Anti-synchronization Control Research Articles

The design, circuit realization and applications of chaotic system with offset-boosting and multistability

Based on the foundation of a three-dimensional Jerk chaotic system, a novel four-dimensional chaotic system based on a memristor with cosine magneto-memristor is proposed. The system’s dynamical behavior is analyzed using bifurcation diagrams, Lyapunov exponents, multiple bifurcation diagrams, two-parameter Lyapunov exponents, complexity, and attractor basins. It is found that this new chaotic system exhibits characteristics such as multistability, transient chaos, intermittent chaos, spike discharge, controllable multi-vortex, and offset-boosting. The chaotic system is designed and simulated in analog circuit and realized in a digital circuit using a Field-Programmable Gate Array (FPGA). The consistency between simulation results and implementation results demonstrates the existence and feasibility of the system. Finally, the significance and superiority of the control scheme are verified based on the adaptive anti-synchronization control strategy, and the synchronization strategy is applied to image encryption, and the results show that the encryption effect is excellent and the anti-jamming ability is strong.

Secure adaptive event-triggered anti-synchronization for BAM neural networks with energy-limited DoS attacks

This article focuses on the problem of adaptive event-triggered anti-synchronization control for bidirectional associative memory neural networks subject to energy-limited denial of service attacks. First, a novel adaptive event-triggered scheme is developed by resorting to the acknowledgment character technique, which can help conserve valuable communication resources and has better performance in resisting malicious cyber attacks compared to traditional schemes. Second, a more general attack strategy for denial of service attacks is proposed with the consideration of energy constraints, and an anti-synchronization error system is established to analyze the anti-synchronization behavior. Then, sufficient conditions are provided to guarantee the anti-synchronization of drive and response bidirectional associative memory neural networks in H∞ sense. Next, a design approach is obtained based on the above conditions for the controller gains. Finally, a numerical example is employed to demonstrate the effectiveness of the proposed method and its superiority over the traditional event-triggered scheme.

Chaotic control problem of BEC system based on Hartree–Fock mean field theory

Abstract Due to the difficulty of studying nonlinear quantum systems and the unique composition of Bose–Einstein condensate (BEC) systems, BECs face significant difficulties in solving dynamic analysis and chaotic control problems. Therefore, Hartree–Fock mean field theory is introduced to study the chaotic characteristics, control, and synchronization issues of BEC systems loaded on optical lattices. First, the stability and chaos of BECs in optical lattices were analyzed. Subsequently, constant shift method and activation control were introduced based on the Gross–Pitaevskii equation to achieve control and synchronization of the BEC system. Second, based on the Lyapunov exponent theory, offset parameters are added to BEC chaotic control to achieve control of particle density. Finally, based on the stability theory of linear systems, a control term is introduced to achieve variable analysis of the system’s drive–response system, ensuring that chaotic systems with different initial conditions can still achieve good synchronization and anti-synchronization control. The chaotic problem of BEC system was analyzed using numerical and theoretical methods in the experiment. The effect of adjusting the parameters of the BEC system under the constant shift method is significant. The system exhibits a chaotic state under the Lyapunov exponent, which is mainly concentrated between [3.4, 4.5], demonstrating good system stability. When the offset constant range is [4.21, 5.67], the maximum Lyapunov exponent value is below 0. In the problem of chaotic synchronization, adding activation control causes the system’s time series to exhibit anti-synchronization with spatiotemporal variable variation, while adding control terms leads the system to tend towards synchronization and anti-synchronization with time evolution. The analysis of chaotic control problems in BEC systems can provide reference value and theoretical basis for the dynamic research of quantum physics and related nonlinear systems.

Multiple Noncooperative Targets Encirclement by Relative Distance-Based Positioning and Neural Antisynchronization Control

From prehistoric encirclement for hunting to GPS orbiting the earth for positioning, target encirclement has numerous real world applications. However, encircling multiple non-cooperative targets in GPS-denied environments remains challenging. In this work, multiple targets encirclement by using a minimum of two tasking agents, is considered where the relative distance measurements between the agents and the targets can be obtained by using onboard sensors. Based on the measurements, the center of all the targets is estimated directly by a fuzzy wavelet neural network (FWNN) and the least squares fit method. Then, a new distributed anti-synchronization controller (DASC) is designed so that the two tasking agents are able to encircle all targets while staying opposite to each other. In particular, the radius of the desired encirclement trajectory can be dynamically determined to avoid potential collisions between the two agents and all targets. Based on the Lyapunov stability analysis method, the convergence proofs of the neural network prediction error, the target-center position estimation error, and the controller error are addressed respectively. Finally, both numerical simulations and UAV flight experiments are conducted to demonstrate the validity of the encirclement algorithms. The flight tests recorded video and other simulation results can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/B8uTorBNrl4</uri>

Moving-Target Circumnavigation Using Adaptive Neural Anti-Synchronization Control via Distance-Only Measurements.

In this work, we investigate the unknown moving-target circumnavigation problem in GPS-denied environments. A minimum of two tasking agents is excepted to circumnavigate the target cooperatively and symmetrically without prior knowledge of its position and velocity in order to achieve optimal sensor coverage persistently for the target. To achieve this goal, we develop a novel adaptive neural anti-synchronization (AS) controller. Based on relative distance-only measurements between the target and two tasking agents, a neural network is used to approximate the displacement of the target such that the position of the target can be estimated accurately and in real time. On this basis, a target position estimator is designed by considering whether all agents are in the same coordinate system. Furthermore, an exponential forgetting factor and a new information utilization factor are introduced to improve the accuracy of the aforementioned estimator. Rigorous convergence analysis of position estimation errors and AS error shows that the closed-loop system is globally exponentially bounded by the designed estimator and controller. Both numerical and simulation experiments are conducted to demonstrate the correctness and effectiveness of the proposed method.

Fixed-Time Anti-Synchronization of Unified Chaotic Systems via Adaptive Backstepping Approach

In this brief, the fixed-time anti-synchronization control problem is considered for two unified chaotic systems with uncertain parameters. In the process of controller design, adaptive technique is applied to approximate the uncertain constants occurred in the original systems and the piecewise functional approach is introduced to overcome the singularity problem appeared in the virtual control signals. Based on the Lyapunov theory and fixed-time control method, an adaptive fixed-time control scheme is designed. The designed controller achieves the anti-synchronization of two unified chaotic systems in a fixed-time. Theoretical analysis and simulation results demonstrate that the effectiveness of the control method.

Optimal antisynchronization control for unknown multiagent systems with deep deterministic policy gradient approach

The topic of optimal antisynchronization control for unknown multiagent systems (MASs) with cooperative-competitive (i.e.,coopetition) interaction is challenging because of the complex connection characteristics and the coupling among agents. This paper proposes a reinforcement learning Algorithm based on coopetition strength (CS) to achieve optimal antisynchronization in unknown MASs. Firstly, an innovative CS function is explored, and the local state error information of the agents can be redefined. Furthermore, we propose a novel strategy iteration method to approximate the agent’s optimal control policy. Meanwhile, the proposed algorithm’s convergence analysis is presented, which is based on the Lyapunov stability and functional theorem. In the implementation of a data-based control policy, a network structure of actor-critic (AC) is designed. To improve the robustness of the control policy, the target network and experience replay (ER) are introduced in the training process. Finally, the algorithm’s effectiveness is validated using comparable numerical simulations.

Chaotic attitude synchronization and anti-synchronization of master-slave satellites using a robust fixed-time adaptive controller

It is well known that the satellite attitude motion exhibits the chaotic phenomenon. This article addresses the challenging problem of chaotic attitude synchronization and anti-synchronization for master-slave satellites under unknown moments of inertia and disturbance torques. First, a fixed-time adaptive synchronization controller is designed by combining the fixed-time control technique and adaptive control technique. The parametric adaptation laws are adopted to identify the unknown parameters in the synchronization error system. Benefiting from the adaptive identifications, the proposed controller is highly robust to unknown moments of inertia and disturbance torques. The practical fixed-time stability of the resulting closed-loop system is strictly achieved. The proposed controller can guarantee all error variables in the closed-loop system regulate to the small residual sets around zero in fixed time. Then, a fixed-time adaptive anti-synchronization controller is developed in a similar way. Finally, simulations studies are conducted to demonstrate the effectiveness and excellent control performance of the proposed controllers.

Anti-Synchronization of Delayed State-Based Switched Inertial Neural Networks.

In this article, global anti-synchronization control for a class of state-based switched inertial neural networks (SBSINNs) with time-varying delays is considered. Based on the hybrid control strategies and Lyapunov stability theory, several criteria are obtained to ensure global anti-synchronization of the underlying SBSINNs. Furthermore, we consider the global asymptotic anti-synchronization directly from the SBSINNs themselves with a nonreduced-order method. Finally, a numerical simulation is given to illustrate the effectiveness of the results.

Input–Output Data-Based Output Antisynchronization Control of Multiagent Systems Using Reinforcement Learning Approach

This article investigates an output antisynchronization problem of multiagent systems by using an input-output data-based reinforcement learning approach. Till now, most of the existing results on antisynchronization problems required full-state information and exact system dynamics in the controller design, which is always invalid in practical scenarios. To address this issue, a new system representation is constructed by using just the available input/output data from the multiagent system. Then, a novel value iteration algorithm is proposed to compute the optimal control laws for the agents; moreover, a convergence analysis is presented for the proposed algorithm. In the implementation of the data-based controllers, an actor-critic network structure is established to learn the optimal control laws without the requirement of information of the agent dynamics. An incremental weight updating rule is proposed to improve the learning performance. Finally, simulation results are presented to demonstrate the effectiveness of the proposed antisynchronization control strategy.

Anti-synchronization for complex-valued neural networks with leakage delay and time-varying delays

This paper discusses the anti-synchronization control of complex-valued neural networks(CVNNs) with leakage delay and time-varying delays. Based on different types of activation functions meeting different assumption conditions, via some inequalities techniques and the Lyapunov approach, some sufficient conditions are acquired of the anti-synchronization of CVNNs by choosing the suitable controllers. Finally, two examples are presented to validate the effectiveness of the obtained criteria.

Robust adaptive anti-synchronization control of multiple uncertain chaotic systems of different orders

The precise anti-synchronization control of uncertain chaotic systems has always remained an interesting problem. The anti-synchronization control of multiple different orders uncertain chaotic systems increases the complexity and enhances the security of the information signal in secure communications. Hence, it confines the hacking in digital communication systems. This paper proposes a novel adaptive control technique and studies the double combination anti-synchronization of multiple different orders uncertain chaotic systems. The proposed adaptive feedback control technique consists of three fundamental nonlinear components. Each component accomplishes a different objective; (i) stability of the closed-loop, (ii) smooth and fast convergence behaviour of the anti-synchronization error, and (iii) disturbance rejection. The theoretical analysis in (i) to (iii) uses the Lyapunov stability theory. This paper also provides parameters adaptation laws that stabilize the uncertain parameters to some constants. The paper discusses the simulation results of two representative examples of four different orders uncertain chaotic systems. These examples demonstrate anti-synchronization among hyperchaotic Lü, uncertain chaotic Shimizu Morioka, uncertain second-order nonlinear duffing, and uncertain parametrically excited second-order nonlinear pendulum systems. The computer-based simulation results certify the efficiency and performance of the proposed anti-synchronization control approach and compare them with peer works.

Fault-tolerant anti-synchronization control for chaotic switched neural networks with time delay and reaction diffusion

<p style='text-indent:20px;'>This paper is concerned with the issue of fault-tolerant anti-synchro-nization control for chaotic switched neural networks with time delay and reaction-diffusion terms under the drive-response scheme, where the response system is assumed to be disturbed by stochastic noise. Both arbitrary switching signal and average dwell-time limited switching signal are taken into account. With the aid of the Lyapunov-Krasovskii functional approach and combining with the generalized Itô formula, sufficient conditions on the mean-square exponential stability for the anti-synchronization error system are presented. Then, by utilizing some decoupling methods, constructive design strategies on the desired fault-tolerant anti-synchronization controller are proposed. Finally, an example is given to demonstrate the effectiveness of our design strategies.

Resilient fault-tolerant anti-synchronization for stochastic delayed reaction–diffusion neural networks with semi-Markov jump parameters

This paper deals with the anti-synchronization issue for stochastic delayed reaction–diffusion neural networks subject to semi-Markov jump parameters. A resilient fault-tolerant controller is utilized to ensure the anti-synchronization in the presence of actuator failures as well as gain perturbations, simultaneously. Firstly, by means of the Lyapunov functional and stochastic analysis methods, a mean-square exponential stability criterion is derived for the resulting error system. It is shown the obtained criterion improves a previously reported result. Then, based on the present analysis result and using several decoupling techniques, a strategy for designing the desired resilient fault-tolerant controller is proposed. At last, two numerical examples are given to illustrate the superiority of the present stability analysis method and the applicability of the proposed resilient fault-tolerant anti-synchronization control strategy, respectively.

Mixed ℋ∞ and ℒ2 — ℒ∞ Anti-synchronization Control for Chaotic Delayed Recurrent Neural Networks

This paper deals with the issue of mixed ℋ∞ and ℒ2 — ℒ∞ anti-synchronization control for chaotic delayed recurrent neural networks with unknown parameters and stochastic noise. By means of the Lyapunov- Krasovskii functional method and some stochastic analysis techniques, an adaptive controller strategy is proposed to guarantee the mixed ℋ∞ and ℒ2 — ℒ∞ anti-synchronization of the drive and response systems. When there is no stochastic noise, it is shown that the present control strategy is less conservative and less complex than a previously reported adaptive control method. Finally, a numerical example is employed to illustrate the applicability of the proposed adaptive control strategy.

Multi-Switching Combination Anti-synchronization of Unknown Hyperchaotic Systems

The security of the information signal in digital communication systems is an active research problem. This paper proposes a new robust adaptive anti-synchronization control (RAASC) technique and studies the multi-switching combination anti-synchronization (MSCAS) of different unknown hyperchaotic (UH) systems. This controller reduces the overshoots in the transient phase and establishes the anti-synchronization convergence quickly. The proposed MSCAS scheme anti-synchronizes two master UH systems with a slave UH system using the multi-switching mechanism that increases the complexity and enhances the security of the information signal in digital communications. The Lyapunov direct method assures the robust stability of the closed-loop dynamical system. This work provides adaptive laws that estimate the true values of unknown parameters. A numerical example of three different hyperchaotic Pang, Lu and Lorenz–Stenflo systems is simulated. The simulation results verify the theoretical findings. The proposed MSCAS approach is used for the secret communication of digital messages using the chaotic masking technique. The receiver retrieves the information signal with high security and good precession qualitatively, under the effect of different types of disturbances. This paper also discusses the implications of controller constituents of the proposed RAASC algorithm that establishes fast convergence. The article suggests some future research problems related to this work.

Impulsive anti-synchronization control for fractional-order chaotic circuit with memristor

This paper investigates the anti-synchronization of fractional-order memristive chaotic circuits (FMCC) with time delay via an impulsive control scheme. Based on the Mittag-Leffler function, the impulsive control principle and the Lyapunov stability theory, several criteria are adopted to derive the impulsive anti-synchronization of FMCC with time delay. Finally, numerical examples are exploited to verify the effectiveness of the theoretical analysis, and some discussions about the stable region are given.

Anti-synchronization control of BAM memristive neural networks with multiple proportional delays and stochastic perturbations

Proportional delay is a class of unbounded time-varying delay. A class of bidirectional associative memory (BAM) memristive neural networks with multiple proportional delays is concerned in this paper. First, we propose the model of BAM memristive neural networks with multiple proportional delays and stochastic perturbations. Furthermore, by choosing suitable nonlinear variable transformations, the BAM memristive neural networks with multiple proportional delays can be transformed into the BAM memristive neural networks with constant delays. Based on the drive-response system concept, differential inclusions theory and Lyapunov stability theory, some anti-synchronization criteria are obtained. Finally, the effectiveness of proposed criteria are demonstrated through numerical examples.

Exponential Antisynchronization Control of Stochastic Memristive Neural Networks with Mixed Time-Varying Delays Based on Novel Delay-Dependent or Delay-Independent Adaptive Controller

The global exponential antisynchronization in mean square of memristive neural networks with stochastic perturbation and mixed time-varying delays is studied in this paper. Then, two kinds of novel delay-dependent and delay-independent adaptive controllers are designed. With the ability of adapting to environment changes, the proposed controllers can modify their behaviors to achieve the best performance. In particular, on the basis of the differential inclusions theory, inequality theory, and stochastic analysis techniques, several sufficient conditions are obtained to guarantee the exponential antisynchronization between the drive system and response system. Furthermore, two numerical simulation examples are provided to the validity of the derived criteria.

Anti-synchronization control for delayed memristor-based distributed parameter NNs with mixed boundary conditions

This paper addresses the anti-synchronization control problem of memristor-based distributed parameter neural networks (MDPNNs) with time-varying delays. An intermittent sampled-data controller is proposed such that the response system can be anti-synchronized with the drive system by using Lyapunov stability theory. The design approach of the desired intermittent sampled-data controller is first proposed. Based on a differential inequality and linear matrix inequality (LMI) technology, novel sufficient conditions for the exponential anti-synchronization are obtained in terms of line matrix inequalities. Numerical simulations are shown to demonstrate the effectiveness of the theoretical results.