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To investigate the impact of non-linear interactions on dynamic coarse graining, we study a simplified model system featuring a tracer particle in a complex environment. Using a projection operator formalism and computer simulations, we systematically derive generalized Langevin equations (GLEs) describing the dynamics of this particle. We compare different kinds of linear and non-linear coarse-graining procedures to understand how non-linearities enter reconstructed GLEs and how they influence the coarse-grained dynamics. For non-linear external potentials, we show analytically and numerically that the non-Gaussian parameter and the incoherent intermediate scattering function will not be correctly reproduced by the GLE if a linear projection is applied. This, however, can be overcome by using non-linear projection operators. We also study anharmonic coupling between the tracer and the environment and demonstrate that the reconstructed memory kernel develops an additional trap-dependent contribution. Our study highlights some open challenges and possible solutions in dynamic coarse graining. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the state-feedback control for nonlinear systems with a prescribed upper bound of the settling time. First, a prescribed-time stability theorem using Lyapunov analysis and a uniformly bounded scaling function is proposed. Then, based on this theorem and the backstepping technique, a finite-time control procedure is provided for the uncertain nonlinear systems with unmatched nonlinear terms. Based on the design procedure, a state-feedback controller is obtained, which can render the system states exactly convergent to zero in a prescribed time and maintain at zero thereafter. Finally, simulation examples are used to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we present an innovative approach for assessing the stability of linear time-invariant (LTI) systems. The key innovations in our methodology include the calculation of Markov parameters, the extension to nonlinear continuous-time systems, and significantly improved computational efficiency. Our novel approach leverages Markov parameters derived from the characteristic polynomial of the system. Unlike traditional methods, we employ the Leverrier-Takeno algorithm to efficiently determine the coefficients of the characteristic polynomial for state-space representation. The systematic calculation of Markov parameters using a specialized Maclaurin series expansion enhances our method's precision. One of our major achievements is the rigorous verification of stability through Hankel matrices, ensuring positive definiteness using the Cholesky decomposition algorithm. In addition, we introduce a second stability criterion based on iterated polynomial long divisions, although we acknowledge its limitations in handling high-order systems due to computational inefficiency. Our pioneering contributions extend beyond LTI systems. We adapt our innovative techniques to analyze the local stability of nonlinear continuous-time systems by introducing the concept of Jacobian matrices and employing the indirect Lyapunov method. This expansion broadens the applicability of our approach to a wider range of real-world systems. Notably, our approach exhibits remarkable computational efficiency. According to CPU-time analysis, our first stability criterion outperforms the classical Routh–Hurwitz method by more than 12 times for dynamic systems with orders ranging from 2 to 180. This significant reduction in computation time positions our method as a valuable option for real-time stability analysis applications. [ABSTRACT FROM AUTHOR]

In this paper, a fixed-time convergence scheme for fractional sliding modes is proposed for nonlinear second-order systems. Firstly, a fixed-time fractional-order sliding mode surface is designed by combining the fixed-time theory with fractional-order sliding mode control, which has a faster convergence rate and less chattering. Secondly, the proposed sliding mode controller is applied to a class of second-order nonlinear systems subject to uncertainties and external perturbations to ensure that the system is globally robust fixed-time stable. Then, a continuous fractional order approach law is designed and the proposed sliding mode controller is shown to converge in fixed time by Lyapunov function and the convergence time is related to the choice of controller parameters. Finally, the fixed-time fractional-order sliding mode control strategy is applied to a second-order nonlinear magnetic levitation system system, and the simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a new fuzzy Functional observer is developed for nonlinear neutral systems. Also, the existence conditions for it are studied. The delay-dependent stability of this observer is guaranteed by the combination of the solution of the Sylvester equation and the Lyapunov–Krasovskii stability approach. The parameters of the studied observer are obtained by solving linear matrix inequalities (LMIs). The performance of the approach developed in this paper is demonstrated at the end of the paper by numerical examples. [ABSTRACT FROM AUTHOR]

In this article, an event-triggered finite-time neural control strategy is proposed for nonlinear power systems with unknown disturbances and static var compensator (SVC). We first transform the power system with SVC into a three-dimensional uncertain nonlinear system and then extend it to an n -dimensional uncertain nonlinear system. The disturbance observer is established to estimate external disturbances and the unknown nonlinear terms are approximated by the radial basis function neural networks. Moreover, to avoid the complexity explosion problem in the traditional backstepping method, the command filtering technique is adopted, and the error caused by the command filters is compensated. The adaptive event-triggered finite-time controller ensures that all signals are bounded in finite time and excludes Zeno phenomena. In the end, the simulation for the two-area interconnected power system with SVC is presented to verify the availability and feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive controller for a class of uncertain nonlinear systems is presented using a combination of Legendre polynomials and brain emotional learning-based intelligent controller (BELBIC). Recently, some versions of BELBIC have been presented with the aim of satisfying the universal approximation property using Gaussian basis function. However, the size of regressor vector is too large that imposes a heavy computational load to the processor. The novelty of this paper is presenting a new version of BELBIC with less computational burden using Legendre polynomials. Moreover, there are very few tuning parameters in Legendre polynomials. Another contribution of this paper is editing the stability analysis presented in recent related works. Due to the intrinsic non-differentiability of the adaptation rules of BELBIC, the second time derivative of Lyapunov function is undefined and thus, the Barbalat's lemma cannot be applied to verify the asymptotic convergence of the error function. Therefore, bounded-input-bounded-output (BIBO) stability can only be claimed for this controller. Simulation results on different case studies show that Legendre polynomials can improve the universal approximation property of BELBIC with less tuning parameters. Moreover, in the absence of the robust control term in the control law, the performance Legendre polynomials will not deteriorate, while the performance degrade in Gaussian basis function is quite considerable. [ABSTRACT FROM AUTHOR]

A current challenge with adaptive controllers is to define efficient tuning methods of the controller parameters. Unlike linear systems, nonlinear systems may need parameters that are continuously tuned at different operating points to provide stability and desired behaviours. This study aims to develop a solution for tuning proportional–integral–derivative (PID) controller parameters as opposed to changing the operating points of a nonlinear system. Most tuning methods calculate parameters according to the system's step or frequency response. However, adaptive controllers have self-tuneable parameters or control rules. The proposed algorithm in this paper contains a controller, an estimator, and a reference model, and uses the system model. Unlike the model reference adaptive control method, the proposed controller has tuneable controller parameters estimated by the extended Kalman–Bucy filter. The filter estimates the controller parameters to make the system perform like the auxiliary ideal reference model to ensure minimum-time consumption. Hence, this study aims to develop an algorithm that will automatically calculate controller parameters for each operating point of the controlled chaotic or nonlinear system to minimize settling time at each operating point. The proposed algorithm is implemented in a unified chaotic system in which the estimator and controller of the system run together. Simulation results confirm the performance of the proposed algorithm. In addition, the simulation results provide strong evidence that the proposed algorithm can be an effective tool for controlling nonlinear or chaotic systems. [ABSTRACT FROM AUTHOR]

In this article, the guaranteed cost control problem for a class of nonlinear networked control systems (NCSs) with dynamic quantization, external bounded disturbance, and data dropout is investigated via the dynamic output feedback. The Takagi–Sugeno (T-S) fuzzy model is used to represent the discrete-time nonlinear system discussed in this paper. Under the dynamic quantization strategy, two quantizers with dynamic parameters are used to quantize the measured output and control input, and the data dropout process is described by introducing the Bernoulli stochastic variable. By using the quadratic boundedness technique, the quadratic stability of the quantized NCS with external disturbance is described. Furthermore, the sufficient condition for the global design approach are proposed, that is, not only the dynamic output feedback controller but parameters of the quantizers are designed synchronously with strict linear matrix inequalities. Eventually, a simulation of nonlinear mass-spring-damper mechanical system is carried out to verify the effectiveness of the provided algorithm. [ABSTRACT FROM AUTHOR]

This article introduces a measure of noncompactness in the generalized Morrey space. We study the applications of our new definition in investigating the conditions for the existence of solutions for systems of nonlinear integral equations. We can extend many useful theorems in L p (R N) for functions belonging to the generalized Morrey spaces. Compared to the L p (R N) spaces, the advantage of studying in the Morrey spaces is that we can research no compact support functions in our problems. Finally, significant examples are presented to show the efficiency of the main results. [ABSTRACT FROM AUTHOR]

This article addresses the identification problem of the input nonlinear closed-loop system with unknown time delay (INCTD). An identification algorithm of the INCTD system which combines the wavelet packet decomposition (WPD), the variance Gaussian kernel function (VGKF) and the grouped method of data handling (GMDH) network is proposed. The data collected by the sensor is decomposed into intrinsic mode function with different nonlinear characteristics using WPD. The GMDH network is applied to mine bidirectional strong nonlinear relationship between input and output data. The VGKF criterion selects the optimal complexity model to enhance the extrapolation performance, and further improves the identification accuracy. Thus, the WPD-GMDH-VGKF identification algorithm is derived. The numerical simulation result verifies that the WPD-GMDH-VGKF identification method can effectively identify the INCTD system, and it is superior to GMDH and WPD-GMDH in terms of identification accuracy. The application case verifies the feasibility of applying the WPD-GMDH-VGKF identification method to the battery management system (BMS). [ABSTRACT FROM AUTHOR]

Aiming to the sudden change of the system actual output constraint, a novel safe tracking control strategy is investigated for a class of non-strict feedback nonlinear systems with unknown initial tracking condition in this paper. A secure boundary protection method (SBPM) based on prescribed performance and a new prescribed finite-time performance function (PFTPF) are proposed. The SBPM can automatically construct two adjustable secure boundaries, so that the system output meets both the control performance prescribed by the PFTPF and the secure boundary constraint. In order to solve the problem of the excessive control input jitter caused by the sudden change of output constraint, a bidirectional filtering smoothing mechanism (BFSM) is proposed. The proposed SBPM can effectively ensure the safe tracking control of the system with unknown initial tracking condition when the actual output constraint changes suddenly, and ensure that the system has the prescribed finite-time control performance given by the PFTPF. Differently from the existing works, the system output will be strictly constrained into the actual output constraint without the prediction for the system output value. Finally, the effectiveness of this proposed method is verified by simulations. [ABSTRACT FROM AUTHOR]

In this technical note, the problem of the extended dissipative control for interval type-2 fuzzy model with nonlinear network systems in the event-triggered framework is investigated. A novel dynamic event-triggered strategy is developed to ascertain the instants of activation. It has the advantage of reducing data transmission and optimising the utilisation of communication resources. On the basis of the proposed mechanism, the stochastic Lyapunov function including internal dynamic variables is constructed. Based on this function, coupled with the inequality scaling technique and the application of Dynkin's formula, we derive a sufficient condition for the system's stochastic asymptotic stability and extended dissipativity. The design of the dynamic event-triggered extended dissipative controller becomes challenging because the new sufficient condition is nonlinear for some matrix variables. However, utilising the Projection Lemma and the co-design approach, such a controller can be developed based on the obtained analysis result. The methodology is demonstrated through a rigorous numerical simulation case study. [ABSTRACT FROM AUTHOR]

This paper studies the global regulation problem by a non-predictor event-triggered controller for high-order feedforward nonlinear systems with unknown time-varying delays in the states and input. We propose a non-predictor-based event-triggered controller with a dynamic gain-scaling factor, in which any information on the delays is not required in controller design. The dynamic gain-scaling factor is utilised to deal with high-order terms so that the considered class of systems are generalised over the previous results. The simulation results effectively demonstrate the validity of our proposed control method. [ABSTRACT FROM AUTHOR]

This work developed an adaptive fuzzy tracking control strategy for pure-feedback nonlinear multi-agent systems via backstepping. The relevant unknown functions are addressed by fuzzy logic systems (FLSs), which take into account entirely unknown nonlinearities. The non-affine pure-feedback form is dealt with a Butterworth low-pass filter (BLF), and the converted systems can be used to construct a controller. The implementation of the first-order sliding-mode differentiator overcomes the difficulty of calculation explosion. The backstepping method is applied to design the virtual controller step by step, and the actual controller is designed successfully. Furthermore, through Lyapunov stability theory, the stability of systems is ensured, and that the tracking error is kept to a minimum. Finally, the simulation result shows the validity of the designed control strategy. [ABSTRACT FROM AUTHOR]

In this paper, we develop an improved data-based integral policy iteration method to address the robust control issue for nonlinear systems. Combining multi-step neural networks with pre-training, the condition of selecting the initial admissible control policy is relaxed even though the information of system dynamics is unknown. Based on adaptive critic learning, the established algorithm is conducted to attain the optimal controller. Then, the robust control strategy is derived by adding the feedback gain. Furthermore, the computing error is considered during the process of implementing matrix inverse operation. Finally, two examples are presented to verify the effectiveness of the constructed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive optimal control strategy of finite-time control for high-order uncertain strict-feedback nonlinear systems. Firstly, a reinforcement learning (RL) based an optimal control scheme is employed to design a optimal controller, to achieve global optimisation. Additionally, considering the unmeasurable states, we construct a fuzzy observer and utilise fuzzy logic systems to approximate the unknown functions. Meanwhile, the inclusion of command filtering and time-based control simplifies the controller design and enhances the system's response rapidity. Finally, the effectiveness and feasibility of the proposed approach are validated through a numerical simulation and a single link-robot system simulation. [ABSTRACT FROM AUTHOR]

This study aims to investigate a novel nonlinear optimization problem that incorporate a partial differential equation (PDE) constraint for image denoising in the context of mixed noise removal. Based on H−s$$ {H}^{-s} $$‐norm and decomposition approach, we develop a nonlinear system that involves the fractional Laplacian operator. Based on Schauder's fixed point theorem, we establish the existence and uniqueness of weak solution for the direct problem. Furthermore, we study the well‐posedness of the optimal control, and we also prove the existence of the weak solutions for the adjoint problem by using Galerkin's method. In order to numerically compute the solution of the proposed model, we introduce the numerical discretization scheme and the primal–dual algorithm used to solve our problem. Finally, we provide comparative numerical experiments to evaluate the efficiency and effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

This paper studies the $ {H_\infty } $ H ∞ filtering problem for a class of discrete-time semi-Markov switching repeated scalar nonlinear systems with an event-triggered scheme. Considering the limitation of bandwidth, the mode-dependent event-triggered mechanism is introduced to determine whether the currently sampled sensor data is transmitted to the filter, in which the parameters of the event generator depend on the system mode. By means of a constructed time-varying Lyapunov function, the two stages of the systems jump instant and mode residence are analysed, and a linear matrix inequality technique is used to ensure the mean-square stability and $ {H_\infty } $ H ∞ performance of the filter error system. Accordingly, the co-design method of the mode-dependent time-varying filter and the event-triggered mechanism is derived for semi-Markov switching systems. Finally, a numerical simulation is given to illustrate the effectiveness and feasibility of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

In this article, the distributed adaptive finite-time containment control problem is investigated for nonlinear multi-agent systems with unknown input saturations. Since the leaders are only available to partial subsystems, a distributed estimator is designed to estimate the local reference signals. The saturated function is converted to the combination of a linear part and a nonlinear part, thus overcomes the difficulties caused by unknown saturated bounds. By utilising the command filtered and backstepping control techniques, a two-step method is adopted to construct the controller, under which the effect of unknown input saturations can be compensated well. It is proved that the closed-loop system is practical finite-time stable, and the output of each follower can converge to the dynamic convex hull spanned by the leaders in finite time. Finally, a group of four one-link manipulator systems is used to support the feasibility of the developed control scheme. [ABSTRACT FROM AUTHOR]

In this article, the prescribed‐time global stabilization problem is taken into consideration when it comes to multi‐input‐multi‐output (MIMO) systems with unknown mismatched coupling nonlinear items. It is assumed that the unknown nonlinearities satisfy the linear growth conditions. First, a class of MIMO coupling nonlinear systems with controllable canonical form are considered. Then, by wielding a time‐varying Lyapunov‐like function and properties of parametric Lyapunov equations, a high‐gain linear time‐varying state feedback control law is developed to attenuate the impact of unknown nonlinear terms. Meanwhile, all state signals and control inputs are globally bounded and the target system becomes stable within the prescribed‐time. Finally, the simulation results on an example system with 3 inputs and 11 state variables verify that the developed control mechanism accomplishes the goal of global stabilization in the pre‐defined time. [ABSTRACT FROM AUTHOR]

Summary: This paper designs a distributed dynamic‐detection‐based event‐triggered control method to address the secure tracking control problem. A distributed online iterative dynamic detection mechanism is designed to detect denial‐of‐service (DoS) attacks. The detection results are transmitted into a proposed ET method to adjust the triggering threshold, which can mitigate the impact of DoS attacks. Moreover, the signals of state, observations of disturbance observers and adaptive laws are calculated to generate the trigger signals in the control signal simultaneously, which can help conserve communication resources and reduce the occurrence of pulse phenomenon. Meanwhile, since the predictor exists certain prediction errors that will reduce the tracking performance, a speed function is introduced to ensure the achievement of the control objective. The designed solution guarantees semiglobally uniformly ultimately bounded for all signals within the closed‐loop system. Finally, the effectiveness of control scheme is demonstrated through several simulation results. [ABSTRACT FROM AUTHOR]

The stabilization of nonlinear stochastic systems with input and output delays is investigated in this article. First, an observer that includes input delay information is designed to estimate the state information after eliminating the effect of input delay. Second, a predictive scheme with delay information is utilized to predict the system state affected by output delay, and an event‐triggered predictive controller is established to eliminate the effects of input and output delays simultaneously. Subsequently, the convergence of the prediction error is analyzed by utilizing a recursive method, and a sufficient condition to ensure the global exponential stability is given by constructing a new closed‐loop system. Finally, numerical simulations of two nonlinear stochastic systems with different delay sizes are utilized to verify the impact of the event‐triggered predictive control in addressing the stabilization of nonlinear stochastic systems with time delays. [ABSTRACT FROM AUTHOR]

This paper focused on fault estimation in Lipschitz nonlinear systems by providing an improved intermediate estimator (IIE). The core feature of an intermediate estimator is to simultaneously estimate the fault and states of the system without considering the matching condition. Since fault estimation is the first step of the process to compensate for its effect, the system performance can be further improved by making more accurate fault estimation. The structure of the nominal intermediate estimator (NIE) was modified by being inspired by the proportional‐integral (PI) controller to improve the performance characteristics of the estimator, such as convergence rate, overshoot, and steady‐state error. The estimation equation was assumed as such in the proposed estimator structure that the estimated fault would have a PI structure of the output estimation error. This has increased the number of design parameters, improving the estimator performance in transient and steady‐state. The NIE can be indeed known as a particular case of the IIE since the proposed estimator benefits from all the advantages of an NIE while improving the estimation performance. The states of the error system were proven to be uniformly ultimately bounded according to Lyapunov stability theory and using the LMI method, as well as the appropriate selection of parameters. The analysis of the theory, simulation, and comparing the results of the proposed method with the NIE method reveal the capabilities and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the prescribed performance adaptive fault‐tolerant control problem for nonlinear systems with actuator faults and input dead‐zone (IDZ). The partial loss of effectiveness fault and bias fault are considered. First, the asymmetric nonlinear IDZ is transformed into an affine function in the backstepping process. Second, the radial basis function neural network technique is applied to deal with the unknown uncertainty of the studied system. The state observer is designed to estimate the unmeasurable states in the nonlinear systems. Third, a fault‐tolerant controller is developed such that all signals are semi‐globally uniformly ultimately bounded for the closed‐loop system, and the tracking error remains within the specified performance requirements even though the actuator faults and IDZ occur. Finally, an electromechanical system example is provided to exhibit the utilizability of the obtained control technique. [ABSTRACT FROM AUTHOR]

In this article, a class of global finite‐time stabilization problem for high‐order stochastic nonlinear systems with multiple unknown control directions is studied. In order to solve the difficulties caused by the unknown control directions and at the same time, the system is required to converge to the origin in a limited time. Therefore, a new switching mechanism based on the Lyapunov‐like function is proposed. By adding a power integral technique and utilizing sign function, a finite‐time switching controller is designed. The controller parameters need to be adjusted online according to the proposed switching logic. It is rigorously proved that the designed controller can make the system state converge probabilistically to the origin in a finite‐time. A simulation example shows the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

In the new radio wireless system, use cases for automation, automotive safety and virtual reality will require transmission and reception with very high reliability and very low latency. Radio channel fading and the onset of strong interference combine to make this task hard, which require new methods for mitigation. The alternative studied in this article is to introduce fast signal to interference and noise ratio (SINR) feedback control supported by feed‐forward control from interference estimates, thereby improving the robustness of the networked loop. This challenging nonlinear networked control problem involves delay and coarse control signal quantization. To guarantee that the deployed feedback loop instances are always well‐behaved, the SINR feedback controller is designed to be globally ℒ2$$ {\mathcal{L}}_2 $$‐stable using a new loop gain—loop delay product condition, derived from the classical circle criterion. Monte‐Carlo simulations show that the proposed controller is able to reduce the SINR back‐off significantly. [ABSTRACT FROM AUTHOR]

This article addresses the event‐triggered bipartite consensus problem for high‐order nonlinear multi‐agent systems with antagonistic interactions. Intermittent actuator faults covering bias and partial loss of effectiveness are also investigated. To achieve consensus, a relative‐output‐dependent compensator is constructed based on a time‐varying gain. Subsequently, by integrating adaptive compensation mechanisms as well as an event‐triggered mechanism, an output‐feedback fault‐tolerant consensus protocol is proposed for the sake of fault compensation. It is worth noting that a positive ℒp$$ {\mathcal{L}}_p $$ function is embedded in the triggering function to adjust the triggering threshold and exclude the Zeno behavior. Under a signed digraph, it is shown that the designed protocol enables all agents to achieve consensus with opposite signs, even if the total number of unknown faults is infinite. Finally, a simulation is presented to illustrate the validity of the obtained protocol. [ABSTRACT FROM AUTHOR]

This paper designs an adaptive event‐triggered output‐feedback controller for a class of p$$ p $$‐normal switched SNS with uncertain output function and time‐varying delays. Firstly, a Lyapunov function and reduced‐order observer using the output signal are constructed for nominal system by virtue of sign function and API technique. Then a dynamic gain and time‐decaying parameter are introduced into the event‐triggered mechanism to reduce the number of control updates. Next, by virtue of LKF and stochastic Barbalat's Lemma, it is proved that all the signals are bounded and system states asymptotically converge to zero in probability, and Zeno behavior is excluded. Finally, a numerical example and practical one are given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the problem of adaptive security control for a class of nonlinear multi‐agent systems (MASs) under deception attacks is studied. This scheme aims to design a new adaptive fuzzy control scheme for each subsystem of MASs to reduce the impact of network attacks (from sensor to controller) on state signals, and proposes Nussbaum function to deal with unknown time‐varying control directions. In addition, for multi‐agent systems, a finite‐time convergent security control scheme is designed to ensure the convergence of tracking error and the boundedness of signals in the closed‐loop system. Finally, the effectiveness of the proposed method is verified by a simulation example. [ABSTRACT FROM AUTHOR]

One of the primary advantages of model‐free predictive control over conventional methods is that it does not use any mathematical models and relies only utilizes measured input/output data from the storage. The capability of model‐free predictive control has been already demonstrated in nonlinear systems using linear and polynomial regression for data storage. However, identifying the appropriate order that aligns with the actual system order remains a primary challenge, selecting an incorrect order may result in increasing redundant terms, ultimately leading to instability issues. In this study, we employed the Singular Value Decomposition (SVD) order selection technique, combined with the Bayesian Information Criterion (BIC), to identify the appropriate input and output orders of the system as well as the optimal horizon order in predictive control. This combined technique was subsequently applied to determine the appropriate order for model‐free predictive control. Our findings confirmed the effectiveness of the proposed method using numerical simulations in both linear and nonlinear systems. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this paper, the coupled system nonlinear (2 + 1)-dim Broer–Kaup–Kupershmit (BKK) equation under consideration is based on extension of modified rational expansion method. The various kinds of multiple soliton solutions named anti-kink soliton, traveling wave solutions, periodic solution, dark soliton, kink soliton, bright soliton, anti-kink bright solitons, kink bright solitons for nonlinear BKK system are successfully constructed under symbolic computation Mathematica. The calculated results are very interesting, different and novel which have not been investigated in past literature. The graphical demonstration of constructed solutions show by contour, 2D and 3D shape by using computer software Mathematica. The constructed multiple soliton solutions prove that EMRE approach is very straightforward, efficient, reliable and powerful for the investigation of other nonlinear partial differential equations. The investigated results will be described and play a keen role in the study of nonlinear physical phenomena in the area of physics and engineering. [ABSTRACT FROM AUTHOR]

In this study, the travelling wave solutions of the general Drinfel'd–Sokolov–Wilson (DSW)-system, introduced as a model of water waves, are obtained and wave dynamics are investigated. Jacobi elliptic functions are valuable mathematical tools that can be used to various aspects of physics engineering. The method used is effective in generating periodic solutions. It has been observed that the periodic solutions obtained by using Jacobi elliptic function expansions containing different Jacobi elliptic functions may be different, and some new periodic solutions can be obtained. To examine the behaviour of the solutions obtained for appropriate values of the parameters, three-dimensional simulations were conducted using Maple TM . Additionally, 2D simulations are presented to physicists for easy observation of wave motion. The usage of these simulations aids in better understanding and predicting the behaviour of waves, tidal forces and other phenomena, ultimately contributing to the development of safer and more efficient structures and systems. The stability property of the obtained solutions was tested to demonstrate their reliability. The novelty of this study is that the rich solution group obtained with Jacobi elliptic functions is different from the solutions in the literature and thus provides new physical interpretations. The findings are expected to contribute to the development of new analytical and numerical tools for solving other nonlinear systems in the future, containing a large number of travelling wave solutions crucial for explaining certain scientific phenomena in fluid media. [ABSTRACT FROM AUTHOR]

The response of a nonlinear system excited by multi-frequency signals and Poisson white noise contains abundant dynamic behaviours. The study on system response has certain practical values. Utilising optimal dynamics response has unique advantages in weak feature identification under strong background noise. In this work, we study the optimal dynamics response in the bistable system excited by multi-frequency signals and Poisson white noise. The effects of noise parameters on system response and feature identification are discussed and analysed. Also, a signal identification method for unknown multi-frequency signals based on the optimal resonance response of the nonlinear system is proposed and applied to the compound fault diagnosis of rolling bearings, which utilises the difference between stochastic resonance and coherence resonance to determine characteristic components of the signals. The proposed method effectively identifies the characteristic components of the original excitation and avoids misjudgment caused by strong noise, which is effectively verified via simulation analysis and experiments. [ABSTRACT FROM AUTHOR]

This article is about a modification of Verlet's time integration method. First, a one-step version of the basic scheme is described. Subsequently, a diagonal matrix is introduced in the scheme to improve the behavior of Verlet's method with respect to period elongation. The generic coefficient of this diagonal matrix is defined by referring to the frequency which locally dominates the structural dynamics of the correlated unknown. Since the computation of these coefficients is algorithmic, the method presented does not involve the adjustment of any parameter. Furthermore, the presented modification does not alter the excellent amplitude decay and stability characteristics of the original method. As a result, a second order one-step explicit time integration scheme with desirable accuracy and stability properties is obtained. Several numerical examples for nonlinear problems are conducted to show the ability of the presented method to reduce unwanted period distortion. Computational algorithms can automate repetitive design tasks, saving designers time and effort. In this sense, these computational tools provide designers with detailed data insights that can guide decision-making processes. Once implemented, algorithms become effective and useful tools for increasing productivity and creating more robust designs. However, the reliability of the results is a prerequisite for the success of these approaches. In the context of the dynamic behavior of structures, the present work improves a preexisting computational tool to reproduce the motion of the system under study. Practical engineering applications for linear and nonlinear dynamic analysis can then be addressed by introducing data rigor into the design. Starting from appropriate mathematical models for the analyzed structures, the presented algorithm provides an accurate description of the resulting displacements and stresses. All this through a calculation effort comparable to that of the previous method. As demonstrated by the tests carried out, the simulation for the control of suspension systems as well as the seismic response to earthquake excitation on buildings can represent applicative implications in the engineering field. [ABSTRACT FROM AUTHOR]

In this manuscript we propose a numerical method for non-linear integro-differential systems arising in age-of-infection models in a heterogeneously mixed population. The discrete scheme is based on direct quadrature methods and provides an unconditionally positive and bounded solution. Furthermore, we prove the existence of the numerical final size of the epidemic and show that it tends to its continuous equivalent as the discretization steplength vanishes. [ABSTRACT FROM AUTHOR]

This study presents an astrophysics-inspired transit search optimization (TSO) algorithm based on exoplanet search divided into five phases: galaxy phase, star phase, transit phase, neighbor phase and exploitation phase for effective parameter estimation of fractional Hammerstein control autoregressive (Fr-HCAR) systems. Various physical phenomena and real processes can be modeled with Fr-HCAR systems and estimating the Fr-HCAR parameters becomes a vital task. The mean-square error (MSE)-based criterion function is developed, and efficacy of the TSO for Fr-HCAR identification is deeply analyzed for different fractional orders, disturbance levels and degrees of freedom. The TSO remained accurate, convergent, robust and stable for all variations in Fr-HCAR but the accuracy level degrades a little bit for high disturbance and increased degrees of freedom. The reliability and trustworthiness of the TSO for Fr-HCAR identification are endorsed through statistical analyses conducted on sufficient autonomous executions of the scheme. [ABSTRACT FROM AUTHOR]

Over the past two decades, vibrational resonance has garnered significant interest and evolved into a prominent research field. Classical vibrational resonance examines the response of a nonlinear system excited by two signals: a weak, slowly varying characteristic signal, and a fast-varying auxiliary signal. The characteristic signal operates on a much longer time scale than the auxiliary signal. Through the cooperation of the nonlinear system and these two excitations, the faint input can be substantially amplified, showcasing the constructive role of the fast-varying signal. Since its inception, vibrational resonance has been extensively studied across various disciplines, including physics, mathematics, biology, neuroscience, laser science, chemistry, and engineering. Here, we delve into a detailed discussion of vibrational resonance and the most recent advances, beginning with an introduction to characteristic signals commonly used in its study. Furthermore, we compile numerous nonlinear models where vibrational resonance has been observed to enhance readers' understanding and provide a basis for comparison. Subsequently, we present the metrics used to quantify vibrational resonance, as well as offer a theoretical formulation. This encompasses the method of direct separation of motions, linear and nonlinear vibrational resonance, re-scaled vibrational resonance, ultrasensitive vibrational resonance, and the role of noise in vibrational resonance. Later, we showcase two practical applications of vibrational resonance: one in image processing and the other in fault diagnosis. This presentation offers a comprehensive and versatile overview of vibrational resonance, exploring various facets and highlighting promising avenues for future research in both theory and engineering applications. [ABSTRACT FROM AUTHOR]

In this article, the adaptive finite time prescribed performance tracking control problem of a class of high‐order uncertain nonlinear systems under full state time‐varying constraints is addressed. By using the logarithmic function based nonlinear mapping technique rather than the barrier Lyapunov function based method, the obstacle of the full‐state constraints is resolved, and the undesired "feasibility conditions" in controller design are eliminated. By improving the prescribed performance control method, the initial value problem is overcome, and the finite‐time convergence of the tracking error can be ensured. Then, with the aid of the adding a power integrator method, an adaptive finite time prescribed performance tracking controller is designed, not only the tracking error but also the full states can be guaranteed to converge to a predefined constraint region in the predetermined time. Simulation examples are given to show the validness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The present work aims at investigating the adaptive iterative learning control (AILC) design for uncertain high‐order fully actuated nonlinear systems. In order to show the design principles, three types of nonlinear systems are considered, including systems with just parametric uncertainty, systems with both parametric and input distribution uncertainties as well as systems with both parametric uncertainty and an unknown control gain matrix. For different systems, the corresponding AILC schemes are proposed with different techniques in dealing with various system uncertainties, for which the convergence analysis are conducted rigorously based on the composite energy function. The effectiveness of the proposed AILC strategies is verified through numerical simulations. [ABSTRACT FROM AUTHOR]

In this article an optimal control scheme is proposed to solve robust control problem for matched and unmatched system. In the proposed optimal approach the value functions are designed such that the obtained optimal control law guarantees asymptotic stability of the uncertain nonlinear system. Since the proposed robust optimal control problem is not straightforward to solve, an off‐policy reinforcement‐learning algorithm based on neural networks approximation is developed to obtain robust optimal control law iteratively. The robust control law for matched uncertain systems can be achieved via proposed off‐policy learning algorithm without requiring exact knowledge of system's dynamics. The advantages of the proposed robust optimal controller are verified by comparative simulations on an uncertain model of a car suspension system and a mathematical nonlinear model. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an adaptive observer based output feedback prescribed‐time control is designed for a class of nonlinear systems with unknown parameters. According to the characteristics of the nonlinear systems, a novel adaptive observer is first designed. Then by imposing parametric Lyapunov equations, a time‐varying high‐gain observer based output feedback adaptive controller is constructed, by which, it is proven that the states of both the observer and the original system can be driven to zero within any prescribed time. Moreover, the control signal is proven uniformly bounded by a scalar. Two numerical examples are illustrated to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article studies the fault‐tolerant consensus issue of a class of nonlinear two‐time‐scale‐agent systems with heterogeneous sensor faults and external disturbances. First, the decentralized proportional integral (PI) observer for each agent is constructed to estimate the states and sensor faults simultaneously. Then, a distributed adaptive protocol based on the decentralized PI observer is designed to guarantee the consensus of the underlying systems. Some sufficient existence conditions are given in terms of linear matrix inequalities (LMIs) for the decentralized PI observer and the fault tolerant consensus protocol. Note that H∞$$ {H}_{\infty } $$ performance index is employed to attenuate the influence of sensor faults and disturbances on state estimation and the consensus of the underlying systems. Finally, the effectiveness and superiority of the proposed method are demonstrated by an application example and a numerical example. [ABSTRACT FROM AUTHOR]

This article proposes a novel multidimensional Taylor network (MTN)‐based adaptive finite‐time control approach for stochastic nonlinear systems with input saturation constraints. First, by introducing the hyperbolic tangent function, a piecewise smooth function is proposed to approximate the input saturation constraints, which can solve the problem caused by input saturation. Then, with the aid of MTN approximation theorem, a novel adaptive finite‐time control approach is proposed, which can guarantee that all signals of the closed‐loop system are semi‐globally finite‐time stability in probability (SGFTSP), the system output follows the given reference signal, meanwhile, the tracking error converges to a small neighborhood of the origin in finite‐time. Compared with the existing results, the proposed approach has the characteristics of simple structure and low calculation, which can achieve the satisfactory control performance with a reduced computational burden. In the end, the effectiveness of the proposed approach is validated by Lyapunov stability theory and two simulation experiments. [ABSTRACT FROM AUTHOR]

In this article, the issue of fixed‐time fault‐tolerant control is studied for a class of nonlinear systems with sensor/actuator faults. The tuning functions and command filters are used to solve the problem of over‐parameterization and differential explosion in traditional backstepping method. The above methods simplify the derivation and reduce the calculation burden of the system respectively. Moreover, the introduction of fixed‐time control further improves the rapidity of the system. Another feature of this design is that the fixed‐time stability is finally realized by introducing a special Lyapunov function, rather than the practical fixed‐time stability. The tracking error can be guaranteed to converge to the origin in a fixed time. Simulation results show that the proposed control strategy is effective even if there are actuator/actuator faults in the system. [ABSTRACT FROM AUTHOR]

In this article, a multi‐event‐triggered (MET) near‐optimal tracking control algorithm is developed for a discrete‐time (DT) nonlinear non‐zero‐sum game (NZSG). Firstly, the iterative dual heuristic dynamic programming (DHP) approach is utilized to obtain the optimal tracking control set. Subsequently, distinct independent triggering conditions are formulated for control inputs to improve the utilization efficiency of resources and ensure that the independence between them and the closed‐loop system has an excellent control performance. Meanwhile, input‐to‐state stability (ISS) is proven for the MET tracking plant. Additionally, neural networks (NNs) are designed in the iterative DHP algorithm to construct the model module, the critic module and the action modules, with aims to identify the unknown system, approximate costate function and estimate optimal tracking control strategy set, respectively. Finally, two numerical experimental simulations are conducted to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Many practical dynamic models always suffer from complex nonlinearities, parameter uncertainties and random noises. Such kind of models are always characterized by uncertain stochastic nonlinear systems. Besides, many factors will lead to dead‐zone actuator, which further adds more challenges to control engineering. This paper studies stabilization issue of stochastic systems with polynomial nonlinear conditions, unknown parameters and dead‐zone actuator. An adaptive control approach is proposed by exploring homogeneous domination control and using dynamic‐gain‐based adaptive design strategy. It shows that all the signals are bounded almost surely and the system states converge to the origin almost surely. The presented method is successfully applied to the mass spring mechanical system. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive fault‐tolerant control (FTC) scheme is proposed for a class of uncertain nonlinear systems with actuator failure, in which a healthy actuator is available as a backup. Instead of monitoring the failure in real‐time, an intermittent monitoring mechanism (IMM) is proposed by designing a set of monitoring functions operating only at discrete time instants, which makes the proposed scheme more applicable for networked control systems for which the actuator status has to be monitored via network. An adaptive backstepping controller is designed with the errors being modified to include shifting functions. Once actuator failure is detected by the monitoring functions, the actuator is switched to its backup and the controller is reconfigured by updating the shifting functions so that the tracking error can be driven into the prescribed performance in a given time. [ABSTRACT FROM AUTHOR]