Passive Performance Level Research Articles

Anti-disturbance observer-based control for fuzzy chaotic semi-Markov jump systems with multiple disturbances and mixed actuator failures

This article develops robust stabilization and anti-disturbance control design for fuzzy chaotic semi-Markov jump systems with randomly occurring uncertainties, nonlinear actuator faults, matched and mismatched disturbances. Primarily, the matched disturbances stimulated from the exogenous systems are dealt with by constructing a disturbance observer. At the same time, mismatched part is handled by the extended passivity performance. Additionally, the actuator fault model which encompasses both linear and nonlinear characteristics is put forward in the controller design. Thus, by taking advantage of the parallel distributed compensation methodology, a composite anti-disturbance control is framed by combining the fuzzy rule-based nonlinear fault-tolerant controller and estimation of the disturbance. Thereafter, by selecting relevant mode-dependent Lyapunov function candidate, adequate criteria are acquired in the context of linear matrix inequalities to ensure robust asymptotic stability along with the endorsed extended passivity performance level for the closed-loop system. In due course, by solving the developed inequalities under the aegis of MATLAB platform, the gain matrices of the devised controller and the framed disturbance observer are obtained. Last of all, to validate the effectiveness and applicability of the theoretical findings, simulation results of standard Chua’s circuit system, Lorenz system and Rössler system are exploited.

Event-triggered [formula omitted]/passive synchronization for Markov jumping reaction–diffusion neural networks under deception attacks

The issue of H∞/passive master–slave synchronization for Markov jumping neural networks with reaction–diffusion terms is investigated in this paper via an event-triggered control scheme under deception attacks. To lighten the burden of limited communication bandwidth as well as ensure the control performance, an event-triggered transmission scheme is developed. Meanwhile, the randomly occurring deception attacks, which received from the event generator are assumed to modify the sign of the control signal, are taken into account. Furthermore, sufficient conditions ensuring the prescribed H∞/passive performance level of the neural networks, are deduced beyond Lyapunov stability theory, and the controller gains are derived dealing with the matrix convex optimization problem. At last, the availability of the approach proposed is demonstrated via a numerical example.

Mixed [formula omitted]/passivity based stability analysis of fractional-order gene regulatory networks with variable delays

In this paper, we derive delay and fractional-order dependent sufficient conditions for fractional-order gene regulatory networks (FOGRNs) with time-varying delays to be stable with H∞/passivity performance. Distinct from the existing works on delayed FOGRNs, the Lyapunov-Krasovskii functional (LKF) is suitably structured so that the delay functions can be non-differentiable or even have jump type discontinuities. Further, the positive-definiteness condition is relaxed for the matrices involved in LKF. A new set of linear matrix inequality (LMI) conditions that ensure the stability of FOGRNs with certain H∞/passivity performance level is derived using various results on fractional derivatives/integrals and convex property of LMIs. Finally, the results obtained are verified with existing FOGRNs via numerical simulations.

Mixed $H_{\infty }$ and Passive Control for Fractional-Order Nonlinear Systems Via LMI Approach

This the first time that the problem of global asymptotic stability analysis, and mixed $H_{\infty }$ and passive control for a class of control fractional-order nonlinear systems has been studied in this paper. By using the Lyapunov direct method and some properties of fractional calculate, we propose sufficient conditions to ensure the unforced system to be asymptotically stable with mixed $H_{\infty }$ and passivity performance level. Further, mixed $H_{\infty }$ and passive control design with an appropriate gain matrix has been derived to achieve the stabilization for fractional-order system with nonlinear perturbations and order $0 < \alpha < 1$ . These conditions are in the form of linear matrix inequalities, which therefore can be efficiently solved by using existing convex algorithms. The effectiveness of our results is illustrated through three numerical examples.

Reliable mixed H ∞ /passive control for T–S fuzzy semi‐Markovian jump systems under different event‐triggered schemes

This study investigates the problem of reliable mixed H ∞ /passive control for Takagi-Sugeno (T-S) fuzzy semi-Markovian jump systems under different event-triggered schemes. An asynchronous fuzzy fault-tolerant controller is designed to deal with the actuator failures, which results in the closed-loop system is stochastically stable with a prescribed mixed H ∞ /passive performance level even if the actuator failures appear. In order to relieve the transmission pressure over the network, the authors utilise different event-triggered schemes and compare their ability of network resource saving. In addition, there is a difference between the system modes and the controller modes to formulate the asynchronisation phenomena. By implementing the delay system method, in combination with the free-weighting matrix and Lyapunov-Krasovskii functional, some conditions on the performance analysis are established to ensure the stochastic stability of the system. Then, controller gain matrices and event-triggered matrices can be obtained simultaneously based on a group of feasible linear matrix inequalities. Finally, simulations are given to show the availability of the proposed method.

Partial-Neurons-Based Passivity-Guaranteed State Estimation for Neural Networks With Randomly Occurring Time Delays.

In this brief, the partial-neurons-based passivity-guaranteed state estimation (SE) problem is examined for a class of discrete-time artificial neural networks with randomly occurring time delays. The measurement outputs available utilized for the SE are allowed to be available only at a fraction of neurons in the networks. A Bernoulli-distributed random variable is employed to characterize the random nature of the occurrence of time delays. By resorting to the Lyapunov-Krasovskii functional method as well as the stochastic analysis technique, sufficient criteria are provided for the existence of the desired state estimators ensuring the estimation error dynamics to achieve the asymptotic stability in the mean square with a guaranteed passivity performance level. In addition, the parameterization of the estimator gain is acquired by solving a convex optimization problem. Finally, the validity of the obtained theoretical results is illustrated via a numerical simulation example.

Mixed H_{infty }/passive exponential function projective synchronization of delayed neural networks with hybrid coupling based on pinning sampled-data control

This paper presents the problem of mixed H_{infty }/passive exponential function projective synchronization of delayed neural networks with constant discrete and distributed delay couplings under pinning sampled-data control scheme. The aim of this work is to focus on designing of pinning sampled-data controller with an explicit expression by which the stable synchronization error system is achieved and a mixed H_{infty }/passive performance level is also reached. Particularly, the control method is designed to determine a set of pinned nodes with fixed coupling matrices and strength values, and to select randomly pinning nodes. To handle the Lyapunov functional, we apply some new techniques and then derive some sufficient conditions for the desired controller existence. Furthermore, numerical examples are given to illustrate the effectiveness of the proposed theoretical results.

Finite‐time boundedness of large‐scale systems with actuator faults and gain fluctuations

SummaryThis paper addresses the issue of finite‐time boundedness of large‐scale interconnected systems with the use of a distributed nonfragile fault‐tolerant controller. The objective of this paper is to design a state‐feedback controller consisting of a time‐varying delay such that the resulting closed‐loop system is finite‐time bounded under a prescribed extended passivity performance level even in the presence of all admissible uncertainties and possible actuator faults. More precisely, based on the Lyapunov‐Krasovskii stability theory, a new set of sufficient conditions is obtained in the framework of linear matrix inequality constraints that ensures finite‐time boundedness and satisfies the prescribed extended passivity performance index of the considered system. Finally, two numerical examples, including the interconnected inverted pendulum, are given to show the effectiveness of the proposed controller design technique.

Projective synchronization for two nonidentical time-delayed fractional-order T–S fuzzy neural networks based on mixed $${H_\infty }$$ H ∞ /passive adaptive sliding mode control

This paper deals with the problem of mixed $${H_\infty }$$ /passive projective synchronization for two different fractional-order (FO) T–S fuzzy neural networks with uncertain parameters and time delays. Firstly, a fractional integral sliding surface which is suitable for the considered FO error system is proposed. Second, in terms of the established sliding surface, combining a novel reaching law, a new adaptive sliding mode control law is introduced, which can force the closed-loop dynamic error system trajectories to reach the sliding surface. Then, by giving a continuous frequency distributed model of the FO dynamic networks and the application of FO system stability theory, the projective synchronization conditions are addressed in terms of linear matrix inequality techniques. Based on the conditions, a desired controller which can guarantee the robust stability of the closed-loop system and also ensure a mixed $${H_\infty }$$ /passive performance level is designed. Finally, synchronization of two nonidentical time-delayed FO T–S fuzzy neural networks with uncertain parameters as a simulation example is given to illustrate the effectiveness of the proposed method.

Mixed H∞ and Passive Projective Synchronization for Fractional Order Memristor-Based Neural Networks with Time-Delay and Parameter Uncertainty**Supported by National Natural Science Foundation of China under Grant Nos. U1604146, U1404610, 61473115, 61203047, Science and Technology Research Project in Henan Province under Grant Nos. 152102210273, 162102410024, and Foundation for the University Technological Innovative Talents of Henan Province under Grant No. 18HASTIT019

This paper investigates the mixed H∞ and passive projective synchronization problem for fractional-order (FO) memristor-based neural networks. Our aim is to design a controller such that, though the unavoidable phenomena of time-delay and parameter uncertainty are fully considered, the resulting closed-loop system is asymptotically stable with a mixed H∞ and passive performance level. By combining active and adaptive control methods, a novel hybrid control strategy is designed, which can guarantee the robust stability of the closed-loop system and also ensure a mixed H∞ and passive performance level. Via the application of FO Lyapunov stability theory, the projective synchronization conditions are addressed in terms of linear matrix inequality techniques. Finally, two simulation examples are given to illustrate the effectiveness of the proposed method.

Mixed and passive filtering for a class of singular systems with interval time‐varying delays

SummaryThis paper deals with the problem of mixed and passive filtering for a class of singular systems with interval time‐varying delays. First, by combining the Wirtinger‐based integral inequality with the reciprocally convex inequality, sufficient delay‐range‐dependent conditions are obtained in terms of strict linear matrix inequalities to ensure that the considered singular system is admissible with a mixed and passivity performance level. Then, based on a matrix transformation technique and employing several free scalars, more flexible strict linear matrix inequality–based conditions are proposed to get the desired filter. Finally, 2 numerical examples are given to show less conservatism and the effectiveness of the results.

Mixed $$H_\infty $$ H ∞ /Passive Projective Synchronization for Nonidentical Uncertain Fractional-Order Neural Networks Based on Adaptive Sliding Mode Control

This paper deals with the problem of mixed $$H_\infty $$ /passive projective synchronization for two different fractional-order (FO) neural networks with uncertain parameters. Firstly, a fractional integral sliding surface which is suitable for the considered FO error system is proposed. Secondly, in terms of the established sliding surface, combining a novel reaching law, a new adaptive sliding mode control law is introduced, which can force the closed-loop dynamic error system trajectories to reach the sliding surface. Then, a continuous frequency distributed model of the FO dynamic networks is given, via the application of FO system stability theory and robust control, the projective synchronization conditions are addressed in terms of linear matrix inequality techniques. Based on the conditions, a desired controller which can guarantee the robust stability of the closed-loop system and also ensure a mixed $$H_\infty $$ /passive performance level is designed. Finally, synchronization of two nonidentical FO neural networks with uncertain parameters as a simulation example is given to illustrate the effectiveness and advantages of the proposed method.

Delay‐dependent fault‐tolerant controller for time‐delay systems with randomly occurring uncertainties

SummaryThis paper addresses the passivity‐based control problem for a class of time‐varying delay systems subject to nonlinear actuator faults and randomly occurring uncertainties via fault‐tolerant controller. More precisely, the uncertainties are described in terms of stochastic variables, which satisfies Bernoulli distribution, and the existence of actuator faults are assumed not only linear but also nonlinear, which is a more general one. The main objective of this paper is to design a state feedback‐reliable controller such that the resulting closed‐loop time‐delay system is stochastically stable under a prescribed mixed and passivity performance level γ&gt;0 in the presence of all admissible uncertainties and actuator faults. Based on Lyapunov stability method and some integral inequality techniques, a new set of sufficient conditions is obtained in terms of linear matrix inequality (LMI) constraints to ensure the asymptotic stability of the considered system. Moreover, the control design parameters can be computed by solving a set of LMI constraints. Finally, two examples including a quarter‐car model are provided to show the efficiency and usefulness of the proposed control scheme. Copyright © 2017 John Wiley &amp; Sons, Ltd.

Mixed H∞ and passive projective synchronization for fractional-order memristor-based neural networks with time delays via adaptive sliding mode control

This paper investigates the mixed [Formula: see text] and passive projective synchronization problem for fractional-order (FO) memristor-based neural networks with time delays. Our aim is to design a controller such that, though the unavoidable phenomena of time delay and external disturbances is fully considered, the resulting closed-loop system is stable with a mixed [Formula: see text] and passive performance level. By combining sliding mode control and adaptive control methods, a novel adaptive sliding mode control strategy is designed for the synchronization of time-delayed FO dynamic networks. Via the application of FO system stability theory, the projective synchronization conditions are addressed in terms of linear matrix inequalities. Based on the conditions, a desired controller which can guarantee the stability of the closed-loop system and also ensure a mixed [Formula: see text] and passive performance level is designed. Finally, two simulation examples are given to illustrate the effectiveness of the proposed method.

Reliable mixed [formula omitted]/passive control for T–S fuzzy delayed systems based on a semi-Markov jump model approach

This paper investigates the problem of the reliable mixed H∞/passive control for Takagi–Sugeno (T–S) fuzzy delayed systems based on a semi-Markov jump model (SMJM) approach. The focus is to design a fuzzy fault-tolerant controller such that the resulting closed-loop system is stochastically stable with a prescribed mixed H∞/passive performance level even if the actuator failures appear. A semi-Markov process is employed to describe the encountered failures of the actuator. By applying the Lyapunov–Krasovskii method, in combination with some novel inequalities, some conditions on the performance analysis are established, where some negative quadratic terms are fully considered to reduce the conservatism. Based on the conditions, an explicit expression for the desired controller is given. Three numerical examples are presented to show the effectiveness and reduced conservatism of the proposed method.

Mixed H∞ and passive control for singular systems with time delay via static output feedback

In this paper, the mixed H∞ and passive control problem is studied for singular systems with time delay. A sufficient condition is proposed to ensure the considered system to be regular, impulse-free and stable with mixed H∞ and passivity performance level. Then the criterion is adopted to design a static output feedback controller in terms of bilinear and linear matrix inequalities. An algorithm is established to cast the nonlinear feasibility problem into a sequential minimization problem. Finally, a numerical example is presented to show the effectiveness of the proposed methods.

Mixed [formula omitted]/passive synchronization for complex dynamical networks with sampled-data control

This paper deals with the problem of mixed H∞/passive synchronization for complex dynamical networks (CDNs) with time-varying delayed couplings via a sampled-data control scheme. The purpose is focus on designing controller such that the resulting synchronization error system is stable and a mixed H∞/passive performance level is satisfied. By using some new tools to deal with the Lyapunov functional, a sufficient condition which ensures the existence of the desired controller is presented. Based on the condition, an explicit expression for the desired controller is given. Finally, two examples are employed to demonstrate the effectiveness and the reduced conservatism of the proposed method.

Reliable Mixed H∞ and Passivity-Based Control for Fuzzy Markovian Switching Systems With Probabilistic Time Delays and Actuator Failures.

This paper is concerned with the problem of reliable mixed H ∞ and passivity-based control for a class of stochastic Takagi-Sugeno (TS) fuzzy systems with Markovian switching and probabilistic time varying delays. Different from the existing works, the H∞ and passivity control problem with probabilistic occurrence of time-varying delays and actuator failures is considered in a unified framework, which is more general in some practical situations. The main aim of this paper is to design a reliable mixed H∞ and passivity-based controller such that the stochastic TS fuzzy system with Markovian switching is stochastically stable with a prescribed mixed H∞ and passivity performance level γ > 0 . Based on the Lyapunov-Krasovskii functional (LKF) involving lower and upper bound of probabilistic time delay and convex combination technique, a new set of delay-dependent sufficient condition in terms of linear matrix inequalities (LMIs) is established for obtaining the required result. Finally, a numerical example based on the modified truck-trailer model is given to demonstrate the effectiveness and applicability of the proposed design techniques.

Mixed H∞ and passivity based state estimation for fuzzy neural networks with Markovian-type estimator gain change

This paper is concerned with the mixed H∞ and passivity based state estimation for a class of discrete-time fuzzy neural networks with the estimator gain change, where a discrete-time homogeneous Markov chain taking value in a finite set Γ={0,1} is introduced to model this phenomenon. Based on the Markovian system approach and linear matrix inequality technique, a new sufficient condition has been derived such that the estimation error system is exponentially stable in the mean square sense and achieves a prescribed mixed H∞ and passivity performance level. The estimator parameter is then determined by solving a set of linear matrix inequalities (LMIs). A numerical example is presented to show the effectiveness of the proposed design method.

Mixed [formula omitted] and passive filtering for singular systems with time delays

In this paper, the problem of mixed H∞ and passive filtering is studied for singular systems with time delays. A sufficient condition is first proposed for the considered systems to be admissible with mixed H∞ and passivity performance level in terms of strict linear matrix inequality (LMI). Then the criterion is adopted to design a mixed H∞ and passive filter of the Luenberger observer type, and a design method is proposed in terms of the solution of a LMI. Finally, the effectiveness of the filter design method is illustrated by a numerical example.