Modeling transit bus emissions and fuel economy requires a large amount of experimental data over wide ranges of operational conditions. Chassis dynamometer tests are typically performed using representative driving cycles defined based on vehicle instantaneous speed as sequences of “microtrips”, which are intervals between consecutive vehicle stops. Overall significant parameters of the driving cycle, such as average speed, stops per mile, kinetic intensity, and others, are used as independent variables in the modeling process. Performing tests at all the necessary combinations of parameters is expensive and time consuming. In this paper, a methodology is proposed for building driving cycles at prescribed independent variable values using experimental data through the concatenation of “microtrips” isolated from a limited number of standard chassis dynamometer test cycles. The selection of the adequate “microtrips” is achieved through a customized evolutionary algorithm. The genetic representation uses microtrip definitions as genes. Specific mutation, crossover, and karyotype alteration operators have been defined. The Roulette-Wheel selection technique with elitist strategy drives the optimization process, which consists of minimizing the errors to desired overall cycle parameters. This utility is part of the Integrated Bus Information System developed at West Virginia University.

This paper presents the development of a biologically inspired generalized conceptual framework for the detection, identification, evaluation, and accommodation of aircraft subsystem abnormal conditions. The artificial immune system paradigm in conjunction with other artificial intelligence techniques, analytical tools, and heuristics are used in an attempt to provide a comprehensive solution to the problem of safely operating aircraft under abnormal flight conditions. The main concepts and foundations are established, and methodologies and algorithms for implementation are outlined. The approach addresses directly the complexity and multidimensionality of aircraft dynamic response in the context of abnormal conditions and is expected to facilitate the design of onboard augmentation systems to increase aircraft survivability, improve operation safety, and optimize performance at both normal and abnormal/upset conditions. Results obtained with an example implementation are presented to illustrate the potential of the proposed framework for producing high-performance schemes for aircraft subsystem abnormal condition detection, identification, evaluation, and accommodation.

This paper describes the results of an attempt to develop a modelling and simulation environment for the closed-loop dynamics of the NASA Intelligent Flight Control System F-15 aircraft. Emphasis was placed on the modelling of fault tolerance capabilities for the aircraft flight control system. Failures of the primary control surfaces are simulated for the most common failure scenarios, which are actuator blockage with and without a missing portion of the control surface. The controller featured in the flight control system has an optimal “feedforward and feedback” architecture to provide desirable handling qualities at nominal flight conditions while retaining good performance at post-failure conditions. The parameters within the control laws are updated using an online real-time parameter identification scheme based on the Fourier Transform Regression method. The desirable performance and utility of the entire scheme are illustrated through numerical simulations in the event of stabilator failure.