This article considers a finite-time control problem of nonlinear quantized systems in complex environments. The controlled system is in a non-strict feedback form. By applying a nonlinear decomposition of hystereticquantizer, the quantization issue is tackled successfully. By employing a structural property of radial basis function(RBF) neural networks (NNs), the conventional backstepping method is extended to non-strict feedback nonlinearquantized systems. Based on the finite time stability criterion, a new adaptive neural control scheme is presented. The constructed neural controller can ensure the transient performance of nonlinear quantized systems.
This article considers a finite-time control problem of nonlinear quantized systems in complex environments. The controlled system is in a non-strict feedback form. By applying a nonlinear decomposition of hystereticquantizer, the quantization issue is tackled successfully. By employing a structural property of radial basis function(RBF) neural networks (NNs), the conventional backstepping method is extended to non-strict feedback nonlinearquantized systems. Based on the finite time stability criterion, a new adaptive neural control scheme is presented. The constructed neural controller can ensure the transient performance of nonlinear quantized systems.