Nonlinear Chaotic Systems Research Articles

Event-triggered finite-time fuzzy control approach for fractional-order nonlinear chaotic systems with input delay

In this paper, a novel fuzzy event-triggered control approach with guaranteed performance of practical finite-time tracking convergence is constructed for uncertain fractional-order nonlinear chaotic systems in the presence of time-varying input delay. Firstly, fuzzy logic systems are employed to deal with immeasurable systematic information. Secondly, fractional-order command filters are incorporated to circumvent the problem of “complexity explosion” in backstepping controller construction. Meanwhile, a sequence of fractional-order finite-time compensation signals are introduced to counteract the adverse influence of time-varying input delay rapidly. Furthermore, a novel practical finite-time chaos control approach based on event-triggered strategy is proposed, which not only facilitates the decrease of sampling frequency and controller burden dynamically, but also ensures the convergence of all tracking errors towards a small region around the origin in finite time. Finally, through the simulation research, the transient response performance and the chaos-suppression ability of fractional-order chaotic systems stabilized via the suggested approach are rigorously testified.

LMI Based Synchronization Control of Nonlinear Affine Fractional Order Chaotic Systems Considering Input Constraint

LMI Based Synchronization Control of Nonlinear Affine Fractional Order Chaotic Systems Considering Input Constraint

A ZNN-based sliding mode control strategy for solving synchronization problems of chaotic systems with application in memristor oscillator

In this article, a fixed-time sliding mode control (FTSMC) strategy is proposed to solve the synchronization problem of a class of generalized nonlinear chaotic systems (CSs). In the process of solving such problems, the following three main works are conducted: 1) an integral sliding manifold based on zeroing neural network is devised to improve the convergence and robustness of CSs; 2) a controller with superior performance is designed so that CSs can be synchronized within a fixed-time, and the upper bound of the convergence time is independent of the initial states; 3) a novel activation function is proposed to weaken the chattering phenomenon. In addition, through rigorous mathematical proof, the upper bounds of fixed-time for tending mode and sliding mode are given. Finally, by means of simulation results, especially in the CS of the memristor oscillator, we demonstrate the excellent convergence and robustness of the FTSMC strategy in dealing with the synchronization problems of the CSs, and the practical value of FTSMC strategy in DNA encoding-based chaotic image encryption is also verified.

Proximal policy optimization‐based controller for chaotic systems

AbstractDeep reinforcement learning (DRL) algorithms are suitable for modeling and controlling complex systems. Methods for controlling chaos, a difficult task, require improvement. In this article, we present a DRL‐based control method that can control a nonlinear chaotic system without any prior knowledge of the system's equations. We use proximal policy optimization (PPO) to train an agent. The environment is a Lorenz chaotic system, and our goal is to stabilize this chaotic system as quickly as possible and minimize the error by adding extra control terms to the chaotic system. Therefore, the reward function accounts for the total triaxial error. The experimental results demonstrated that the trained agent can rapidly suppress chaos in the system, regardless of the system's random initial conditions. A comprehensive comparison of different DRL algorithms indicated that PPO is the most efficient and effective algorithm for controlling the chaotic system. Moreover, different maximum control forces were applied to determine the relationship between the control forces and controller performance. To verify the robustness of the controller, random disturbances were introduced during training and testing, and the empirical results indicated that the agent trained with random noise performed better. The chaotic system has highly nonlinear characteristics and is extremely sensitive to initial conditions, and DRL is suitable for modeling such systems.

Realizing stochastic fixed-time synchronization between two nonlinear chaotic finance systems

The issue of stochastic fixed-time synchronization (SFxS) between two nonlinear chaotic finance systems (CFSs) in noisy environment is addressed by this investigation. Two novel strategies, including the adaptive and fixed-time control, are integrated organically, which steers the CFSs to realize synchronization within a fixed-time, and meanwhile the controlling gains can be updated by the designed adaptive law. Particularly, the designed adaptive fixed-time controlling (AFC) scheme is differentiable, so that the chattering phenomenon can be alleviated and even eliminated effectively. Furthermore, by the stochastic Lyapunov stability analysis, the sufficient criterion is derived for realizing the SFxS of CFSs, and the upper-bound expectation of settling-time (UET) is estimated as well. At last, the numerical experiments are arranged to verify the correctness and feasibility of analytical results, the influence of noisy perturbation on the convergence rate is uncovered, and the interesting similar Dragon–King events are observed.

On the solution of fractional differential equations using Atangana’s beta derivative and its applications in chaotic systems

In this research, we examine the solution of ordinary fractional differential equations using Atangana’s beta derivative. Our approach is divided into two parts. First, we establish conditions under which the fractional differential equation has a unique solution. Next, we develop a numerical technique based on the Adams–Bashforth method and demonstrate its convergence. We then apply the numerical method to a test example to evaluate its efficacy. Finally, we analyze three nonlinear chaotic and hyperchaotic fractional dynamical systems, calculating the strange attractors for various fractional orders. We analyzed the fractional chaotic systems and showed that by changing the order of the fractional derivative, some properties of the system change, such as the area of the chaos attracting region.

Applications of Modified Bessel Polynomials to Solve a Nonlinear Chaotic Fractional-Order System in the Financial Market: Domain-Splitting Collocation Techniques

We propose two accurate and efficient spectral collocation techniques based on a (novel) domain-splitting strategy to handle a nonlinear fractional system consisting of three ODEs arising in financial modeling and with chaotic behavior. One of the major numerical difficulties in designing traditional spectral methods is in the handling of model problems on a long computational domain, which usually yields to loss of accuracy. One remedy is to split the underlying domain and apply the spectral method locally in each subdomain rather than on the global domain of interest. To treat the chaotic financial system numerically, we use the generalized version of modified Bessel polynomials (GMBPs) in the collocation matrix approaches along with the domain-splitting strategy. Whereas the first matrix collocation scheme is directly applied to the financial model problem, the second one is a combination of the quasilinearization method and the direct first numerical matrix method. In the former approach, we arrive at nonlinear algebraic matrix equations while the resulting systems are linear in the latter method and can be solved more efficiently. A convergence theorem related to GMBPs is proved and an upper bound for the error is derived. Several simulation outcomes are provided to show the utility and applicability of the presented matrix collocation procedures.

Robust Adaptive Fuzzy Fractional Control for Nonlinear Chaotic Systems with Uncertainties

The control of nonlinear chaotic systems with uncertainties is a challenging problem that has attracted the attention of researchers in recent years. In this paper, we propose a robust adaptive fuzzy fractional control strategy for stabilizing nonlinear chaotic systems with uncertainties. The proposed strategy combined a fuzzy logic controller with fractional-order calculus to accurately model the system’s behavior and adapt to uncertainties in real-time. The proposed controller was based on a supervised sliding mode controller and an optimal robust adaptive fractional PID controller subjected to fuzzy rules. The stability of the closed-loop system was guaranteed using Lyapunov theory. To evaluate the performance of the proposed controller, we applied it to the Duffing–Holmes oscillator. Simulation results demonstrated that the proposed control method outperformed a recently introduced controller in the literature. The response of the system was significantly improved, highlighting the effectiveness and robustness of the proposed approach. The presented results provide strong evidence of the potential of the proposed strategy in a range of applications involving nonlinear chaotic systems with uncertainties.

Wave turbulence in geophysical flows

Despite elegant theoretical descriptions and numerous potential applications in nature, the various features of wave turbulence and its associated transfers across scales are still debated. One reason is the difficulty, both experimentally and numerically, of studying a chaotic nonlinear wavy system with sufficient precision, over a sufficient duration, and with sufficient separation between the injection scale, the system scale and the dissipation scale, to dwell in detail on the description of a statistically converged state. The numerical study of Thomas & Ding (J. Fluid Mech., 2023, this issue) sheds new light on one key aspect of this global problem: the upscale transfer of waves in rotating shallow water equations, which is of relevance to atmosphere and ocean dynamics. Their results first confirm the theoretical predictions, with a robust inverse wave cascade, a predominant role of quartic resonances and a slope value of the wave spectrum in agreement with the expected range. But their deeper analysis of the results also highlights some weakness in the theoretical foundations, exhibiting strong intermittency and departure from ideal Gaussian statistics. This work thus calls for improved modelling, acknowledging that important large-scale climatic features could actually result from the piling up of small-scale waves, unresolved by typical Earth system models. Beyond rotating shallow water systems, this study more generically resonates with open questions in the field of wave turbulence and its geophysical applications, including recent research in deep rotating and stably stratified systems.

Presentation of the model and optimal control of non-linear fractional-order chaotic system of glucose–insulin

Recent advances in optimal diabetes control have made it possible for diabetic patients to live longer, healthier, and happier lives. In this research, particle swarm optimization and genetic algorithm are applied in order to control the non-linear fractional order chaotic system of glucose–insulin optimally. A fractional system of differential equations discussed the chaotic behavior of the growth of the blood glucose system. Particle swarm optimization and genetic algorithm were used to solve the presented optimal control problem. The results showed that when the controller is applied from the beginning, the results of the genetic algorithm method are excellent. All the results obtained for the particle swarm optimization method show that this method is also very successful and the results are very close to the genetic algorithm method.

Aperiodic Sampled-Data Control for Anti-Synchronization of Chaotic Nonlinear Systems Subject to Input Saturation

This paper studies the aperiodic sampled-data (SD) control anti-synchronization issue of chaotic nonlinear systems under the effects of input saturation. At first, to describe the simultaneous existence of the aperiodic SD pattern and the input saturation, a nonlinear closed-loop system model is established. Then, to make the anti-synchronization analysis, a relaxed sampling-interval-dependent Lyapunov functional (RSIDLF) is constructed for the resulting closed-loop system. Thereinto, the positive definiteness requirement of the RSIDLF is abandoned. Due to the indefiniteness of RSIDLF, the discrete-time Lyapunov method (DTLM) then is used to guarantee the local stability of the trivial solutions of the modeled nonlinear system. Furthermore, two convex optimization schemes are proposed to expand the allowable initial area (AIA) and maximize the upper bound of the sampling period (UBSP). Finally, two examples of nonlinear systems are provided to illustrate the superiority of the RSIDLF method over the previous methods in expanding the AIA and enlarging the UBSP.

Chaotic van der Pol Oscillator Control Algorithm Comparison

The damped van der Pol oscillator is a chaotic non-linear system. Small perturbations in initial conditions may result in wildly different trajectories. Controlling, or forcing, the behavior of a van der Pol oscillator is difficult to achieve through traditional adaptive control methods. Connecting two van der Pol oscillators together where the output of one oscillator, the driver, drives the behavior of its partner, the responder, is a proven technique for controlling the van der Pol oscillator. Deterministic artificial intelligence is a feedforward and feedback control method that leverages the known physics of the van der Pol system to learn optimal system parameters for the forcing function. We assessed the performance of deterministic artificial intelligence employing three different online parameter estimation algorithms. Our evaluation criteria include mean absolute error between the target trajectory and the response oscillator trajectory over time. Two algorithms performed better than the benchmark with necessary discussion of the conditions under which they perform best. Recursive least squares with exponential forgetting had the lowest mean absolute error overall, with a 2.46% reduction in error compared to the baseline, feedforward without deterministic artificial intelligence. While least mean squares with normalized gradient adaptation had worse initial error in the first 10% of the simulation, after that point it exhibited consistently lower error. Over the last 90% of the simulation, deterministic artificial intelligence with least mean squares with normalized gradient adaptation achieved a 48.7% reduction in mean absolute error compared to baseline.

Global Existence and Fixed-Time Synchronization of a Hyperchaotic Financial System Governed by Semi-Linear Parabolic Partial Differential Equations Equipped with the Homogeneous Neumann Boundary Condition

Chaotic nonlinear dynamical systems, in which the generated time series exhibit high entropy values, have been extensively used and play essential roles in tracking accurately the complex fluctuations of the real-world financial markets. We are concerned with a system of semi-linear parabolic partial differential equations supplemented by the homogeneous Neumann boundary condition, which governs a financial system comprising the labor force, the stock, the money, and the production sub-blocks distributed in a certain line segment or planar region. The system derived by removing the terms involved with partial derivatives with respect to space variables from our concerned system was demonstrated to be hyperchaotic. We firstly prove, via Galerkin's method and establishing a priori inequalities, that the initial-boundary value problem for the concerned partial differential equations is globally well posed in Hadamard's sense. Secondly, we design controls for the response system to our concerned financial system, prove under some additional conditions that our concerned system and its controlled response system achieve drive-response fixed-time synchronization, and provide an estimate on the settling time. Several modified energy functionals (i.e., Lyapunov functionals) are constructed to demonstrate the global well-posedness and the fixed-time synchronizability. Finally, we perform several numerical simulations to validate our synchronization theoretical results.

Impulsive Control of Some Types of Nonlinear Systems Using a Set of Uncertain Control Matrices

So many real life problems ranging from medicine, agriculture, biology and finance are modelled by nonlinear systems. In this case, a chaotic nonlinear system is considered and, as opposed to solving Linear Matrix Inequality (LMI), which is the usual approach but cumbersome, a completely different approach was used. In some other cases, the computation of singular value of matrix was used but the method in this study needs not such. In addition, most models, if not all, concentrate on finding a control matrix J under some sufficient conditions. The problem is that only one such matrix J is provided. In reality, the actual control quantity may have a little deviation from the theoretical J. Hence, the study in this paper provides a set of infinite uncertain matrices Jα which are able to adapt to control the system under uncertain conditions. It turns out that this new method controls the system in shorter time with less computational complexities.

An optimal robust fuzzy adaptive integral sliding mode controller based upon a multi-objective grey wolf optimization algorithm for a nonlinear uncertain chaotic system

This paper presents a novel robust fuzzy adaptive integral sliding mode controller designed via a multi-objective grey wolf optimization algorithm for a nonlinear uncertain chaotic system. To this end, proper integral surfaces are defined and a sliding mode stabilizer is designed to converge the system errors to zero. Next, the gradient descent approach is employed to tune the design gains of the integral sliding mode controller. Then, fuzzy rules are employed to regulate the coefficients of the proposed robust control approach. In order to acquire the proper parameters of the proposed controller and avoid trial-and-error processes, a multi-objective grey wolf optimization algorithm is employed to enhance the performance of the proposed controller. The challenging case study of a Duffing-Holmes oscillator, as a nonlinear autonomous uncertain chaotic system without stable equilibrium points, is considered to assess the behavior of the suggests optimal robust fuzzy adaptive integral sliding mode controller. The results of this study are compared with the outcomes of a distinguished work in the literature. Lastly, the discussions elucidate the efficiency of the proposed controller with respect to uncertain chaotic nonlinear systems in terms of optimal control inputs and minimum tracking error.

Chaos control and numerical solution of time-varying fractional Newton-Leipnik system using fractional Atangana-Baleanu derivatives

<abstract><p>Nonlinear fractional differential equations and chaotic systems can be modeled with variable-order differential operators. We propose a generalized numerical scheme to simulate variable-order fractional differential operators. Fractional calculus' fundamental theorem and Lagrange polynomial interpolation are used. Two methods, Atangana-Baleanu-Caputo and Atangana-Seda derivatives, were used to solve a chaotic Newton-Leipnik system problem with fractional operators. Our scheme examined the existence and uniqueness of the solution. We analyze the model qualitatively using its equivalent integral through an iterative convergence sequence. This novel method is illustrated with numerical examples. Simulated and analytical results agree. We contribute to real-world mathematical applications. Finally, we applied a numerical successive approximation method to solve the fractional model.</p></abstract>

Numerical study of a nonlinear fractional chaotic Chua's circuit

<abstract> <p>As an exponentially growing sensitivity to modest perturbations, chaos is pervasive in nature. Chaos is expected to provide a variety of functional purposes in both technological and biological systems. This work applies the time-fractional Caputo and Caputo-Fabrizio fractional derivatives to the Chua type nonlinear chaotic systems. A numerical analysis of the mathematical models is used to compare the chaotic behavior of systems with differential operators of integer order versus systems with fractional differential operators. Even though the chaotic behavior of the classical Chua's circuit has been extensively investigated, our generalization can highlight new aspects of system behavior and the effects of memory on the evolution of the chaotic generalized circuit.</p> </abstract>

Analysis of non-equilibrium 4D dynamical system with fractal fractional Mittag–Leffler kernel

In this article, we presents the theoretical and numerical study of the four dimensional chaotic system which has no equilibrium point in the sense of fractal-fractional Mittag–Leffler kernel. By using the approach of fixed point theory, the existence and uniqueness of solution for the considered model is studied. The approximate solution is acquired by applying the technique of fractional Newton’s polynomial interpolation. The numerical simulations of the approximate results are presented for different fractal dimension and fractional orders. From the figures we obtained the butterfly-type attractor by using different values of fractal dimension which shows symmetric form. Furthermore, fractal and fractional operators show significant impacts on the dynamics of the non-linear chaotic systems.

Temporal Convolutional Networks with RNN approach for chaotic time series prediction

The prediction of chaotic time series, which constitutes many systems in the field of science and engineering, has recently become the focus of attention of researchers. Chaotic time series prediction is making future predictions about these systems using previously observed data for a nonlinear chaotic system with a known initial condition. Chaotic time series prediction can be applied in many fields such as weather forecasting, finance and stock markets. Many disciplines work on solving time series prediction problem, ranging from forecasting weather events days in advance to traders predicting the future of stocks. In recent studies, it has been observed that hybrid deep neural network methods give better performance in solving time series prediction problems and have gained popularity in order to benefit from the advantages of more than one method in solving such problems. In this study, a hybrid deep neural network architecture is proposed for chaotic time series prediction. The used hybrid approach includes both temporal convolutional network to extract low level features from input and recurrent neural network layers such as long short-term memory and gated recurrent units to capture temporal information. Simulations were carried out on nine different chaotic time series dataset which are obtained from Lorenz, Rössler and a Lorenz-like chaotic equation sets, and twenty-one electrocardiogram (ECG) recordings of patients with arrhythmias. In the benchmark study, in which twelve different methods, including classical machine learning, deep neural network and hybrid models were used, the proposed model achieved the best prediction performance with an average root-mean-square error (RMSE) value of 0.0022 for chaotic dataset and 0.0082 for ECG arrhythmia dataset. Performance evaluation metrics show that the proposed hybrid architecture can compete with the models in state-of-the-art studies in chaotic time series prediction.

Using particle swarm optimization and genetic algorithms for optimal control of non-linear fractional-order chaotic system of cancer cells

The purpose of this paper is to investigate optimal control the non-linear fractional-order chaotic system of cancer cells by use of particle swarm optimization and genetic algorithms. The chaotic behavior of cancer cell growth and the tumor growth model were expressed as a system of differential equations. Then, optimal control of cancer cells using drugs was presented. Particle swarm optimization method and genetic algorithms were used to solve the problem of optimal control of cancer cell growth. The results of the control applied to the model can control the cancer cell growth. The application of control results in decreasing the number of cancer cells to zero. When the controller is applied from the beginning, the Results of genetic algorithm method are excellent. All the results obtained for the particle swarm optimization method show that this method has also been very successful and have results very close to the genetic algorithm method.