Multi-objective evolutionary–fuzzy augmented flight control for an F16 aircraft

In this article, the multi-objective design of a fuzzy logic augmented flight controller for a high performance fighter jet (the Lockheed-Martin F16) is described. A fuzzy logic controller is designed and its membership functions tuned by genetic algorithms in order to design a roll, pitch, and yaw flight controller with enhanced manoeuverability which still retains safety critical operation when combined with a standard inner-loop stabilizing controller. The controller is assessed in terms of pilot effort and thus reduction of pilot fatigue. The controller is incorporated into a six degree of freedom motion base real-time flight simulator, and flight tested by a qualified pilot instructor

H∞ fuzzy control for systems with repeated scalar nonlinearities and random packet losses

Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper is concerned with the H∞ fuzzy control problem for a class of systems with repeated scalar nonlinearities and random packet losses. A modified Takagi-Sugeno (T-S) fuzzy model is proposed in which the consequent parts are composed of a set of discrete-time state equations containing a repeated scalar nonlinearity. Such a model can describe some well-known nonlinear systems such as recurrent neural networks. The measurement transmission between the plant and controller is assumed to be imperfect and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to represent the phenomenon of random packet losses. Attention is focused on the analysis and design of H∞ fuzzy controllers with the same repeated scalar nonlinearities such that the closed-loop T-S fuzzy control system is stochastically stable and preserves a guaranteed H∞ performance. Sufficient conditions are obtained for the existence of admissible controllers, and the cone complementarity linearization procedure is employed to cast the controller design problem into a sequential minimization one subject to linear matrix inequalities, which can be readily solved by using standard numerical software. Two examples are given to illustrate the effectiveness of the proposed design method

Robust fuzzy PSS design using ABC

This paper presents an Artificial Bee Colony (ABC) algorithm to tune optimal rule-base of a Fuzzy Power System Stabilizer (FPSS) which leads to damp low frequency oscillation following disturbances in power systems. Thus, extraction of an appropriate set of rules or selection of an optimal set of rules from the set of possible rules is an important and essential step toward the design of any successful fuzzy logic controller. Consequently, in this paper, an ABC based rule generation method is proposed for automated fuzzy PSS design to improve power system stability and reduce the design effort. The effectiveness of the proposed method is demonstrated on a 3-machine 9-bus standard power system in comparison with the Genetic Algorithm based tuned FPSS under different loading condition through ITAE performance indices

Fuzzy Logic Path Planning System for Collision Avoidance by an Autonomous Rover Vehicle

Systems already developed at JSC have shown the benefits of applying fuzzy logic control theory to space related operations. Four major issues are addressed that are associated with developing an autonomous collision avoidance subsystem within a path planning system designed for application in a remote, hostile environment that does not lend itself well to remote manipulation of the vehicle involved through Earth-based telecommunication. A good focus for this is unmanned exploration of the surface of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. The four major issues addressed are: (1) avoidance of a single fuzzy moving obstacle; (2) back off from a dead end in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system