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For mission-critical and time-sensitive navigation of autonomous vehicles, controller design must exhibit excellent tracking performance with respect to the speed of convergence to reference command and steady-state accuracy. In this article, a novel design integration of the neural network with the traditional control system is proposed to adaptively obtain optimized controller parameters resulting in improved transient and steady-state performance of motion and position control of autonomous vehicles. Application of the proposed intelligent control scheme to mobile robot navigation was presented for an eightshaped trajectory by optimizing a Lyapunov-based nonlinear controller. Furthermore, a Linear Quadratic Regulator-based controller was optimized based on the proposed strategy to control the pitch and yaw angles of a 2-Degree-of -Freedom helicopter. The simulation results showed that the proposed scheme outperforms the traditional controllers in terms of the speed of convergence to the desired trajectory and overall error minimization.In order to execute maneuvers for unmanned Air Vehicles (UAVs), feedback linearization [24,25] and sliding mode control [26] have been proposed, but they have failed for certain models due to the nonlinear dynamics of the vehicle [27]. For helicopter control, backstepping control strategy [28] and Linear Quadratic Regulator (LQR)-based control [29] have been proposed.

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