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Advancements in electrical, mechanical, and structural design onboard modern more electric aircraft have added significant stress to the electrical systems. An electrical system level analysis tool has been created in MATLAB/Simulink to facilitate rapid system analysis and optimization to meet the growing demands of modern aircraft. An integratated model of segment level models of an electrical system including a generator, electrical accumulator unit, electrical distribution unit and electromechanical actuators has been developed. Included in the model are mission level models of an engine and aircraft to provide relevant boundary conditions. It is anticipated that the tracking of the electrical distribution through numerical integration of these various subsystems will lead to more accurate predictions of the bus power quality. In this paper the tool is used to evaluate two architectures using two different load profiles.

Looking forward to an autonomous Unmanned Combat Aerial Vehicle (UCAV) for future applications, it becomes apparent that on-board intelligent controllers will be necessary for these advanced systems. LETHA (Learning Enhanced Tactical Handling Algorithm) was created to develop intelligent managers for these advanced unmanned craft through the novel means of a genetic cascading fuzzy system. In this approach, a genetic algorithm creates rule bases and optimizes membership functions for multiple fuzzy logic systems, whose inputs and outputs feed into one another alongside crisp data. A simulation space referred to as HADES (Hoplological Autonomous Defend and Engage Simulation) was created in which LETHA can train the UCAVs intelligent controllers.

Preliminary investigations of nonlinear modeling of aircraft synchronous generators using neural networks are presented. Aircraft synchronous generators with high power density tend operate at current-levels proportional to the magnetic saturation region of the machine's material. The nonlinear model accounts for magnetic saturation of the generator, which causes the winding flux linkages and inductances to vary as a function of current. Finite element method software is used to perform a parametric sweep of direct, quadrature, and field currents to extract the respective flux linkages. This data is used to train a neural network which yields current as a function of flux linkage. The neural network is implemented in a Simulink synchronous generator model and simulation results are compared with a previously developed linear model. Results show that the nonlinear neural network model can more accurately describe the responsiveness and performance of the synchronous generator.

Cost and performance requirements are driving military and commercial systems to highly integrated, optimized systems which require more sophisticated, highly complex controls. To realize benefits and make confident decisions, the validation of both plant and control models becomes critical. To quickly develop controls for these systems, it is beneficial to develop models and determine the uncertainty of those models so as to predict performance and stability. A process of model validation for a boost circuit based on acceptance sampling is presented here. The validation process described in this paper includes the steps of defining requirements, performing a screening and exploration of the system, completing a system and parameter identification, and finally executing a validation test. To minimize the cost of experimentation and simulation, design of experiments is used extensively to limit the amount of data taken without losing information.

Cost and performance requirements are driving military and commercial systems to become highly integrated, optimized systems which require more sophisticated, highly complex controls. To realize benefits of those complex controls and make confident decisions, the validation of both plant and control models becomes critical. To quickly develop controls for these systems, it is beneficial to develop plant models and determine the uncertainty of those models to predict performance and stability of the control algorithms. A process of model and control algorithm validation for a dc-dc boost converter circuit based on acceptance sampling is presented here. The validation process described in this paper is based on MIL-STD 3022 with emphasis on requirements settings and the testing process. The key contribution of this paper is the process for model and control algorithm validation, specifically a method for decomposing the problem into model and control algorithm validation stages.