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An adaptive nonlinear control method is developed for a class of completely nonaffine pure-feedback systems by the combination of back-stepping design and singular perturbation theory. This strategy can simplify the back-stepping design for the control of pure-feedback system by eliminating the problem of “explosion of complexity”. The virtual/actual control input is derived from the solution of a...
An adaptive output feedback control scheme is proposed for a class of non-affine nonlinear system in which the output signal can track the reference signal. A state observer is constructed to estimate the unknown state in system. A three-layer neural network is introduced to compensate the modeling errors and a Robust control is also used to reduce the approximation error, which adds to the anti-interference...
A sufficient condition to solve an optimal control problem is to solve the Hamilton-Jacobi-Bellman (HJB) equation. However, finding a value function that satisfies the HJB equation for a nonlinear systems is challenging. Previous efforts have utilized feedback linearization methods which assume exact model knowledge, or have developed neural network (NN) approximations of the HJB value function. The...
We consider the existence of global solutions of the nonlinear elastodynamic system with a boundary dissipation structure when initial data are small. The medium material is assumed to be isotropic and hyperelastic. We show the existence of global smooth solutions. In particular, we obtain the exponential decay of the energy, which implies the exponential stabilization of the system by boundary feedback.
This paper focus on adaptive output feedback control of a class of underactuated systems using neural networks. Through Lyapunov-like stability analysis, adaptive laws are obtained to drive neural networkpsilas free parameter adjustment and at the same time assure uniformly ultimate boundedness of the error signals. The approach permits to enhance the performance of an available linear controller...
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