Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Robust output feedback control for nonlinear systems including actuators
Systems Analysis Modelling Simulation
Delay-dependent robust stability and l2-gain analysis of a class of nonlinear time-delay systems
Automatica (Journal of IFAC)
Analysis and synthesis of nonlinear time-delay systems via fuzzy control approach
IEEE Transactions on Fuzzy Systems
Lyapunov-Krasovskii approach to the robust stability analysis of time-delay systems
Automatica (Journal of IFAC)
Adaptive neural control for a class of nonlinearly parametric time-delay systems
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This paper establishes new robust delay-dependent stability and stabilisation methods for a class of nonlinear continuous-time systems with time-varying delays. The nonlinearities are unknown time-varying perturbations satisfying Lipschitz conditions in the state and the delayed-state. An appropriate Lyapunov functional is constructed and delay-dependent stability analysis is performed to characterise Linear Matrix Inequalities (LMIs)-based conditions under which the nominally-linear delay system is robustly asymptotically stable with an γ-level ℒ2-gain. Then we design delay-dependent feedback stabilisation schemes: a static one based on state (delayed-satte)-measurements and a dynamic one based on observer-based output feedback. In all schemes, the closed-loop feedback system enjoys the delay-dependent asymptotic stability with a prescribed γ-level ℒ2-gain. The feedback gains are determined by convex optimisation over LMIs and all the developed results are tested on a representative example.