Robust neural output-feedback stabilization for stochastic nonlinear process with time-varying delay and unknown dead zone

Abstract

This article investigates the output-feedback control of a class of stochastic nonlinear system with time-varying delay and unknown dead zone. A robust neural stabilizing algorithm is proposed by using the circle criterion, the NNs approximation and the MLP (minimum learning parameter) technique. In the scheme, the nonlinear observer is first designed to estimate the unmeasurable states and the assumption "linear growth" of the nonlinear function is released. Furthermore, the uncertainty of the whole system (including the perturbation of time-varying delay) is lumped and compensated by employing one RBF NNs (radial basis function neural networks). Though, only two weight-norm related parameters are required to be updated online for the merit of the MLP technique. And the gain-inversion related adaptive law is targetly designed to mitigate the adverse effect of unknown dead zone. Comparing with the previous work, the proposed algorithm obtains the advantage: a concise form and easy to implementation due to its less computational burden. The theoretical analysis and comparison example demonstrate the substantial effectiveness of the proposed scheme.

Delay compensation for linear systems with both state and distinct input delays

“…Let k 1 be a nonnegative integer and 1 ∈ [0, r] such that k 1 r + 1 = h 1 . According to (14), for all t ≥ h 2 and 1 ∈ [0, r], we have…”

“…Stability analysis and stabilization of time-delay systems are two fundamental problems and many important results have been obtained. For example, stability analysis for linear time-delay systems was investigated in the works of Bekiaris-Liberis and Krstic 4 and Seuret et al, 5 stabilization for linear time-delay systems was studied in the works of Liu and Hu 6 and Zhang et al, 7 the stabilization problem of linear time-invariant systems with distinct input delays was considered in the work of Tsubakino et al, 8 and fixed-time stabilization of linear control systems was investigated in the works of Polyakov et al 9,10 Stability and stabilization problems have also been extensively studied for nonlinear time-delay systems [11][12][13] and stochastic nonlinear time-delay system, 14 and particularly, global output feedback stabilization of nonlinear systems was addressed in the works of Lan and Li 15 and Wang et al 16 There are also many references dealing with various analysis and design problems for time-delay systems, for example, dissipativity analysis, 17,18 sampled-data control, 19 and reliable H ∞ control. 20 It is well known that memory and memoryless controllers are two different approaches for stabilization of input delayed systems.…”

Safety‐constrained robust adaptive control for a class of MIMO non‐linear systems

“…In [8], BLF-based DSC design is enhanced to control a pure-feedback SISO non-linear system, where along with output constraints, time delay and actuator faults are also considered in the control design. Considering time-varying delays and unknown dead zone, a robust output-feedback controller is proposed in [23] for stochastic SISO non-linear system, where NNs approximation and minimum learning parameter technique is used in the control design.…”

Improved evolutionary algorithm and its application in PID controller optimization

“…Unfortunately, the parameter tuning of PID controllers remains a sticking point. For example, properly adjusting the gains of PID controllers is difficult, because the transfer functions of many industrial plants often face various problems such as high-order, time-delay, and nonlinearity [4,5].…”