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In this paper, an observer with exponential time-varying gain is proposed for a class of nonlinear systems in Takagi–Sugeno form with both continuous-time and aperiodic sampled outputs. The proposed observer consists of two parts: one related to the continuous-time outputs and one related to the discrete outputs. Sufficient conditions involving a tuning parameter and the maximum allowed sampling time are derived in terms of quasi linear matrix inequality with respect to a scalar to ensure asymptotic convergence of the observer on the basis of Lyapunov–Krasovskii functional and H ∞ approach. Numerical simulations are provided to show the benefits of the proposed observer. Some comparisons with the high gain continuous-discrete observer and fuzzy observer with constant gain are provided to illustrate the interest of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of leaderless consensus control for the multiagent systems whose nonlinear dynamics satisfying incremental quadratic constraints. A distributed dynamic consensus protocol, decided by communication among neighboring agents, is presented to render nonlinear agent consensus with appropriate coupling weights. Next, an observer-based distributed protocol is considered to ensure consensus of nonlinear system without knowing full state information. Further, extensions to consensus strategies with nonlinear dynamics for the leader-following fashion are also addressed. By comparison to the traditional nonlinear consensus control methodologies, the proposed approach generalizes the Lipschitz nonlinearity as well as the combined nonlinearity of one-sided Lipschitz condition and quadratic inner-boundness condition towards a more generalized type of nonlinearity, which shows us a less conservative result in the Lyapunov proof. Finally, the numerical simulations for six agents are illustrated to show the feasibility and performance of the proposed control protocol with or without the presence of the observer. [ABSTRACT FROM AUTHOR]

In this paper, the optimal consensus control problem of nonlinear multi-agent systems(MASs) with completely unknown dynamics is considered. The problem is formulated in a differential graphical game approach which can be solved by Hamilton-Jacobi (HJ) equations. The main difficulty in solving the HJ equations lies in the nonlinear coupling between equations. Based on the Adaptive Dynamic Programming (ADP) technique, an VI-PI mixed HDP algorithm is proposed to solve the HJ equations distributedly. With the PI step, a suitable iterative initial value can be obtained according to the initial policies. Then, VI steps are run to get the optimal solution with exponential convergence rate. Neural networks (NNs) are applied to approximate the value functions, which makes the data-driven end-to-end learning possible. A numerical simulation is conducted to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the consensus tracking problem is studied for a group of nonlinear heterogeneous multiagent systems with asymmetric state constraints and input delays. Different from the existing works, both input delays and asymmetric state constraints are assumed to be nonuniform and time-varying. By introducing a nonlinear mapping to handle the problem caused by state constraints, not only the feasibility condition is removed, but also the restriction on the constraint boundary functions is relaxed. The time-varying input delays are compensated by developing an auxiliary system. Furthermore, by utilizing the dynamic surface control method, neural network technology and the designed finite-time observer, the distributed adaptive control scheme is developed, which can achieve the synchronization between the followers' output and the leader without the violation of full-state constraints. Finally, a numerical simulation is provided to verify the effectiveness of the proposed control protocol. [ABSTRACT FROM AUTHOR]

Practical time-varying formation-containment tracking (TVFCT) issue for nonlinear multi-agent systems with general noise is researched in this paper. A neuro-adaptive practical TVFCT protocol is put forward, where neural networks (NNs) and parameter adaptive laws are developed for estimating the nonlinearities and compensating the upper bound of the time-varying formation (TVF) condition, respectively. Leaders are expected to achieve the predetermined TVF and followers are driven to move into the formation structure confined by leaders simultaneously. The whole formation position depends on the desired trajectory produced by a virtual-leader. General noise rather than white noise is investigated in the multi-agent systems. The noise-to-state-practically stability in probability (NSPS-P) criteria can be guaranteed by employing the Lyapunov theories combined random differential equations (RDEs). Numerical simulation results reveal the effectiveness of the developed protocols. [ABSTRACT FROM AUTHOR]

This paper is devoted to adaptive neural network control issue for a class of nonstrict-feedback uncertain systems with input delay and asymmetric time-varying state constraints. State-related external disturbances are involved into the system, and the upper bounds of disturbances are assumed as functions of state variables instead of constants. Additionally, during the approximations of unknown functions by neural networks, the online computation burdens are declined sharply, since the norms of neural network weight vectors are only estimated. In the process of dealing with input delay, an auxiliary function is applied such that the conditions for time delay are more general than the ones in existing literature. A novel adaptive neural network controller is designed by constructing the asymmetric barrier Lyapunov function, which guarantees that the output of system has a good tracking performance and the state variables never violate the asymmetric time-varying constraints. Finally, numerical simulations are presented to verify the proposed adaptive control scheme. [ABSTRACT FROM AUTHOR]

• Adaptive global finite-time stabilization problem of nonlinear switched systems has been addressed in the presence of imposed perturbations and arbitrary switchings. • Nonlinear finite-time disturbance observer has been designed for switched systems in order to estimate the lumped disturbances and avoid loss of information caused by the persistent switchings in these systems. • Unlike the Lyapunov parameter estimation methods, the proposed finitetime disturbance observer method has the capability of exact disturbance estimation in finite time in which the global adaptive finite-time stabilization of nonlinear switched systems with free-chattering feature in spite of imposed perturbations is achieved. • Numerical simulations have been established on a mass-spring-damper dynamical system in order to indicate the advantages and superior efficiency of the suggested method. The problem of adaptive global finite-time stabilization control for a class of nonlinear switched systems in the presence of external perturbations and arbitrary switchings has been addressed in this research study. The proposed scheme has been designed based on a finite-time estimation technique in which during the control procedure, unknown imposed perturbations are accurately estimated by means of the designed finite-time disturbance observer (FTDO). Due to the exact estimation of the external disturbances within a given finite time, the encountered complications and adversities from loss of information in the Lyapunov parameter estimation (LPE) methods have been solved which are caused by the persistent switchings in the system. Furthermore, a new solution for the problem of chattering phenomenon in nonlinear switched systems has been presented by utilizing the designed FTDO, which can counteract the malfunctioning responses of the system caused by external disturbances and unmodeled dynamics. In this paper, an acknowledged class of nonlinear switched systems has been taken into account which is in the general form of canonical structure. In addition, the established design strategy is formulated for the control of perturbed nonlinear switched systems with one and only input and assures that the system states through the finite-time convergence characteristic, reach the equilibrium point of origin. Finally, numerical simulations are carried out on a mass-spring-damper (MSD) dynamical system to indicate advantages and superior efficiency of the suggested method. [ABSTRACT FROM AUTHOR]

As the fault and unknown inputs may cause great estimate errors and even divergence for conventional filters when dealing with nonlinear discrete-time systems, this paper proposed a three-stage unscented Kalman filter (ThSUKF) and robust three-stage unscented Kalman filter (RThSUKF) to improve the performance by estimating the state and fault simultaneously. The detail derivation of the filters is illustrated in the paper. The proposed filters are available when the statistic models of the fault and unknown inputs are perfectly known. But if there is no prior knowledge about the fault and unknown input, RThSUKF can also have a great performance. Their performance and effectiveness are demonstrated by the numerical simulation of two-dimensional target tracking. And the results show that ThSUKF and RThSUKF can achieve superior performances with low estimation errors if the nonlinear systems are in presence of fault and unknown inputs. [ABSTRACT FROM AUTHOR]

• The first study to solve the approximation problem of T-S fuzzy linear singular system for a class nonlinear singular system with derivative of input and the nonlinear singular system exists impulses. • The algorithm that if input-state system is Bounded Impulse-free Item and Separable Impulse Item, it can be approximated by T-S fuzzy singular system with arbitrary accuracy is given. • Considering a practical example (A two-wheel drive robot) and two numerical examples, the effect of algorithm approximation is especially good. In this paper, the approximation problem of T–S fuzzy linear singular system for a class of nonlinear singular system with derivative of input is considered and the nonlinear singular system has impulses. Consider a numerical example and a two-wheel drive robot, the T–S fuzzy singular systems are calculated for original system with derivative of input. According to solvability and steps of solving of the two examples, the results are extended to more generally nonlinear singular system with derivative of input. The theorem and algorithm that are given if input-state system is bounded impulse-free item and separable impulse item, it can be approximated by T–S fuzzy singular system with arbitrary accuracy. Finally, a numerical simulation is carried out to show the consistency with theoretical analysis and illustrate the effectiveness of approximation. [ABSTRACT FROM AUTHOR]

Abstract Competitive neural networks(CNNs) has not been well developed in nonlinear fractional order dynamical system, which is developed first time in this paper. Then, by means of a proper Lyapunov functional, asymptotic expansion of Mittag-Leffler function properties, together with some Caputo derivative properties, the testable novel sufficient conditions are given to guarantee the existence, uniqueness of the equilibrium point as well as global asymptotic stability for a class of fractional order competitive neural networks (FOCNNs) are all derived in the form of matrix elements. Furthermore, the boundedness for the solution of FOCNN is presented by employing Cauchy–Schwartz inequality and Gronwall inequality. Besides, a linear feedback control and adaptive feedback control are designed to achieve the global asymptotic synchronization criterion for FOCNNs with time delay and these explored consequences are extended from some previous integer order CNNs output. At last, two numerical simulations are performed to illustrate the effectiveness of our proposed theoretical results. [ABSTRACT FROM AUTHOR]

Abstract This paper solves the problem of adaptive neural dynamic surface control (DSC) for a class of full state constrained stochastic nonlinear systems with unmodeled dynamics. The concept of the state constraints in probability is first proposed and applied to the stability analysis of the system. The full state constrained stochastic nonlinear system is transformed to the system without state constraints through a nonlinear mapping. The unmodeled dynamics is dealt with by introducing a dynamic signal and the adaptive neural dynamic surface control method is explored for the transformed system. It is proved that all signals of the closed-loop system are bounded in probability and the error signals are semi-globally uniformly ultimately bounded(SGUUB) in mean square or the sense of four-moment. At the same time, the full state constraints are not violated in probability. The validity of the proposed control scheme is demonstrated through the simulation examples. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we mainly tend to consider distributed leader-following fixed-time quantized consensus problem of nonlinear multi-agent systems via impulsive control. An appropriate quantized criterion and some novel control protocols are proposed in order to solve the problem. The protocols proposed integrates the two control strategies from the point of view of reducing communication costs and constraints, which are quantized control and impulsive control. The fixed-time quantized consensus of multi-agent is analyzed in terms of algebraic graph theory, Lyapunov theory and comparison system theory, average impulsive interval. The results show that if some sufficient conditions are met, the fixed-time consensus of multi-agent systems can be guaranteed under impulsive control with quantized relative state measurements. In addition, compared with finite-time consensus, the settling-time of fixed-time quantized consensus does not depend on the initial conditions of each agent but on the parameters of the protocol. Finally, numerical simulations are exploited to illustrate the effectiveness and performance to support our theoretical analysis. [ABSTRACT FROM AUTHOR]

Highlights • A dynamic event-triggered mechanism is proposed to regulate the triggering threshold with respect to system output. • Considering actuator executing with failures, the fault parameters are estimated adaptively to offset the fault affects to systems. • New technique is developed to treat the controller synthesis. Abstract This paper is concerned with the reliable event-triggered H ∞ output control of nonlinear systems with actuator faults. A dynamic triggering scheme depending on system outputs is implemented to reduce the amount of communication transmissions, which is different from existing constant triggering thresholds. The parameters of actuator faults are estimated via observer state. To compensate for the fault effects on systems, the reliable controller parameters are adjusted along with the obtained estimations. By using some technical lemmas, new sufficient conditions for the closed-loop system to be asymptotically stable with prescribed H ∞ performance are formed in linear matrix inequalities. Lastly, simulations are implemented to demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a novel approach to stabilize a class of nonlinear systems with state constraints. The motivation behind this study is the need to develop a stabilizing state feedback controller that does not require the knowledge of Lyapunov function and can regulate the states to the equilibrium while meeting the constraints. By using an integration of two relatively new tools: immersion and invariance (I&I) theory and viability theory, a sufficient condition for stability and stabilizability of a general nonlinear affine system with state constraints is derived; Then, the related results are exploited to stabilize a class of nonlinear system in feedback form and with state constraints represented by inequalities and the viable I&I stabilizing state feedback controller is obtained constructively. Further, an application to a nonlinear aero-engine model with the temperature constraint is given to illustrate the applicability and the effectiveness of the proposed method. Finally, a comparative simulation is presented, highlighting the advantages of the viable I&I controller. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a robust adaptive control strategy for a class of state-constrained uncertain nonlinear systems with prescribed transient and steady-state behavior. The prescribed tracking performance can be characterized by constraints on an output tracking error. Both state and output constraints are achieved by bounding integral barrier Lyapunov functions in the backstepping procedure. A robust adaptive term is designed to compress auxiliary system uncertainties without the knowledge of their bounds. The satisfaction of control constraints and tracking error convergence are verified by theoretical analysis and are illustrated by simulation results. [ABSTRACT FROM AUTHOR]

In this paper, a decentralized adaptive approximation-based output feedback control scheme is developed for a class of uncertain switched stochastic nonlinear interconnected systems. Fuzzy logic systems (FLSs) combined with adaptive backstepping method are applied to model the unknown nonlinear functions of the switched system. The full-order observer is designed to estimate the immeasurable states and the dynamic surface control (DSC) technique is adopted to ensure the computation burden is greatly reduced. Moreover, the proposed controllers guarantee the semi-global bounded of the resulting closed-loop system under any switching laws. Simulation results show the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an intelligent self-repairing control scheme is proposed for a class of nonlinear MIMO system. A direct self-repairing controller of a nonlinear SISO system is firstly designed, and then the control scheme is promoted to a nonlinear MIMO system. The error signals are replaced by the state variables to deal with the high derivate problems of the desired signals and a nonlinear regulating function is brought in to improve the performances of the sliding mode. The self-repairing controller is made up of four parts: the nonlinear regulator, the equal controller, the compensator I and the compensator II. The control method is applied to a helicopter flight control system with loss-in-effectiveness faults. Some simulation results illustrate the effectiveness and feasibility of the proposed control scheme in the paper. [Copyright &y& Elsevier]

It is well known that the main problem hindering the application of the sliding mode control (SMC) is the chattering effect produced by the use of discontinuous switching control functions. In this paper, a combination of several specialized SMC controllers is proposed in order to solve this problem and obtain high accuracy steady state sliding motions in case of a class of MIMO nonlinear systems with external matched disturbances and plant uncertainty. The developed method uses a disturbance estimation, a nominal controller that assumes the cancellation of the disturbance inputs and an error controller that guides the trajectory of the plant states using the nominal response as a tracking reference. The design of the SMC control laws is implemented using an adaptive nonlinear sliding mode definition that creates fast non-overshooting responses over the selected output variables. Numerical simulation results are given to illustrate the effectiveness of the proposed SMC. [ABSTRACT FROM AUTHOR]

In this paper, the spatiotemporal dynamics of a delayed diffusive prey–predator model with nonlinear predator harvesting is investigated. Through mathematical analysis, we obtain the conditions for Turing and Hopf bifurcation. Numerical simulations display a variety of spatial patterns including spots, strips, mixture of spots and strips, spiral, patchy structure, and chaos. The delay is found to have significant influence on the emergent spatial patterns, such as changing arm length and direction of strips, and accelerating the transformation of spatial patterns. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the central finite-dimensional filter for nonlinear polynomial systems with multiplicative noise, that is suboptimal for a given threshold with respect to a modified Bolza–Meyer quadratic criterion including the attenuation control term with the opposite sign. In contrast to the previously obtained results, the paper reduces the original filtering problem to the corresponding optimal H 2 filtering problem, using the technique proposed in . The paper presents the central suboptimal filter for the general case of nonlinear polynomial systems with multiplicative noise, based on the optimal H 2 filter given in . The central suboptimal filter is also derived in a closed finite-dimensional form for third (and less) degree polynomial system states. Numerical simulations are conducted to verify performance of the designed central suboptimal filter for nonlinear polynomial systems against the central suboptimal filters available for polynomial systems with state-independent noise and the corresponding linearized system. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a passive fuzzy controller design methodology for nonlinear system with multiplicative noises. Applying the Itô''s formula and the sense of mean square, the sufficient conditions are developed to analyze the stability and to design the controller for stochastic nonlinear systems which are represented by the Takagi–Sugeno (T–S) fuzzy models. The sufficient conditions derived in this paper belong to the Linear Matrix Inequality (LMI) forms which can be solved by the convex optimal programming algorithm. Besides, the passivity theory is applied to discuss the effect of external disturbance on system. Finally, some numerical simulation examples are provided to demonstrate the applications of the proposed fuzzy controller design technique. [Copyright &y& Elsevier]

This paper presents a new data-driven adaptive ILC (DDAILC) for a class of nonlinear discrete-time systems by introducing a pointwise dynamical linearization approach in the iteration direction. For the nonlinear systems, which cannot be linearly parameterized, the proposed DDAILC is capable of achieving a perfect performance without requiring any identical conditions exposed both on the initial state and on the reference trajectory. It is a data-driven control approach since only the I/O data is required for the control system design and analysis. The parameter updating law is constructed to estimate the inverse values of the system׳s unknown partial derivatives of the nonlinear system with respect to the control inputs, which are utilized to compute the learning gain of control law further. The control input is updated by using both the information of reference trajectory of the current operation as a feedback term and the input signals in previous operations as a feedforward term. Extension results to MIMO nonlinear discrete-time systems are provided further. Both theoretical analysis and simulation results verify the effectiveness of the proposed data-driven AILC approach. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we consider the consensus of second-order multi-agent systems with exogenous disturbances generated by some nonlinear exosystems under switching topologies. Firstly, a dynamic gain technique based disturbance observer is proposed to estimate the disturbances generated by some nonlinear exosystems. Secondly, based on the proposed disturbance observer, consensus protocol is further proposed. A rigorous consensus analysis is performed. Finally, numerical simulation results are provided to show the effectiveness of the proposed results. [Copyright &y& Elsevier]

Abstract: This paper investigates the adaptive output feedback control problem for a class of nonlinear systems with unknown time delays and output function. The system satisfies linear growth condition with an unknown growth rate. First of all, based on a dynamic gain scaling technique, we present a new dynamic high-gain observer without requiring precise information of the output function. Then, by employing the idea of universal control and the backstepping method, a universal adaptive output feedback control law is designed to globally regulate all the states of the system. A simulation example is presented to illustrate the effectiveness of the proposed design scheme. [Copyright &y& Elsevier]

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