Adaptive neural DSC for stochastic nonlinear constrained systems under arbitrary switchings

作者：Si, Wenjie^*; Dong, Xunde

来源：Nonlinear Dynamics, 2017, 90(4): 2531-2544.

DOI：10.1007/s11071-017-3821-6

• Adaptive neural control
• Stochastic nonlinear switched systems
• Prescribed performance control (PPC)
• Common Lyapunov function
• Input saturation
• Dynamic surface control (DSC)

This paper proposes an adaptive neural dynamic surface control scheme for a class of strict-feedback stochastic nonlinear systems with guaranteed predefined performance under arbitrary switchings. First, by utilizing the prescribed performance control, the prescribed tracking control performance can be ensured with unknown initial errors, and input constraints are achieved by employing a continuous differentiable asymmetric saturation model. Second, RBF neural networks are used to handle unknown nonlinear functions and stochastic disturbances, and the dynamic surface control technique is used to avoid the problem of 'explosion of complexity' in control design. At last, by combining the common Lyapunov function method with the backstepping design principle, a common adaptive neural controller is constructed. The designed controller overcomes the problem of the over-parameterization and further alleviates the computational burden. Under the proposed common adaptive controller, all the signals in the closed-loop system are four-moment (or two-moment) semi-globally uniformly ultimately bounded, and the prescribed transient and steady tracking control performance are guaranteed under arbitrary switchings. The arbitrary switching behaviors among two and three subsystems are performed to demonstrate and verify the effectiveness of the proposed method.

• 出版日期2017-12
• 单位华南理工大学