- Project Summary
This scheme is a novel Neural Network Parallel Adaptive Controller (NNPAC), which consists of an online growing dynamic Radial Basis Function Neural Network (RBFNN) structure in parallel with a direct Model Reference Adaptive Controller (MRAC), for complex and un-modeled dynamic system control. For the changing modes of operation of the plant, a suitable single reference model is used, while the RBFNN controller operating in parallel with the direct MRAC responds to those changing modes. Moreover, the parallel RBFNN controller is capable of precisely tracking the system output to the desired command signal generation. The update details of the RBFNN width, centers and weights are derived in order to ensure the error reduction and for improved tracking accuracy. The importance of the proposed scheme is its ability to perform effectively even when the plant mode swings without using Multiple Model Concept or a Multiple Reference Model Adaptive Controller. Unlike the Model reference adaptive control in which the plant follows the reference model output, the proposed scheme is capable of preparing the plant output to follow the desired trajectory itself. Moreover, the RBFNN structure avoids the ‘Dimensionality Problem’ inherent in such architecture and is dynamic and growing in nature. Thus the RBFNN online scheme provides an additional control value dynamically inverting the plant nonlinearities, such that the systems un-modeled dynamics are effectively controlled and correcting the reference model output deviation from the command signal.
- For further reading
S. Kamalasadan, Adel A. Ghandakly “A Neural Network Parallel Adaptive Controller Algorithm for Fighter Aircraft Pitch Rate Control” In proceedings of the ISCA 12th International Conference On Intelligent And Adaptive Systems and Software Engineering (IASSE'05), pp.123-128, July 2005. []
S. Kamalasadan, Adel A. Ghandakly, “A Neural Network Parallel Adaptive Controller for dynamic Systems Control”, In Press, IEEE Transactions of Instrumentation and Measurements, June 2007. []