Self-recurrent wavelet neural network based indirect adaptive control architecture with modified adaptive learning rates for the speed control of motion platforms

Arı, Evrim Onur
Motion platforms are widely employed in military systems for the purpose of controlling payloads like optical sensors, antennas, guns etc. One of the most critical components of these platforms is the motion control sub-system which is responsible for controlling the speed of the platform. In this thesis, the components of the speed control are investigated in detail and finally a novel control architecture was proposed in order to improve the transient performance of the speed control without any adverse effect on its robustness. This architecture is a form of indirect adaptive control using Self-Recurrent Wavelet Neural Networks (SRWNNs). The architecture is enhanced by a novel parameter update method in order to guarantee fast convergence, and an additional algorithm for structural evolution. The performance of the proposed architecture has been shown with simulations and verified with experiments. Moreover, its performance is compared with several robust RST-based control designs.


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Citation Formats
E. O. Arı, “Self-recurrent wavelet neural network based indirect adaptive control architecture with modified adaptive learning rates for the speed control of motion platforms,” Ph.D. - Doctoral Program, Middle East Technical University, 2015.