Dynamic Evolution Analysis Of Complex Systems With Parametric Uncertainty

Recently,complex systems have attracted wide attention because of their applications in engineering,physics,biology and so on.Furthermore,model uncertainties are normally encountered in real systems,which may have a negative impact on system performance.Therefore,it is of great significance for investigate the dynamic properties of complex systems with parameter uncertainties.This paper mainly studies several types of complex systems with parameter uncertainties.By utilizing matrix inequality techniques and Lyapunov method as well as combining with nonlinear systems with partially measurable states and exogenous disturbances,switched systems with frequent asynchronism and positive switched neural networks with actuator saturation and sensor faults,respectively,the dynamic characteristics of uncertain complex systems are studied and then some theoretical criteria of evolutionary behavior are obtained.The main contents of this paper are summarized as follows:The problem of input-to-state stabilization for a group of uncertain nonlinear systems equipped with nonabsolutely available states and exogenous disturbances is investigated.To appropriately cope with the partially measurable state variables as well as dramatically minimize controller updating burden and communication costs,an observer-based event-triggered impulsive controller with the combination of sample control is devised.By resorting to the iterative method and Lyapunov technology,some sufficient criteria are established to guarantee the input-to-state stability of the uncertain controlled system under the employed controller,in which an innovative approximation condition as to the uncertain term is proposed and the linear matrix inequality technique is utilized for restraining sophisticated parameter uncertainties.Furthermore,the Zeno behavior in the proposed event-triggered strategy is excluded.The control gains and event-triggered mechanism parameters are conjointly designed by resolving some inequalities of linear matrix.Eventually,the availability of the achieved theoretical works are elucidated by two simulation examples.The stabilization for a group of uncertain switched systems with frequent asynchronism is studied.Without the limitation of minimum residence time,the average dwell-time strategy makes it possible for the system to switch frequently over successive event intervals.Since it is highbrow and expensive to obtain the whole state information in practice,a event-triggered dynamic output-feedback controller is applied.With the aid of a controller-pattern-related Lyapunov functional,sufficient conditions are established to ensure stability of the resulting uncertain closed-loop system.To appropriately deal with the uncertain parameters,some inequalities of linear matrix are tactfully utilized together with the Lyapunov functional is constructed by the strategy of block matrix.Furthermore,the presence of the lower boundary on adjacent event intervals is discussed to eliminate the Zeno behavior.Eventually,the feasibility of the theoretical results are illuminated by a numerical simulation.The robust exponential stabilization of positive uncertain switched neural networks subject to actuator saturation and sensor faults is discussed.To deal with actuator saturation,the convex hull scheme is employed.Consider the existence of interval uncertain parameters and the constraint concerning positivity of the original system,a positive state-bounding observer is constructed to guarantee the coinstantaneous estimation of system state and sensor faults.By designing the state-feedback controller and utilizing the multiple time-varying linear co-positive Lyapunov function,sufficient conditions for the robust exponential stability on the studied system are established under dwell-time switching.Furthermore,for optimizing the observer matrix,an iterative algorithm is developed.In the end,a numerical example is exploited to illuminate the applicability of the proposed approach.