A partial state feedback based direct adaptive control scheme is developed using Orthonormal Activation Function based Neural Networks (OAFNN), for real time trajectory tracking control of a class of nonlinear systems. The OAFNNs are employed in the controller for feed-forward compensation of unknown system dynamics. The network weights are tuned on-line, in real time using Desired Compensation based Adaptive Laws (DCAL). The overall stability of the system and the neural networks is guaranteed using Lyapunov analysis and a modified integral version of projection algorithm. The developed neural controllers are evaluated experimentally and the experimental results support theoretical analysis.

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