Showing papers on "Nonlinear system published in 2022"
TL;DR: In this article , an adaptive neural network (NN) output feedback optimized control design for a class of strict-feedback nonlinear systems that contain unknown internal dynamics and the states that are immeasurable and constrained within some predefined compact sets is proposed.
Abstract: This article proposes an adaptive neural network (NN) output feedback optimized control design for a class of strict-feedback nonlinear systems that contain unknown internal dynamics and the states that are immeasurable and constrained within some predefined compact sets. NNs are used to approximate the unknown internal dynamics, and an adaptive NN state observer is developed to estimate the immeasurable states. By constructing a barrier type of optimal cost functions for subsystems and employing an observer and the actor-critic architecture, the virtual and actual optimal controllers are developed under the framework of backstepping technique. In addition to ensuring the boundedness of all closed-loop signals, the proposed strategy can also guarantee that system states are confined within some preselected compact sets all the time. This is achieved by means of barrier Lyapunov functions which have been successfully applied to various kinds of nonlinear systems such as strict-feedback and pure-feedback dynamics. Besides, our developed optimal controller requires less conditions on system dynamics than some existing approaches concerning optimal control. The effectiveness of the proposed optimal control approach is eventually validated by numerical as well as practical examples.
217 citations
TL;DR: In this paper , the numerical solution of nonlinear time-fractional Fisher equations via local meshless method combined with explicit difference scheme is presented, which uses radial basis functions to compute space derivatives while Caputo derivative scheme utilizes for time fractional integration to semi-discretize model equations.
Abstract: This paper addresses the numerical solution of nonlinear time-fractional Fisher equations via local meshless method combined with explicit difference scheme. This procedure uses radial basis functions to compute space derivatives while Caputo derivative scheme utilizes for time-fractional integration to semi-discretize the model equations. To assess the accuracy, maximum error norm is used. In order to validate the proposed method, some non-rectangular domains are also considered.
173 citations
TL;DR: In this article , the impact of unsteady viscous flow in a squeezing channel was examined by inserting hybrid nanofluid particles with different shapes into the base fluid engine oil.
Abstract: The present study examines the impact of unsteady viscous flow in a squeezing channel. Silver–gold hybrid nanofluid particles with different shapes are inserted in the base fluid engine oil. Flow and heat transfer mechanism is detected in the presence of magnetohydrodynamics between the two parallel infinite plates. The thermal conductivity models, that is, Yamada–Ota and Hamilton–Crosser models are used to investigate various shapes (Blade, platelet, cylinder, and brick) of hybrid nanoparticles. The model is made up of paired high nonlinear partial differential equations that are then transformed into ordinary differential equations which are coupled and strong nonlinear using the boundary layer approximation. The MATLAB solver bvp4c package is used to solve the numerical solution of this coupled system. The influence of different parameters on the physical quantities is addressed via graphs. A comparison with already reported results is given in order to confirm the current findings. The analysis shows that surprisingly the Yamada–Ota model of the Hybrid nanofluid gains high temperature and velocity profile than the Hamilton–Crosser model of the hybrid nanofluid. Also, both the models show increasing trends toward increasing the volume fraction rate of silver‐gold hybrid nanoparticles. It is also inferred that the hybrid‐nanoparticles performance is far better than the common nanofluids.
165 citations
TL;DR: In this article , the issue of resilient event-triggered (RET)-based security controller design for nonlinear networked control systems (NCSs) described by interval type-2 (IT2) fuzzy models subject to nonperiodic denial of service (DoS) attacks is studied.
Abstract: This article studies the issue of resilient event-triggered (RET)-based security controller design for nonlinear networked control systems (NCSs) described by interval type-2 (IT2) fuzzy models subject to nonperiodic denial of service (DoS) attacks. Under the nonperiodic DoS attacks, the state error caused by the packets loss phenomenon is transformed into an uncertain variable in the designed event-triggered condition. Then, an RET strategy based on the uncertain event-triggered variable is firstly proposed for the nonlinear NCSs. The existing results that utilized the hybrid triggered scheme have the defect of complex control structure, and most of the security compensation methods for handling the impacts caused by DoS attacks need to transmit some compensation data when the DoS attacks disappear, which may lead to large performance loss of the systems. Different from these existing results, the proposed RET strategy can transmit the necessary packets to the controller under nonperiodic DoS attacks to reduce the performance loss of the systems and a new security controller subject to the RET scheme and mismatched membership functions is designed to simplify the network control structure under DoS attacks. Finally, some simulation results are utilized to testify the advantages of the presented approach.
145 citations
TL;DR: Using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults, respectively, the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed and it is proved that the state of each follower can synchronize with the leader's state under a directed graph.
Abstract: This paper considers the human-in-the-Ioop leader-following consensus control problem of multi-agent systems (MASs) with unknown matched nonlinear functions and actuator faults. It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory. Moreover, the leader's input is nonzero and not available to all followers. By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults, respectively, the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed. It is proved that the state of each follower can synchronize with the leader's state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded. Finally, simulation results are presented for verifying the effectiveness of the proposed control method.
120 citations
TL;DR: A novel planar flow-based variational auto-encoder prediction model (PFVAE) is proposed, which uses the long- and short-term memory network (LSTM) as the auto- Encoder and designs the variational Auto-Encoder (VAE), as a time series data predictor to overcome the noise effects.
Abstract: Prediction based on time series has a wide range of applications. Due to the complex nonlinear and random distribution of time series data, the performance of learning prediction models can be reduced by the modeling bias or overfitting. This paper proposes a novel planar flow-based variational auto-encoder prediction model (PFVAE), which uses the long- and short-term memory network (LSTM) as the auto-encoder and designs the variational auto-encoder (VAE) as a time series data predictor to overcome the noise effects. In addition, the internal structure of VAE is transformed using planar flow, which enables it to learn and fit the nonlinearity of time series data and improve the dynamic adaptability of the network. The prediction experiments verify that the proposed model is superior to other models regarding prediction accuracy and proves it is effective for predicting time series data.
76 citations
TL;DR: In this paper , a distributed tracking problem for uncertain nonlinear multiagent systems (MASs) under event-triggered communication is proposed, where subsystems in MASs are divided into two groups, in which the first group consists of the subsystems that can access partial output of the reference system and the second one contains all the remaining subsystems.
Abstract: The distributed tracking problem for uncertain nonlinear multiagent systems (MASs) under event-triggered communication is an important issue. However, existing results provide solutions that can only ensure stability with bounded tracking errors, as asymptotic tracking is difficult to be achieved mainly due to the errors caused by event-triggering mechanisms and system uncertainties. In this article, with the aim of overcoming such difficulty, we propose a new methodology. The subsystems in MASs are divided into two groups, in which the first group consists of the subsystems that can access partial output of the reference system and the second one contains all the remaining subsystems. To estimate the state of the reference system, a new distributed event-triggered observer is first designed for the first group based on a combined output observable condition. Then, a distributed event-triggered observer is proposed for the second group by employing the observer state of the first group. Based on the designed observers, adaptive controllers are derived for all subsystems. It is established that global stability of the closed loop system is ensured and asymptotic convergence of all the tracking errors is achieved. Moreover, a simulation example is provided to show the effectiveness of the proposed method.
76 citations
TL;DR: In this paper , the human-in-the-oop leader-following consensus control problem of multi-agent systems with unknown matched nonlinear functions and actuator faults is considered.
Abstract: This paper considers the human-in-the-Ioop leader-following consensus control problem of multi-agent systems (MASs) with unknown matched nonlinear functions and actuator faults. It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory. Moreover, the leader's input is nonzero and not available to all followers. By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults, respectively, the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed. It is proved that the state of each follower can synchronize with the leader's state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded. Finally, simulation results are presented for verifying the effectiveness of the proposed control method.
73 citations
TL;DR: This article focuses on scaled consensus tracking for a class of high-order nonlinear multiagent systems with time delays and external disturbances, and a fully distributed consensus protocol is designed to drive all agents to achieve scaled consensus with preassigned ratios.
Abstract: This article focuses on scaled consensus tracking for a class of high-order nonlinear multiagent systems Different from the existing results, for high-order nonlinear multiagent systems with time delays and external disturbances, a fully distributed consensus protocol is designed to drive all agents to achieve scaled consensus with preassigned ratios The control gains are varying and updated by distributed adaptive laws As a result, the presented protocol is independent of any global information, and thus, could be implemented in a fully distributed manner Simultaneously, the fully distributed control protocol using an adaptive $\sigma$ -modification technique is presented to deal with external disturbances, which can guarantee the tracking errors and coupling weights of all following agents are uniformly ultimately bounded To tackle with the derivatives of the functionals with time delays, the Lyapunov–Krasovskii functional is employed to analyze and compensate them by introducing multiintegral terms Finally, simulation examples are included to verify the effectiveness of the theoretical results
72 citations
70 citations
TL;DR: A Reversible Automatic Selection Normalization (RASN) network is proposed, integrating the normalization and renormalization layer to evaluate and select thenormalization module of the prediction model, showing good prediction ability and adaptability for the greenhouse in the Smart Agriculture System.
Abstract: Due to the nonlinear modeling capabilities, deep learning prediction networks have become widely used for smart agriculture. Because the sensing data has noise and complex nonlinearity, it is still an open topic to improve its performance. This paper proposes a Reversible Automatic Selection Normalization (RASN) network, integrating the normalization and renormalization layer to evaluate and select the normalization module of the prediction model. The prediction accuracy has been improved effectively by scaling and translating the input with learnable parameters. The application results of the prediction show that the model has good prediction ability and adaptability for the greenhouse in the Smart Agriculture System.
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01 Jan 2022
TL;DR: In this article , the authors present several approaches for the study of nonlinear composites using full-field or mean-field approaches and provide numerical examples on thermoelastic, elastoplastic, and damageable media.
Abstract: The scope of this chapter is to present briefly several available approaches in the literature for the study of nonlinear composites using full–field or mean–field approaches. The list of methods discussed here includes characteristic cases and should not be seen as exhaustive. To demonstrate the implementation of nonlinear behavior into a micromechanics framework, we present in more detail a simple technique based on the Mori–Tanaka and transformation field analysis approaches. We also provide numerical examples on thermoelastic, elastoplastic, and damageable media using mean–field computational strategies.
TL;DR: In this paper , the authors developed a system for studying the dynamical behavior of the drinking population through the fractional drinking model in the sense of Caputo-Fabrizio (CF) arbitrary order operator along with the special non-singular kernel.
Abstract: The investigation of this research article is the development of studying the dynamical behavior of the drinking population through the fractional drinking model in the sense of Caputo-Fabrizio (CF) arbitrary order operator along with the special non-singular kernel. The proposed system is analyzed for existence result and uniqueness of solution by applying fixed point theory and Picard's technique. Also on utilizing Adams-Bashforth method (ABM) of numerical analysis to interpret the approximate results through plots to observe dynamical behavior corresponding to different fractional order. For the mentioned simulation some real initial and parameter data are used.
TL;DR: In this paper , the steady hydromagnetic flow and heat transfer behavior of non-Newtonian (Cross) hybrid nanofluid with water as base fluid and SWCNT, and MWCNT as nanoparticles past a stretched cylinder has been analyzed.
TL;DR: In this article, a partially-coupled nonlinear parameter optimization algorithm is proposed for the multivariate hybrid models, which has low computational complexity and high parameter estimation accuracy through computational efficiency analysis and numerical simulation verification.
TL;DR: In this article , a general framework based on physics-informed neural networks for identifying unknown geometric and material parameters is presented, which can be applied to other inverse problems in different applications, targeting material characterization, quality assurance, and structural design.
Abstract: Characterizing internal structures and defects in materials is a challenging task, often requiring solutions to inverse problems with unknown topology, geometry, material properties, and nonlinear deformation. Here, we present a general framework based on physics-informed neural networks for identifying unknown geometric and material parameters. By using a mesh-free method, we parameterize the geometry of the material using a differentiable and trainable method that can identify multiple structural features. We validate this approach for materials with internal voids/inclusions using constitutive models that encompass the spectrum of linear elasticity, hyperelasticity, and plasticity. We predict the size, shape, and location of the internal void/inclusion as well as the elastic modulus of the inclusion. Our general framework can be applied to other inverse problems in different applications that involve unknown material properties and highly deformable geometries, targeting material characterization, quality assurance, and structural design.
TL;DR: In this article , the authors investigate the transmission of localized waves through a dual-power law medium exhibiting the perturbations including the intermodal dispersion, self-steepening, and self-frequency shift effects.
Abstract: We investigate the transmission of localized waves through a dual-power law medium exhibiting the perturbations including the inter-modal dispersion, self-steepening, and self-frequency shift effects. A novel class of nonlinear waves that are periodic wave, kink soliton, algebraic soliton and bright soliton, along with the corresponding chirping, are reported. We found the nonlinear contribution chirp is proportional to n power of the light intensity, which is arising from the perturbations. Finally, the influence of degree of nonlinearity on the dynamical properties of the obtained chirped structures is presented.
TL;DR: In this article, an adaptive optimized formation control problem is studied for the second-order stochastic multiagent systems (MASs) with unknown nonlinear dynamics using the actor-critic architecture and Lyapunov stability theory to ensure that all the error signals are bounded in probability.
Abstract: In this article, an adaptive optimized formation control problem is studied for the second-order stochastic multiagent systems (MASs) with unknown nonlinear dynamics. Compared with first-order formation control, the second-order MASs consider not only the states but also the states rates, which is certainly more challenging and difficult work. In the control design of this article, the fuzzy logic systems are applied to approximate the nonlinear functions. By employing the actor-critic architecture and Lyapunov stability theory, the proposed optimal formation control strategy ensures that all the error signals are bounded in probability. Finally, the simulation examples verify that the proposed formation control approach achieves desired results.
TL;DR: In this paper , the authors show that mathematically modeling grids as coupled nonlinear dynamical systems and networks, and utilizing concepts from statistical physics and graph theory provide a comprehensive framework to understand and control their collective behavior as a system of many interacting units.
Abstract: The rapid increase in renewable energy production facilities, domestic installations injecting energy back onto the grid, and the surge in electric vehicle adoption and associated high voltage charging stations are all placing unprecedented demands on the electric power grid. This article summarizes the physics that can inform the design and operation principles for future compliant power grids. The authors show that mathematically modeling grids as coupled nonlinear dynamical systems and networks, and utilizing concepts from statistical physics and graph theory provide a comprehensive framework to understanding and controlling their collective behavior as a system of many interacting units. The article covers key topics including the synchronization dynamics and structural stability of power grids as well as methods to control dynamics and mitigate cascades of failures and large-scale blackouts.
01 Jun 2022
TL;DR: In this article , the adaptive neural network fixed-time tracking control issue for a class of strict-feedback nonlinear systems with prescribed performance demands was investigated, in which the radial basis function neural networks (RBFNNs) are utilized to approximate the unknown items.
Abstract: This article investigates the adaptive neural network fixed-time tracking control issue for a class of strict-feedback nonlinear systems with prescribed performance demands, in which the radial basis function neural networks (RBFNNs) are utilized to approximate the unknown items. First, an modified fractional-order command filtered backstepping (FOCFB) control technique is incorporated to address the issue of the iterative derivation and remove the impact of filtering errors, where a fractional-order filter is adopted to improve the filter performance. Furthermore, an event-driven-based fixed-time adaptive controller is constructed to reduce the communication burden while excluding the Zeno-behavior. Stability results prove that the designed controller not only guarantees all the signals of the closed-loop system (CLS) are practically fixed-time bounded, but also the tracking error can be regulated to the predefined boundary. Finally, the feasibility and superiority of the proposed control algorithm are verified by two simulation examples.
TL;DR: In this article , an adaptive dynamic programming-based data-driven controller for hydraulic servo actuators (HSA) with unknown dynamics is proposed, which requires neither the knowledge of the HSA dynamics nor exosystem dynamics.
Abstract: <p style='text-indent:20px;'>The hydraulic servo actuators (HSA) are often used in the industry in tasks that request great powers, high accuracy and dynamic motion. It is well known that HSA is a highly complex nonlinear system, and that the system parameters cannot be accurately determined due to various uncertainties, inability to measure some parameters, and disturbances. This paper considers control problem of the HSA with unknown dynamics, based on adaptive dynamic programming via output feedback. Due to increasing practical application of the control algorithm, a linear discrete model of HSA is considered and an online learning data-driven controller is used, which is based on measured input and output data instead of unmeasurable states and unknown system parameters. Hence, the ADP based data-driven controller in this paper requires neither the knowledge of the HSA dynamics nor exosystem dynamics. The convergence of the ADP based control algorithm is also theoretically shown. Simulation results verify the feasibility and effectiveness of the proposed approach in solving the optimal control problem of HSA.</p>
TL;DR: In this article , an observer-based fault-tolerant control scheme is proposed to estimate incomplete measurable variables and eliminate the influence of fault dynamically well and enhance the robust stability of the systems subject to quantization effects.
Abstract: This article devotes to solve the fault-tolerant control problem based on interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy stochastic switched uncertain time-delayed systems with signal quantization. Stochastic switched systems can model a dynamic structure susceptible to abrupt faults, making it more practically significant in power systems or economic systems. The core design is an observer-based fault-tolerant control scheme that can estimate incomplete measurable variables and eliminate the influence of fault dynamically well and enhancing the robust stability of the systems subject to quantization effects. A novel method in seeking the upper bound solution of time-varying delay efficiently decreases conservativeness, especially for the proposed time-delayed system. The simulated analysis is specified to verify the availability and validity of the obtained design method.
TL;DR: In this paper , a finite-time adaptive fuzzy prescribed performance control (PPC) via output-feedback for nonstrict feedback nonlinear systems was considered, and the control strategy was shown that the closed-loop system is practical finite time stable, and convergence error converges to a residual set within prescribed performance bound in finite time.
Abstract: This article considers the problem of finite-time adaptive fuzzy prescribed performance control (PPC) via output-feedback for nonstrict-feedback nonlinear systems. The fuzzy state observer is designed to estimate the unmeasured system states. To rapidly approximate the derivative of virtual signal, a novel finite-time command filter is proposed. The fractional power error compensation mechanism is established to remove filtered error. By integrating the PPC and command filter technique into backstepping recursive design, a finite-time adaptive output-feedback controller is constructed, and the stability of closed-loop system is strictly proved. The designed control strategy shows that the closed-loop system is practical finite-time stable, and the output tracking error converges to a residual set within prescribed performance bound in finite time. Finally, a numerical comparison and practical examples are provided to demonstrate the validity of the developed finite-time control algorithm.
TL;DR: In this article, a hybrid forecasting model is developed by using the decomposition strategy, nonlinear weighted combination, and two deep learning models to overcome the drawbacks of the linear weighted combination and further enhance wind power forecasting accuracy and stability.
TL;DR: In this article , the solution of the time-fractional Newell-Whitehead-Segel equation with the help of two different methods is found, and the numerical results obtained by suggested techniques are compared with an exact solution.
Abstract: In this paper, we find the solution of the time-fractional Newell-Whitehead-Segel equation with the help of two different methods. The newell-Whitehead-Segel equation plays an efficient role in nonlinear systems, describing the stripe patterns' appearance in two-dimensional systems. Four case study problems of Newell-Whitehead-Segel are solved by the proposed methods with the aid of the Antagana-Baleanu fractional derivative operator and the Laplace transform. The numerical results obtained by suggested techniques are compared with an exact solution. To show the effectiveness of the proposed methods, we show exact and analytical results compared with the help of graphs and tables, which are in strong agreement with each other. Also, the results obtained by implementing the suggested methods at various fractional orders are compared, which confirms that the solution gets closer to the exact solution as the value tends from fractional-order towards integer order. Moreover, proposed methods are interesting, easy and highly accurate in solving various nonlinear fractional-order partial differential equations.