Saeid Niazi, Alireza Toloei, and Reza Ghasemi


Gimbal mechanism, neural network predictive, body angular rates,stability, coupling


Gimbal mechanisms are used to prepare the stabilization of the detector vector in pointing to the mission and tracking the tar- get. Stabilizing loop for separating detector in the presence of disturbances introduced by the environment is derived. Compared to the most of the researches simplified, the gimbal mechanism model, a mathematical model of gimbal mechanism with mass im- balance, the inertia cross-coupling between two channels of azimuth and elevation, and the body angular rates are developed in this article. The stabilization loops are constructed utilizing a neural network predictive (NNP) controller. Using an NNP controller in stabilizing loop of gimbal mechanism to successful stabilization in the presence of both the angular body rate disturbance and the cross-coupling effect, convergence of the error to neighbourhood of zero, and stability of the closed loop system is all the novelty of this article. The comprehensive control system is simulated using MAT- LAB/SIMULINK. Then, the performance of the proposed controller is analysed comparing with the conventional proportional–integral controller in terms of transient response analysis and quantitative study of error analysis. Finally, the results prove the efficiency of the NNP controller that is proposed, which offers better response respect to the traditional proportional-integral-derivative (PID) controller, and improve the response of gimbal mechanism in the presence of the external disturbances.

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