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A reconfigurable control system is an intelligent control system that detects faults within the system and adjusts its performance automatically to avoid mission failure, save lives, and reduce system maintenance costs. The concept was first successfully demonstrated by NASA between December 1989 and March 1990 on the F-15 flight control system (SRFCS), where software was integrated into the aircraft's digital flight control system to compensate for component loss by reconfiguring the remaining control loop. This was later adopted in the Boeing X-33. Other applications include modular robotics, reconfigurable computing structure, and reconfigurable helicopters. The motivation of this work is to test such control system designs for future long term space missions, more explicitly, the automation of life support systems.

Aircraft incidents and accidents in icing are often the result of degradation in performance and control. However, current ice sensors measure the amount of ice and not the effect on performance and control. No processed aircraft performance degradation information is available to the pilot. In this paper research is reported on a system to estimate aircraft performance and control changes due to ice, then use this information to automatically operate ice protection systems, provide aircraft envelope protection and, if icing is severe, adapt the flight controls. Key to such a safety system would be he proper communication to, and coordination with, the flight crew. This paper reviews the basic system concept, as well as the research conducted in three critical areas; aerodynamics and flight mechanics, aircraft control and identification, and human factors.

The Opposed Piston Two-Stroke (OPTS) engine has many advantages on power density, fuel tolerance, fuel flexibility and package space. A type of self-balanced opposed-piston folded-crank train two-stroke engine for Unmanned Aerial Vehicle (UAV) was studied in this paper. AVL BOOST was used for the thermodynamic simulation. It was a quasi-steady, filling-and-emptying flow analysis -- no intake or exhaust dynamics were simulated. The results were validated against experimental data. The effects of high altitude environment on engine performance have been investigated. Moreover, the matching between the engine and turbocharger was designed and optimized for different altitude levels. The results indicated that, while the altitude is above 6000m, a multi-stage turbocharged engine system need to be considered and optimized for the UAV.

In this paper, a novel driver model is proposed to track vehicle speed in MIL (Model-in-the-Loop) test system, which has structural consistency with HIL (Hardware-in-the-Loop) test system. First, the MIL test system which contains models of driver, vehicle and test bench is established. Second, according to the connections of the established models in Matlab/Simulink environment, the vehicle speed is calculated in vehicle model. Emphatically, through the deviation between driving cycle speed and calculated vehicle speed, PI controller in driver model adjusts the vehicle speed to ideal point through sending the torque command to drive motor, the ILC (Iterative Learning Control) controller modifies and stores P value of PI controller. Then, in order to obtain the better modification of PI controller, iterative learning control algorithm is deeply researched in term of types and parameters.

Approximately 50% energy is consumed during the acceleration of a city bus. Fuel consumption during acceleration is significantly affected by driving behavior. In this study, 13 characteristic parameters were selected to describe driving style based on analysis of how driving influences fuel consumption during acceleration. The 100,000 km real-world vehicle running data of six drivers on three city buses in a particular bus line in Tianjin, China were sampled using a vehicle-on-line data logger. Based on the selected characteristic parameters and collected driving data, an evaluation model of the fuel consumption level of a driver was established by adopting the method of decision tree C4.5. For two-level classification, the model has over 85% prediction accuracy. The model also has the advantages of having a few training samples and strong generalization. As an example of the model application, the fuel-saving potential of a driver under optimal operations was analyzed.

Virtual reality is an emerging technology with the potential for many engineering applications including machinery simulation. In this paper the writers describe the hardware and software components of a virtual reality system that simulates machinery. They detail the flow of information that occurs in this system and discuss the functioning of an existing system at the National Center for Supercomputing Applications (NCSA) located at the University of Illinois at Urbana-Champaign. Finally, they describe potential uses of virtual reality in product design, manufacturing, training and marketing.

Neural networks are massively parallel computational models for knowledge representation and information processing. The capabilities of neural networks, namely learning, noise tolerance, adaptivity, and parallel structure make them good candidates for application to a wide range of engineering problems including diagnostics problems. The general approach in developing neural network based diagnostic methods is described through a case study. The development of an acoustic wayside train inspection system using neural networks is described. The study is aimed at developing a neural network based method for detection defective wheels from acoustic measurements. The actual signals recorded when a train passes a wayside station are used to develop a neural network based wheel defect detector and to study its performance. Signal averaging and scoring techniques are developed to improve the performance of the constructed neural inspection system.

The US Army uses a light tactical High-Mobility Multi-Purpose Wheeled Vehicle (HMMWV) which, due to the amount of armor added, requires air conditioning to keep its occupants comfortable. The current system uses R134a in a dual evaporator, remote-mounted condenser, engine-driven compressor system. This vehicle has been adapted to use an environmentally friendly refrigerant (carbon dioxide) to provide performance, efficiency, comfort and logistical benefits to the Army. The unusual thermal heat management issues and the fact that the vehicle is required to operate under extreme ambient conditions have made the project extremely challenging. This paper is a continuation of work presented at the SAE Alternate Refrigerants Symposium held in Phoenix last June [1].

Although excellent progress has been made recently in powertrain fault diagnosis based on vibration signals, most of them are based on the assumption that the fault features of the training and test data are drawn from the same probability distribution. Due to the limitation of the domain shift phenomenon, the performance of the current intelligent fault diagnosis methods is significantly reduced. Even many existing transfer learning methods have the problem of low generalization ability. Inspired by sparse representation theory, a novel cross-domain fault diagnosis method based on K-means singular value decomposition (K-SVD) and long short-term memory network (LSTM) is proposed in this study. First, K-SVD can convert source domain data into a sparse dictionary and sparse coefficient. The domain-invariant features are explored in the sparse dictionary, which contains redundant features. The sparse coefficients are input into the LSTM to obtain a primary classifier.

This paper proposes an effective nonlinear bicycle model including longitudinal, lateral, and yaw motions of a vehicle. This bicycle model uses a simplified piece-wise linear tire model and tire force tuning algorithm to produce closely matching vehicle trajectory compared to real vehicle for wide vehicle operation ranges. A simplified piece-wise tire model that well represents nonlinear tire forces was developed. The key parameters of this model can be chosen from measured tire forces. For the effects of dynamic load transfer due to sharp vehicle maneuvers, a tire force tuning algorithm that dynamically adjusts tire forces of the bicycle model based on measured vehicle lateral acceleration is proposed. Responses of the proposed bicycle model have been compared with commercial vehicle dynamics model (CarSim) through simulation in various vehicle maneuvers (ramp steer, sine-with-dwell).