|Time-varying Barrier Lyapunov Function Based Adaptive Neural Controller Design for Nonlinear Pure-feedback Systems with Unknown Hysteresis
Li Tang and Dongjuan Li*
International Journal of Control, Automation, and Systems, vol. 17, no. 7, pp.1642-1654, 2019
Abstract : "This paper deals with the adaptive neural network (NN) control problem for a class of pure-feedback
systems with time-varying constrained states and unknown backlash-like hysteresis. First of all, the considered plant
is transferred into a strict feedback system on account of the implicit function theorem and mean value theorem.
Then, the time-varying Barrier Lyapunov functions (BLFs) are integrated into the backstepping techniques so that
all the states do not transgress the corresponding constraint boundary. This approach avoids the procedure of
finding inverse, and therefore greatly improves the robustness of controller. At the same time, the radial basis
function (RBF) NNs are employed to identify the unknown internal dynamics, which is a key operation in each
step. Based on the Lyapunov stability analysis scheme, all the closed-loop signals are proved to be uniformly
ultimately bounded (UUB), and the tracking error converges to a small neighborhood of the origin. Finally, two
simulation examples are developed to further verify the proposed control strategy"
Adaptive control, backlash-like hysteresis, backstepping method, barrier Lyapunov function, neural networks, state constraints