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Analysis and Design of Advanced Miniscale Mechatronic Systems: Synthesis of Intelligent Flight Servos

The flight vehicle performance (flight and handling qualitites, agility, controllability, maneuverability, et cetera) depends upon flight actuators, which displace the control surfaces. Intelligent flight servos must be designed to achieve the specified criteria, requirements, and standards. Miniscale electromechanical flight actuators are actuated by electric servo-motors, and brushless permanent-magnet synchronous machines are perfectly suited due to their efficiency, reliability, high torque density, low cost and maintenance, simplicity, and ruggedness. However, conventional controllers do not ensure the dynamic performance of flight actuators in the full operating envelope under rapidly changing flight and environmental conditions and aerodynamic loads. An intelligent flight servo is designed using the developed neural networks synthesis procedure. The Hopfield equations are modified to incorporate delay for finite switching. The effect of even small delays due to finite switching for composite large networks could be significant, and, therefore, closed-loop systems may exhibit delay-induced instability. The results obtained are valid for systems without delays if the conditions of the theorems hold. These results are applied to illustrate the application of neural network-based control systems design to advanced flight servos . It is shown that new controllers must be synthesized because synchronous servomotors are controlled taking into account the electromagnetic features, software, and hardware used. Control signals, which drive high-switching transistors (more specifically, MOSFET drivers), are developed by the DSP based upon the rotor angular displacement. The DSP performs control and decision-making through learning, adaptation, reconfiguration, scheduling, and optimization mechanisms. Other functions, such as identification, estimation, and diagnostics, can be also performed. We demonstrate that the neural network-based intelligent controllers guarantee the specified tracking accuracy, desired deflection rate, and disturbance attenuation. The system performance is documented.