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By introducing the radial basis function neural network (RBFNN) approximation method, utilizing the backstepping method and choosing an approximate Lyapunov function, we construct an adaptive state-feedback controller which assures the closed-loop system to be mean square semi-global-uniformly ultimately bounded (M-SGUUB).
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Then, an adaptive compensation controller is constructed by utilizing the backstepping technique.
The observer gains are determined by utilizing the backstepping technique, which enables to stabilize the distributed parameter observer error dynamics.
This paper shows that we can use the backstepping method for semi-batch reactor temperature control.
The backstepping method in this study utilizes a command filter to avoid the problem of "explosion of complexity" that occurs in the traditional backstepping method.
The state feedback is designed by using the backstepping method.
The controller is derived based on the backstepping method.
The backstepping method is used to design the control law.
In Section 3, as the main part of this note, an adaptive controller and tuning functions are designed by using the backstepping method for fractional-order nonlinear systems.
The control design is based on the backstepping method and the introduction of an appropriate observer.
The controller designed is nonlinear and is based on the Backstepping method.
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