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Neural networks are used to approximate the nonlinearities and indirect adaptive backstepping technique is employed to construct controllers.
Fuzzy logic systems are used to approximate the nonlinearities and an adaptive backstepping technique is employed to construct controllers.
Fuzzy logic systems are used to approximate unknown nonlinearities and an adaptive backstepping technique is employed to construct controllers.
As such systems belong to non-minimum phase systems and thus our result extends the class of systems stabilizable from minimum phase systems when the standard backstepping technique is employed.
Adaptive backstepping technique is employed to systematically synthesize the proposed controller with the online parameter adaptation rule to the vehicle mass variations and with the recurrent neural network (RNN) approximation to the coupling effect between the force and moment controls.
In fact, the backstepping technique is employed to enrich the Lyapunov redesign method to compensate for not only matched – but also unmatched-uncertainties. On the other hand, using the Lyapunov redesign method in each step of the conventional backstepping technique makes backstepping robust.
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In the proposed control scheme, fuzzy logic systems are used to approximate the unknown nonlinearity in each backstepping design, and the classic adaptive technique is employed to deal with some unknown parameters.
A zonal reporting technique was employed.
A simple randomization technique was employed.
A multistage sampling technique was employed.
A double-portal endoscopic technique was employed.
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