Full-state constraints stabilization control for uncertain nonlinear systems
In this paper,the full-state constraints stabilization control for a class of nonlinear systems with unknown functions is studied.Different from the fuzzy approximation method and the neural network approximation method for solving the unknown function problem,the control method in this paper can make the system state asymptotically converge to the origin,and solve the"explosion of terms"problem of the backstepping method.At the same time,different from the barrier Lyapunov functions for solving the full-state constraints control problem,a new full-state constraints method is proposed to make the state asymptotically stable.Finally,the simulation results of Duffing system and single-link robot verify the effectiveness of this algorithm.
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