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In this brief, a new adaptive neurocontrol algorithm for a single-input-single-output (SISO) strict-feedback nonlinear system is proposed. Most of the previous adaptive neural control algorithms for strict-feedback nonlinear systems were based on the backstepping scheme, which makes the control law and stability analysis very complicated. The main contribution of the proposed method is that it demonstrates...
A new adaptive fuzzy controller for uncertain strict-feedback nonlinear systems is proposed. All the previous approximator-based adaptive control algorithms for the strict-feedback nonlinear systems are based on backstepping scheme and guarantee semi-global closed-loop system stability. This paper proposes a new globally stabilizing adaptive fuzzy controller for strict-feedback nonlinear system without...
An adaptive neural control problem of SISO fully nonaffine pure-feedback nonlinear system is considered in this paper. The main contribution of the proposed method is that it is shown that the control problem of the pure-feedback system can be viewed as that of the system in the standard normal form. As a result, proposed neural control algorithm is much simpler compared to the recently proposed backstepping-based...
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