Hybrid modified function projective synchronization of two different dimensional complex nonlinear systems with parameters identification

In this article, a novel synchronization scheme is proposed to achieve hybrid modified function projective synchronization (HMFPS) in two different dimensional complex nonlinear systems with fully unknown parameters. In the complex space, the response system are asymptotically synchronized up to the different order’s drive system by the state transformation with a scaling function matrix, and all of unknown parameters in both drive and response systems are achieved to be identified. Based on the Lyapunov stability theory, an adaptive controller and updated laws of parameters are developed. Respectively on the ways of increased order and reduced order, the corresponding numerical simulations demonstrate the effectiveness and feasibility of the proposed scheme.     

Lag projective synchronization of fractional-order delayed chaotic systems

This paper considers the lag projective synchronization of fractional-order delayed chaotic systems. The lag projective synchronization is achieved through the use of comparison principle of linear fractional equation at the presence of time delay. Some sufficient conditions are obtained via a suitable controller. The results show that the slave system can synchronize the past state of the driver up to a scaling factor. Finally, two different structural fractional order delayed chaotic systems are considered in order to examine the effectiveness of the lag projective synchronization. Feasibility of the proposed method is validated through numerical simulations.     

Propagation of projective synchronization in a series connection of chaotic systems

Projective synchronization is a type of chaos synchronization where the response system states are scaled replicas of the drive system states. This paper deals with the propagation of projective synchronization in a series connection of N chaotic discrete-time drive systems and N response systems. By exploiting an observer-based approach, the paper demonstrates that dead-beat projective synchronization (i.e., exact synchronization in finite time for any scaling factor) is achieved between the nth drive and nth response systems. In particular, it is shown that projective synchronization starts from the innermost (Nth) drive-response system pair and propagates toward the outermost (first) drive-response system pair. Only a single scalar synchronizing signal connects the cascaded drive and response systems. Finally, an example illustrates the propagation of different types of chaos synchronization in a series connection consisting of a Gingerbreadman map, a third order hyperchaotic Henon map and a Lozi map.     

Chaotic synchronization between different fractional-order chaotic systems

Based on the idea of tracking control and stability theory of fractional-order systems, a novel synchronization approach for fractional order chaotic systems is proposed. We prove that the synchronization between drive system and response system with different fractional order q can be achieved, and the synchronization between different fractional-order chaotic systems with different fractional order q can be achieved. Two examples are used to illustrate the effectiveness of the proposed synchronization method. Numerical simulations coincide with the theoretical analysis.     

Adaptive synchronization of uncertain chaotic systems with adaptive scaling function

The problem of adaptive synchronization for the uncertain chaotic systems with adaptive scaling function is investigated in this paper. In comparison to those of the existing scaling function synchronization, such as the presetting scaling function, the aim of this paper is focused not only on the scaling function but also on the identification of parameters of the chaotic system. Finally, to illustrate the implementation of the proposed method, some numerical simulations are given.     

Synchronization of chaotic systems with unknown parameters using adaptive passivity-based control

This paper investigates the problem of complete synchronization of chaotic systems with unknown parameters. An adaptive control scheme based on a feedback passivity approach is proposed. The convergence of the synchronization error is guaranteed. The unified chaotic and hyperchaotic Lü systems are taken as illustrative examples. The feasibility and effectiveness of the proposed scheme are demonstrated through numerical simulations.     

Decentralized adaptive synchronization with bounded identification errors for discrete-time nonlinear multi-agent systems with unknown parameters and unknown high-frequency gains

This paper addresses the challenging problem of decentralized adaptive control for a class of coupled hidden leader-follower multi-agent systems, in which each agent is described by a nonlinearly parameterized uncertain model in discrete time and can interact with its neighbors via the history information from its neighbors. One of the agents is a leader, who knows the desired reference trajectory, while other agents cannot receive the desired reference signal or are unaware of existence of the leader. In order to tackle unknown internal parameters and unknown high-frequency gains, a projection-type parameter estimation algorithm is proposed. Based on the certainty equivalence principle and neighborhood history information, the decentralized adaptive control is designed, under which, the boundedness of identification error is guaranteed with the help of the Lyapunov theory. Under some conditions, it is shown that the multi-agent system eventually achieves synchronization in the presence of strong couplings. Finally, a simulation example is given to support the results of the proposed scheme.     

Robust synchronization of a class of chaotic networks

This paper studies synchronization of a dynamical complex network consisting of nodes being generalized Lorenz chaotic systems and connections created with transmitted synchronizing signals. The focus is on the robustness of the network synchronization with respect to its topology. The robustness is analyzed theoretically for the case of two nodes with two-sided (bidirectional) connections, and numerically for various cases with large numbers of nodes. It is shown that, unless a certain minimal coherent topology is present in the network, synchronization is always preserved. While for a minimal network where synchronization is global, the resulting synchrony reduces to semi-global if redundant connections are added.     

Command filtered adaptive neural network synchronization control of fractional-order chaotic systems subject to unknown dead zones

Command filters are essential for alleviating the inherent computational complexity (ICC) of the standard backstepping control method. This paper addresses the synchronization control scheme for an uncertain fractional-order chaotic system (FOCS) subject to unknown dead zone input (DZI) based on a fractional-order command filter (FCF). A virtual control function (VCF) and its fractional-order derivative are approximated by the output of the FCF. In order to handle filtering errors and obtain good control performance, an error compensation mechanism (ECM) is developed. A radial basis function neural network (RBFNN) is introduced to relax the requirement of the uncertain function must be linear in the standard backstepping control method. The construction of a VCF in each step satisfies the Lyapunov function to ensure the stability of the corresponding subsystem. By using the bounded information to cope with the unknown DZI, the stability of the synchronization error system is guaranteed. Finally, simulation results verify the effectiveness of our methods.     

Adaptive anti-lag synchronization of two identical or non-identical hyperchaotic complex nonlinear systems with uncertain parameters

In this paper we present the adaptive anti-lag synchronization (ALS) of two identical or non-identical hyperchaotic complex nonlinear systems with uncertain parameters. The concept of ALS is not detected yet in the literature. Based on the Lyapunov function a scheme is designed to achieve ALS of hyperchaotic attractors of these systems. The ALS of two identical complex Lü systems and two different hyperchaotic complex Lorenz and Lü systems are taken as two examples to verify the feasibility of the presented scheme. These hyperchaotic complex systems appear in several applications in physics, engineering and other applied sciences. Numerical simulations are calculated to demonstrate the effectiveness of the proposed synchronization scheme and verify the theoretical results.     

Finite-time multi-switching synchronization behavior for multiple chaotic systems with network transmission mode

By considering network transmission mode, this paper addresses the finite-time multi-switching synchronization problem for two kinds of multiple chaotic systems. For multiple same-order chaotic systems, we construct the general switching rules and analyze the existence of switching cases. The presented schemes guarantee the states of each derive system to be finite-timely synchronized with the desired states of every respond system in the different transmission paths and switching sequences. For multiple different order chaotic systems, we analyze a special multi-switching hybrid synchronization behavior, where part of the states are completely synchronized and the others belong to combination synchronization. Moveover, the easily verifiable criterion is derived for such synchronization. Finally, numerical examples are given to show the effectiveness of the presented theoretical results.     

Robust event-triggered distributed fusion for multi-sensor systems with unknown failure rates

A robust event-triggered distributed fusion algorithm is investigated in this paper for multi-sensor systems with unknown failure rates. A detection technique based on standard Gaussian distributed filtering innovation is designed and applied to judge whether the measurement is failed. This filtering innovation can also be used to construct the event-triggered condition. Specifically, the event condition is not triggered if the innovation is below the lower event-triggered threshold and the measurement is regarded as the failure measurement if the innovation exceeds the higher threshold. In the above two cases, the sensor measurement data is not transferred to the local estimator; otherwise, it will be transferred. Then, the sequential fast covariance intersection (SFCI) fusion algorithm is used for local estimation fusion. Besides, to analyze the estimation performance, sufficient conditions are given to demonstrate the boundness of the local estimation and fusion estimation covariance. Finally, a simulation example is given to show the usefulness of the presented algorithm.     

Optimal LQG controller for linear stochastic systems with unknown parameters

This paper presents the optimal LQG controller for linear systems with unknown parameters. The optimal controller equations are obtained using the separation principle, whose applicability to the considered problem is substantiated. Performance of the obtained optimal controller is verified in the illustrative example against the conventional LQG controller that is optimal for linear systems with known parameters. Simulation graphs verifying overall performance and computational accuracy of the designed optimal controller are included.     

Distributed MPC-based adaptive control for linear systems with unknown parameters

This paper is concerned with the adaptive control problem for a class of linear discrete-time systems with unknown parameters based on the distributed model predictive control (MPC) method. Instead of using the system state, the state estimate is employed to model the distributed state estimation system. In this way, the system state does not have to be measurable. Furthermore, in order to improve the system performance, both the output error and its estimation are considered. Moreover, a novel Lyapunov functional, comprised of a series of distributed traces of estimation errors and their transposes, has been presented. Then, sufficient conditions are obtained to guarantee the exponential ultimate boundedness of the system as well as the asymptotic stability of the error system by solving a nonlinear programming (NP) problem subject to input constraints. Finally, the simulation examples is given to illustrate the effectiveness and the validity of the proposed technique.     

Sliding mode controller design for linear stochastic systems with unknown parameters

This paper presents the sliding mode mean-square and mean-module controllers for linear stochastic systems with unknown parameters. In both cases, the controller equations are obtained using the separation principle, whose applicability to the considered problem is substantiated. Performance of the obtained controllers is verified in the illustrative example against the sliding mode mean-square and sliding mode controllers that are optimal for linear systems with known parameters. Simulation graphs demonstrating overall performance and computational accuracy of the designed optimal controller are included.     

Robust adaptive visual tracking control for uncertain robotic systems with unknown dead-zone inputs

This paper is concerned with the image-based visual servoing (IBVS) control for uncalibrated camera-robot system with unknown dead-zone constraint, where the uncertain kinematics and dynamics are also considered. The control implementation is achieved by constructing a smooth inverse model for dead-zone-input to eliminate the nonlinear effect resulting from the actuator constraint. A novel adaptive algorithm, which does not require a priori knowledge of the parameter intervals of dead-zone model, is proposed to update the parameter values online, and the dead-zone slopes are not required the same. Furthermore, to accommodate the uncertainties of uncalibrated camera-robot system, adaptation laws are developed to estimate the uncertain parameters, simultaneously avoiding singularity of the image Jacobian matrix. With the full consideration of unknown dead-zone constraint and system uncertainties, an adaptive robust visual tracking control scheme together with dead-zone compensation is subsequently established such that the image tracking error converges to the origin. Based on a 3-DOF manipulator, simulations are conducted to verify the tracking performance of the proposed controller.     

On aperiodic event-triggered master-slave synchronization of chaotic Lur'e systems

This paper is concerned with master-slave synchronization for chaotic Lur'e systems subject to aperiodic sampled-data. To reduce the communication burden, an aperiodic event-triggered (APET) transmission scheme is introduced to determine the transmission of the latest sampling synchronization data. In order to reduce the design conservatism, a novel time-dependent Lyapunov functional (TDLF) is constructed to fully use the characteristics about sampling behavior, triggering error, and nonlinear part of the system, simultaneously. A more relaxed constraint criterion is then presented to ensure the positivity of the whole functional between two sampling instants. By partially resorting to the TDLF, the APET-based synchronization criterion depending on the upper and lower bounds of the uncertain sampling period is presented. The synchronization criterion based on aperiodic-sampling mechanism is also provided. Finally, a typical example about neural networks is offered to illustrate the benefit and validity of obtained synchronization methodologies.     

Complex projective synchronization of complex-valued neural network with structure identification

This paper studies the projective synchronization of neural network in complex-valued domain. Both projective factors and neuron state variables are set as complex values in the synchronization process. In our study, unknown network structure and time-varying delays are considered. With the projective synchronization, the network structure will be identified and the problem of bounded time delays can be solved. With Lyapunov–Krasovskii stability theory and adaptive feedback scheme, controllers are designed and the complex projective synchronization is achieved. In the numerical simulation, several complex-valued neural network examples are provided showing the effectiveness of the theoretical results.     

Robust synchronization of different coupled oscillators: Application to antenna arrays

This paper deals with the synchronization of a chain of nonlinear and uncertain models of nonidentical oscillators. Using Lyapunov's theory of stability, a dynamical controller guaranteeing the synchronization of the oscillators is determined. The problem of synchronization is transformed into a problem of asymptotic stabilization for a nonlinear system and then is formulated as a system of linear matrix inequalities where the parameter variations of the two oscillators and their differences are modeled by polytopic matrices. The theoretical result is successfully applied to an array of transistor-based oscillators used in “smart antenna” systems.     

One input control for exponential synchronization in generalized Lorenz systems with uncertain parameters

By taking account of uncertain slave system parameters, the main goal of this paper is to investigate exponential master–slave synchronization between two nearly identical generalized Lorenz systems via one control input, which including a single state proportional feedback, associated with system parameter estimated laws, which not including states of the master system. Sufficient conditions are provided for the guaranteed exponential stability of both synchronized errors and system parameter errors. Meanwhile, numerical studies are also performed to verify the effectiveness of presented schemes.