Adaptive neural control for stochastic pure-feedback non-linear time-delay systems with output constraint and asymmetric input saturation

作者：Si, Wenjie^*; Dong, Xunde; Yang, Feifei

来源：IET Control Theory and Applications, 2017, 11(14): 2288-2298.

DOI：10.1049/iet-cta.2017.0350

• control system synthesis
• adaptive control
• neurocontrollers
• Lyapunov methods
• delay systems
• delays
• approximation theory
• convergence of numerical methods
• hyperbolic equations
• control nonlinearities
• feedback
• stochastic systems
• nonlinear control systems
• robust control
• robust adaptive neural controller
• stochastic pure-feedback nonlinear time-delay systems
• output constraint
• asymmetric input saturation
• adaptive tracking control
• input saturation nonlinearity
• Gaussian error function

In this study, the adaptive tracking control is investigated for a class of stochastic pure-feedback non-linear time-delay systems with output constraint and asymmetric input saturation non-linearity. First, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation model, and the barrier Lyapunov function is designed to cope with the output constraints. Then, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to address the effects of the unknown time-delay terms, and the neural network is employed to approximate the unknown non-linearities. At last, based on Lyapunov stability theory, a robust adaptive neural controller is proposed, which decreases the number of learning parameters and thus avoids the over-estimation problem. Under the designed neural controller, all the closed-loop signals are guaranteed to be 4-moment (or 2 moment) semi-globally uniformly ultimately bounded and the tracking error converges to a small neighbourhood of the origin for bounded initial conditions. Two simulation examples are presented to further illustrate the effectiveness of the designed method.

• 出版日期2017-9-14
• 单位华南理工大学