Abstract
In this article, a neural network–based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design.
Highlights
In the past decades, the unmanned aerial vehicles have been widely studied since they provide a promising manner to fulfill the increasing demands in both commercial and industrial applications
Inspired by the aforementioned works, in this article, we propose an neural network (NN) control enhanced by deterministic learning techniques for the helicopter systems, and special mechanism is embedded in the control design to ensure global stability of the NN control
Consider the subsystems of helicopter dynamic in (4) (5) (6) with the tracking errors (15) under assumptions 1 and 2, employ the global NN controllers (25) and (43) with the NN weight adaptive laws in equations (27) and (45), we have (i) all the signals remain globally uniformly ultimately boundedness (GUUB) and (ii) the tracking errors e1 and e2 converge to a small neighborhood of zero
Summary
The unmanned aerial vehicles have been widely studied since they provide a promising manner to fulfill the increasing demands in both commercial and industrial applications. Consider the subsystems of helicopter dynamic in (4) (5) (6) with the tracking errors (15) under assumptions 1 and 2, employ the global NN controllers (25) and (43) with the NN weight adaptive laws in equations (27) and (45), we have (i) all the signals remain GUUB and (ii) the tracking errors e1 and e2 converge to a small neighborhood of zero. Considering the helicopter system defined in equations (4) to (6), the filtered tracking errors in equation (15), and the NN adaptation law (27) and (45), for any recurrent orbit i, and initial conditions W^ið0Þ 1⁄4 0, we have that the NN weight estimate converges to small neighborhoods of its optimal value Wià along iðziðtÞÞðt!TiÞ, and rthateelsyysbtyemeitdhyenr aWm^ TiicSsi ðfziiðÞzioÞr could be. As shown in the figures, using the proposed global RBFNN controller, the e1 (m)
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