft is described. The algorithms are applied to the problem of evaluating a nonlinear handling quality clearance criterion for a simulation model of a high-performance aircraft with a delta canard configuration and a full-authority flight control law. Hybrid versions of both algorithms, incorporating local gradient-based optimization, are also developed and evaluated. Statistical comparisons of computational cost and global convergence properties reveal the benefits of hybridization for both algorithms. The differential evolution approach in particular, when appropriately augmented with local optimization methods, is shown to have significant potential for improving both the reliability and efficiency of the current industrial flight clearance process.

Innovative Model-Based PID Control Design for Bus Voltage Regulation with STATCOM in Multi-Machine Power Systems

“…To solve the optimization problem (3), this study employs a hybrid genetic algorithm [10,11]. The optimal parameters are given by The optimal PID gain and parameters    ,    ,    are then obtained by solving (10) using SQP (Sequential Quadratic Programming) as follows:…”

“…The optimization problem can be solved using any optimization solver, e.g., a genetic algorithm [10,11] or particle swarm.…”

“…Besides, constraint (11) implies that the closed-loop system is asymptotically stable and constraint (12) results in the non-dominant three closed-loop poles to be placed to the left of the dominant ones. In the following section, the simulation results are described.…”

“…To construct the above mentioned model, the input for the step response is the reactive current injected by the installed STATCOM and the output is the corresponding bus voltage. In this study, building the LTI model is reformulated as a nonlinear optimization problem that is solved using a hybrid GA (Genetic Algorithm) [10,11]. In view of the constructed model, one can observe that there are two open-loop poles close to the imaginary axis and that these cause the oscillatory behavior of bus voltage response.…”

Designing Optimal Fuzzy Controller for MRD-Based Train-Car Suspension Systems

“…In this model, the FL can provide useful solutions for function approximation with a high degree of flexibility along with a vigorous tool for information inference [8,12]; while the sliding mode approach can create adaptive control laws based on its thorough stability analysis capability [7,9,10,18]. In this tendency, optimization methods such as the Evolutionary Algorithm (EA), Differential Evolution (DE) [31,33] or the algorithm rank-DE [34] could be employed to improve the system performance. Such advantages of the FSMC model have been effectively exploited in many fields, including suspension systems [15,16,20].…”