Search

This paper investigates the design problem of quantized event-triggered (ET) integral sliding mode (ISM, SM) controller for uncertain networked linear systems. Firstly, this paper proposes a novel ISM surface constructed using only quantized ET states and applied to the design of the controller. The quantized ET ISM surface in this paper divides the integrals from t 0 to t into the sum of adjacent ET intervals and replaces ET states with quantized ET states. Secondly, based on state error and quantized ET SM error, novel ET conditions are constructed to decide whether to update the current control signal or not. Thirdly, this paper proves the existence of minimum inter-event times under "zoom-out" and "zoom-in" phases respectively, avoiding Zeno phenomenon. Finally, to prove the validity of the proposed method, two comparative simulation results are given. • A novel sliding mode control protocol for networked linear systems is proposed via quantized event-triggered method. • The proposed quantization mechanism is a dynamic type. • The proposed event-triggered mechanism is also a dynamic type via "zoom in"and"zoom out"stages. • Based on the LMIs and the discrete online adjustment strategy for quantized sensitivity to determine the event-triggered threshold gain. [ABSTRACT FROM AUTHOR]

This paper is devoted to the problem of quantized control for Markovian jump systems with general transition rates. Meanwhile, the considered system suffers from sensor attack, actuator attack and multiple disturbances. With the help of mode-dependent intermediate observer, the system states and the total disturbances involving both sensor attack and actuator attack are estimated. A mode-dependent composite controller design is constructed to stabilize the overall controlled system. Based on the technique of vertex separators, sufficient conditions are developed in the form of linear matrix inequalities to guarantee the uniformly ultimate boundedness of the closed-loop system. Finally, an application example illustrates the feasibility of the method. [ABSTRACT FROM AUTHOR]

This study examines the stability and stabilization issues of a type of state-quantized, time-varying delayed (TVD) Takagi–Sugeno (T–S) fuzzy semi-Markov jump systems. First of all, in order to obtain more information of T–S fuzzy systems, an augmented fuzzy Lyapunov–Krasovskii Functional (LKF) is formatted including a quadratic fuzzy Lyapunov matrix (QFLM). In addition, a novel quadratic polynomial inequality (QPI) is applied to narrow the estimation gap for TVD and a quantized controller is used to reduce control accuracy. Then, the sufficient conditions for system stability and stabilization via quantized controller are attained on the basis of Lyapunov stability theory and linear matrix inequalities method. Finally, three examples show how the constructed controller can successfully regulate the examined system and the proposed technique is less conservative than those of the former ones. [ABSTRACT FROM AUTHOR]

This paper proposes a quantized model-free adaptive iterative learning control (MFAILC) algorithm to solve the bipartite containment tracking problem of unknown nonlinear multi-agent systems, where the interactions between agents include cooperation and antagonistic interactions. To design the controller, the agent’s dynamics is transformed into the linear data model based on the dynamic linearization method, and then a quantized MFAILC algorithm is established based on the quantized values of the relative output measurements. The designed controller only depends on the input and output data of the agent. We prove that under the quantized MFAILC algorithm, the multi-agent systems can achieve the bipartite containment, that is, the output trajectories of followers converge to the convex hull formed by the leaders’ trajectories and the leaders’ symmetric trajectories. Finally, we provide simulations to illustrate the effectiveness of our theoretical results. [ABSTRACT FROM AUTHOR]

This paper addresses the fault-tolerant attitude tracking control problem for spacecraft with limited communication capability and input saturation. The design of a hysteresis quantizer aims to alleviate communication burden between the controller module and the actuator module. Then, the attitude tracking problem for spacecraft with input quantization, input saturation, actuator faults and external disturbances is transformed into an attitude control problem with uncertain input coefficients and bounded disturbances. Thereafter a dynamic loop gain function-based approach and a hyperbolic tangent function term with a time-varying boundary layer are introduced to address the uncertainties of input coefficients and the bounded disturbances, respectively. To facilitate the boundness analysis of the signals in the closed-loop system, a pertinent lemma about the dynamic loop gain function is proved. Finally, numerical simulations are employed to validate the effectiveness of the proposed scheme. • The spacecraft attitude tracking problem with disturbances and uncertainties. • The quantized control law alleviates communication burdens of spacecraft. • A dynamic loop gain function method is used to address the impact of uncertain. • A hyperbolic tangent term is devised to counteract bounded disturbances. [ABSTRACT FROM AUTHOR]

In this paper, we ponder a kind of discrete-time fractional-order complex-valued fuzzy BAM neural network. Firstly, in order to guarantee the quasi-projective synchronization of the considered networks, an original quantitative control strategy is designed. Next, by virtue of the relevant definitions and properties of the Mittag-Leffler function, we propose a novel discrete-time fractional-order Halanay inequality, which is more efficient for disposing of the discrete-time fractional-order models with time delays. Then, based on the new lemma, fractional-order h-difference theory, and comparison principle, we obtain some easy-to-verify synchronization criteria in terms of algebraic inequalities. Finally, numerical simulations are provided to check the accuracy of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the predefined-time synchronization of inertial memristive neural networks (IMNNs) is explored. Firstly, based on differential inclusion theory, the system with discontinuous connection weights is transformed into a differential inclusion. Secondly, two more general theorems are given to ensure that the zero solution of the error system is stable within a predefined time. Then, four quantized controllers with state variable and its first-order derivative are designed, which effectively avoids the complex analysis caused by reduced-order method and saves communication resources. Furthermore, some criteria of predefined-time synchronization for IMNNs are derived by using non-reduced order method and the new predefined-time stability theorem. Finally, the effectiveness of the obtained results is verified by a numerical simulation. [ABSTRACT FROM AUTHOR]

This paper addresses stability analysis and quantized stabilization of discrete‐time state‐dependent switching affine systems with quantized control inputs and state measurements. The employed controller is in a mode‐dependent affine form to improve the adaptation to the mode of the controlled system and then to acquire less conservative results. The quantization scheme is considered to be logarithmic and synchronized with the mode switching of the switching affine system and the controller. Aiming at achieving lower conservative criteria, a mode‐dependent Lyapunov function incorporating quantization uncertainties is constructed. Correspondingly, the stability analysis and the stabilizing control synthesis are implemented. In the end, the proposed quantized control strategy is applied to a vehicle path‐following problem to exemplify its effectiveness and robustness against data quantization in control inputs and state measurements. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this article, a novel robust adaptive neural network tracking control scheme is presented for a class of uncertain nonlinear systems with quantized inputs and full state constraints. A tan-type barrier Lyaponov function is proposed to constrain all states, and the unknown nonlinear function term in virtual control is approximated by radial basis function neural network(RBFNN). The uncertainty term and disturbance term in the system are dealt with by robust scheme. Under the proposed quantized tracking control scheme, the communication load of the system is reduced, the boundedness of all signals in the closed-loop system is verified, the full state constraints are satisfied. Simulation results are presented to illustrate the effectiveness of the proposed adaptive control scheme.

This paper investigates the problem of quantized cooperative output regulation of linear multi-agent systems with switching graphs. A novel dynamic encoding-decoding scheme with a finite communication bandwidth is designed. Leveraging this scheme, a distributed protocol is proposed, ensuring asymptotic convergence of the tracking error under both bounded and unbounded link failure durations. Compared with the existing quantized control work of MASs, the semi-global assumption of initial conditions is not required, and the communication graph is only required to be jointly connected. Finally, two simulation examples demonstrate the effectiveness of the proposed distributed protocol for bounded and unbounded link failure durations. [ABSTRACT FROM AUTHOR]

Quantized feedback control is fundamental to system synthesis with limited communication capacity. In sharp contrast to the existing literature on quantized control which requires an explicit dynamical model, the authors study the quadratic stabilization and performance control problems with logarithmically quantized feedback in a direct data-driven framework, where the system state matrix is not exactly known and instead, belongs to an ambiguity set that is directly constructed from a finite number of noisy system data. To this end, the authors firstly establish sufficient and necessary conditions via linear matrix inequalities for the existence of a common quantized controller that achieves our control objectives over the ambiguity set. Then, the authors provide necessary conditions on the data for the solvability of the LMIs, and determine the coarsest quantization density via semi-definite programming. The theoretical results are validated through numerical examples. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive finite-time controller for a quadrotor unmanned aerial vehicle (UAV) model with stochastic perturbations and parameter-unknown terms, under the constraints of a state-constrained system. The controller is designed based on full-state quantization, where the error system is defined to be a function of the quantized error signal. An adaptive method is employed to address the quadrotor UAV system model with nonlinear terms and unknown perturbations. The controller utilizes Barrier Lyapunov function (BLF) bounds with adaptive effective time performance to ensure full-state constraint of the system. The stability of the system is proven using Lyapunov’s stability theorem. The effectiveness of the designed full-state constrained controller for quadrotor UAV based on full-state quantization is verified through a physical experimental simulation platform.

We investigate the quasi-synchronization of fractional-order complex networks (FCNs) with random coupling via quantized control. Firstly, based on the logarithmic quantizer theory and the Lyapunov stability theory, a new quantized feedback controller, which can make all nodes of complex networks quasi-synchronization and eliminate the disturbance of random coupling in the system state, is designed under non-delay conditions. Secondly, we extend the theoretical results under non-delay conditions to time-varying delay conditions and design another form of quantization feedback controller to ensure that the network achieves quasi-synchronization. Furthermore, the error bound of quasi-synchronization is obtained. Finally, we verify the accuracy of our results using two numerical simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, synchronization issue of discrete-time fractional fuzzy neural networks (DFFNNs) with delays is solved via quantized control, and is applied in image encryption. Firstly, a novel fractional-order h -difference inequality which makes Lyapunov method more flexible and practical is strictly proved based on the properties of convex functions and theory of discrete fractional calculus. Secondly, by using compression mapping theorem and mathematical induction, we obtain two sufficient conditions to ensure the existence and uniqueness of solutions for DFFNNs. Whereafter, we design a suitable quantized controller, which not only saves channel resources but also reduces control costs. By utilizing our inequality and some analytical techniques, several conservative synchronization criteria for DFFNNs are acquired. Finally, two examples are arranged to illustrate the validity and practicability of our results. • An important fractional difference inequality is strictly proved, which contributes to constructing more flexible Lyapunov functions in the study of discrete-time fractional systems. • Two sufficient conditions are derived to guarantee the existence and uniqueness of the solution for DFFNN with delay by utilizing the compression mapping and mathematical induction. • Discrete-time fractional Halanay inequality is employed to explore synchronization of DFFNNs for the first time. • Under the quantized controller, some easily verifiable synchronization criteria related to step h are acquired. • Based on synchronization of DFFNNs, an image encryption algorithm is designed, and an example is given to verify the encryption effect of our designed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we ponder a kind of discrete-time fractional-order complex-valued fuzzy BAM neural network. Firstly, in order to guarantee the quasi-projective synchronization of the considered networks, an original quantitative control strategy is designed. Next, by virtue of the relevant definitions and properties of the Mittag-Leffler function, we propose a novel discrete-time fractional-order Halanay inequality, which is more efficient for disposing of the discrete-time fractional-order models with time delays. Then, based on the new lemma, fractional-order h-difference theory, and comparison principle, we obtain some easy-to-verify synchronization criteria in terms of algebraic inequalities. Finally, numerical simulations are provided to check the accuracy of the proposed theoretical results.

This paper considers the fixed-time synchronization of complex-valued coupled networks (CVCNs) with hybrid perturbations (nonlinear bounded external perturbations and stochastic perturbations). To accomplish the target of fixed-time synchronization, the CVCNs can be separated into their real and imaginary parts and establish real-valued subsystems, a novel quantized controller is designed to overcome the difficulties induced by complex parameters, variables, and disturbances. By means of the Lyapunov stability theorem and the properties of the Wiener process, some sufficient conditions are presented for the selection of control parameters to guarantee the fixed-time synchronization, and an upper bound of the setting time is also obtained, which is only related to parameters of both systems and the controller, not to the initial conditions of the systems. Finally, a numerical simulation is given to show the correctness of theoretical results and the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]

This brief discusses the use of quantized control with actuator saturation to achieve the local synchronization of master–slave fractional-order neural networks (FONNs). A refined sector condition (RSC) is proposed that addresses the issue of the simultaneous quantizer effects and actuator constraints. The RSC is used in the theoretical analysis of local synchronization in drive-response systems. The analysis employs inequality techniques on the Mittag–Leffler function and fractional-order Lyapunov theory. Additionally, this paper presents two convex optimization algorithms that aim to minimize the actuator's costs and expand the admissible initial area (AIA). Finally, this paper employs a three-neuron FONN to demonstrate the efficacy of the proposed methods. [ABSTRACT FROM AUTHOR]

This article considers the bipartite event‐triggered consensus problem for linear multi‐agent systems based on periodic sampling data. The cooperative and antagonistic communications are considered at the same time. The bipartite time‐triggered consensus with maximum sampling period is first addressed. Then, the bipartite periodic event‐triggered control strategy with two event‐triggered mechanisms is designed. Compared with previous control schemes, the proposed control method can realize intermittent communication, intermittent controller update and intermittent triggering condition monitoring. Moreover, considering the constraint of limited data rate, a quantized consensus with both state and input quantization is taken into account. The Zeno behavior is completely avoided due to the fact that the sampling time can be taken as the lower bound of trigger interval. Finally, three illustrative examples are provided to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In this article, a practical fixed-time adaptive neural network (NN) trajectory tracking control scheme for underactuated autonomous underwater vehicles (AUVs) subject to uncertain dynamics, unknown time-varying disturbances, an unknown dead zone, actuator faults and input quantization is developed for the first time. Here, a hysteresis quantizer is introduced to decrease the oscillation in the signal quantization process. Then, the radial basis function NN is employed to compensate the uncertainty term in the AUVs trajectory tracking control system. By incorporating the bounded estimate, smoothing functions and parameter adaptive technique, the problem of unknown dead zone, actuator fault and input quantization are addressed. The restrictive conditions of boundedness for the disturbance-like item in conventional sector bounded quantizer is resolved. Subsequently, a practical fixed-time adaptive NN trajectory tracking control law is designed does not require any parameter information of the quantizer under the backstepping design framework. The theoretical analysis further confirms that all signals in the AUV trajectory tracking closed-loop control system remain bounded, and the developed control scheme is shown to be effective through simulation results.

An adaptive neural network backstepping quantized control of fractional-order nonlinear systems with asymmetric actuator dead-zone and unknown external disturbance is investigated in this paper. An adaptive NN mechanism is designed to estimate uncertain functions. A command filter is introduced to estimate the virtual control variable as well as its derivative, so that the "explosion of complexity" problem existed in the classical backstepping method can be avoided. To handle the unknown external disturbance, a fractional-order disturbance observer is developed. Moreover, a hysteresis-type quantizer is used to quantify the final input signal to overcome the system performance damage caused by the actuator dead-zone. The quantized input signal can ensure that all the involved signals stay bounded and the tracking error converges to an arbitrarily small region of the origin. Finally, two examples are presented to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a distributed adaptive quantized bipartite containment neural network control strategy for nonlinear multi-agent systems with sensor faults. The radial basis function neural network is introduced to compensate the uncertainties. The saturation function is employed to achieve the asymmetric hysteresis quantization control, which can effectively eliminate the assumption that the quantizer parameters are bounded. In addition, an adaptive compensation technique is employed to handle the difficulty of sensor faults. Moreover, the second-order tracking differentiator is used to eliminate the repeated differential problem existed in the backstepping technique. Under the proposed control scheme, all signals of the closed-loop multi-agent systems are cooperatively semi-globally uniformly ultimately bounded, and the containment control performance is gained. Finally, two simulation examples are listed to demonstrate the validity of the presented control strategy. [ABSTRACT FROM AUTHOR]

This paper deals with the problems of quasi‐projective synchronization (QPS) and finite‐time synchronization (FTS) for a kind of delayed fractional‐order BAM neural networks (DFOBAMNNs). In order to reach the goals of synchronization and more accurately gauge of settling time and error level, several fresh quantized controllers are structured to make the utmost of confined communication resources. Then, based on the finite‐time theorem, quantized control strategy, Lyapunov function theory and properties of Mittag–Leffler function as well as inequality analysis techniques, some plentiful criteria are formed to set up a relation between control gains and quantization parameters. In addition, the corresponding error bound of QPS and guages of the settling time on FTS are also given. Finally, a few numerical examples are introduced to validate the effectiveness of the presented control protocols. [ABSTRACT FROM AUTHOR]

This paper is devoted to investigating the synchronization of fractional-order output-coupling multiplex networks (FOOCMNs). Firstly, a type of fractional-order multiplex network is introduced, where the intra-layer coupling and the inter-layer coupling are described separately, and nodes communicate with each other by their outputs, which is more realistic when the node states are unmeasured. By using the Lyapunov method and the fractional differential inequality, sufficient conditions are provided for achieving asymptotic synchronization based on the designed adaptive control, where the synchronized state of each layer is different. Furthermore, a quantized adaptive controller is developed to realize the synchronization of FOOCMNs, which effectively reduces signal transmission frequency and improves the effective utilization rate of network resources. Two numerical examples are given at last to support the theoretical analysis. [ABSTRACT FROM AUTHOR]

With the networking of control systems, cyberattacks on industrial control systems are becoming more and more frequent. In this study, the quantized attack-resilient control problem for Markov jump systems with sensor and actuator attacks and partly unknown transition rates was investigated. In the existing work, the forms of attack signals are mostly assumed directly. In order to release these assumptions, the mode-dependent monitor and logarithmic quantizer are designed to obtain the forms of attack signals and their constraints. Based on the constraints of attack signals, an adaptive filter is proposed to estimate the real system outputs, which are attacked by cyberattacks. Then, the adaptive compensator and quantized attack-resilient controller are designed to guarantee stochastic stability and H ∞ performance. Benefiting from the quantized attack-resilient control strategy, the proposed method can well combat the attacks, not only reducing the impact of the attacks, but also constraining the attack signals. Finally, an application example is presented to illustrate the obtained results. [ABSTRACT FROM AUTHOR]

Sampled-data control linear systems subject to uniform input quantization are considered. Within this context, the design of a stabilizing sampled-data state feedback controller is proposed. The proposed controller guarantees uniform global asymptotic stability of an attractor containing the origin of the plant. Due to the interplay of continuous-time dynamics and instantaneous changes in the state, the closed-loop system is modeled as a hybrid dynamical system. By relying on a quadratic clock-dependent Lyapunov function, sufficient conditions in the form of bilinear matrix inequalities are provided to ensure closed-loop stability. These conditions are employed to devise an optimal controller design algorithm based on the use of convex–concave decomposition approach. This leads to an iterative design algorithm based on the solution to a sequence of semidefinite programs for which feasibility is guaranteed. Some illustrative examples show the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

This paper concentrates on the study of adaptive control for the synchronization of fractional-order neural networks. Instead of classical adaptive control updating method, an intermittent-active updating strategy is proposed to adaptively tune the control gain in a fractional-order fashion. Moreover, quantization is brought into the control design to take into account the restricted bandwidth in signal transmission. Note that the suggested controller is basic yet effective in terms of the fractional-order system. The main theorem is established with the method of reduction to absurdity as well as Lyapunov stability theorem. Finally, simulation calculation is conducted to validate the effectiveness of our proposed method.

The finite-time synchronization of fractional-order multi-weighted complex networks (FMCNs) with uncertain parameters and external disturbances is studied. Firstly, based on fractional calculus characteristics and Lyapunov stability theory, quantized controllers are designed to guarantee that FMCNs can achieve synchronization in a limited time with and without coupling delay, respectively. Then, appropriate parameter update laws are obtained to identify the uncertain parameters in FMCNs. Finally, numerical simulation examples are given to validate the correctness of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the synchronization of multi-order fractional neural networks (MoFNNs) with time-varying delays is investigated. Two kinds of controls, namely continuous control and quantized control, are introduced respectively to implement the synchronization. Moreover, by virtue of vector Lyapunov functions, sufficient criteria for realizing the synchronization of the MoFNNs with time-varying delays are deduced. The results of this paper cover the synchronization of traditional fractional neural networks with identical derivative order as a special case. Finally, a numerical example with two cases is given to verify the theoretical results. [ABSTRACT FROM AUTHOR]

This paper considers the fixed-time synchronization of complex-valued coupled networks (CVCNs) with hybrid perturbations (nonlinear bounded external perturbations and stochastic perturbations). To accomplish the target of fixed-time synchronization, the CVCNs can be separated into their real and imaginary parts and establish real-valued subsystems, a novel quantized controller is designed to overcome the difficulties induced by complex parameters, variables, and disturbances. By means of the Lyapunov stability theorem and the properties of the Wiener process, some sufficient conditions are presented for the selection of control parameters to guarantee the fixed-time synchronization, and an upper bound of the setting time is also obtained, which is only related to parameters of both systems and the controller, not to the initial conditions of the systems. Finally, a numerical simulation is given to show the correctness of theoretical results and the effectiveness of the control strategy.

This brief discusses the use of quantized control with actuator saturation to achieve the local synchronization of master–slave fractional-order neural networks (FONNs). A refined sector condition (RSC) is proposed that addresses the issue of the simultaneous quantizer effects and actuator constraints. The RSC is used in the theoretical analysis of local synchronization in drive-response systems. The analysis employs inequality techniques on the Mittag–Leffler function and fractional-order Lyapunov theory. Additionally, this paper presents two convex optimization algorithms that aim to minimize the actuator’s costs and expand the admissible initial area (AIA). Finally, this paper employs a three-neuron FONN to demonstrate the efficacy of the proposed methods.

This article studies the quantized control problem of a scalar continuous‐time linear system over a digital network. The network is subject to bounded transmission delay and independent and identically distributed packet dropouts. Event‐triggered sampling is implemented in the system. We first derive a lower bound on the necessary bit rate for any event‐triggered strategy by analyzing the evolution of the uncertainty set of the system state. Then we propose a control strategy which combines both event‐triggering and time‐triggering to stabilize the system. Note that our control strategy implements a dynamic encoding policy, under which the number of quantization bits is dependent on dropouts. Although the average bit rate occupied by our control strategy is higher than the obtained lower stabilizing bit rate bound, their gap can arbitrarily approach zero by relaxing the integer requirement of the number of quantization bits. Numerical examples are given to illustrate the derived results. [ABSTRACT FROM AUTHOR]

A novel adaptive quantized predefined-time control scheme is proposed in this brief for a class of uncertain nonlinear strict-feedback systems. Composite state tracking errors and a composite estimation error are utilized in backstepping design with introducing two time-varying tuning functions. It is shown that the output tracking error can be steered to an arbitrarily small neighborhood of the origin within a user-predefined time, which is an exact design parameter. The provided upper bound of settling time is less conservative compared with related studies. A simulation example shows the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]

In this article, we investigate the quantized control problem of the continuous‐time linear system with performance barrier. The performance of the system is specified by a continuous decaying function. Considering the state quantization in the feedback, a dynamic quantization scheme and a control law with limited bandwidth are developed, such that the closed‐loop system is asymptotically stable and the specified performance barrier is satisfied. Based on the theory of control barrier function (CBF), the analysis on the performance guarantee of the closed‐loop system under quantization is established. Furthermore, the minimal bandwidth for the quantized system with the performance barrier is explicitly given. Finally, the effectiveness of the obtained result is illustrated by an example. [ABSTRACT FROM AUTHOR]

This article studies the $H_{\infty }$ exponential synchronization problem for complex networks with quantized control input. An aperiodic sampled-data-based event-triggered scheme is introduced to reduce the network workload. Based on the discrete-time Lyapunov theorem, a new method is adopted to solve the sampled-data problem. In view of the aforementioned method, several sufficient conditions to ensure the $H_{\infty }$ exponential synchronization are acquired. Numerical simulations show that the proposed control schemes can significantly reduce the amount of transmitted signals while preserving the desired system performance. [ABSTRACT FROM AUTHOR]

This article investigates the problem of quantized fuzzy control for discrete-time switched nonlinear singularly perturbed systems, where the singularly perturbed parameter (SPP) is employed to represent the degree of separation between the fast and slow states. Taking a full account of features in such switched nonlinear systems, the persistent dwell-time switching rule, the technique of singular perturbation and the interval type-2 Takagi–Sugeno fuzzy model are introduced. Then, by means of constructing SPP-dependent multiple Lyapunov-like functions, some sufficient conditions with the ability to ensure the stability and an expected $H_{\infty }$ performance of the closed-loop system are deduced. Afterward, through solving a convex optimization problem, the gains of the controller are obtained. Finally, the correctness of the proposed method and the effectiveness of the designed controller are demonstrated by an explained example. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive neural network event-triggered and quantized output feedback control scheme for quarter vehicle active suspensions with actuator saturation. The scheme uses neural networks to approximate the unknown parts of the active suspension. When the system states of the suspension are not entirely available, a state observer is designed to estimate the unknown states. The measurable system states, partially estimated observer states, neural network weights, and a filtered virtual control are sequentially event-triggered, quantified, and transmitted to the controller via in-vehicle networks. The problem of non-differentiable virtual control is solved using dynamic surface control technology in the backstepping quantized control design. Integrating a Gaussian error function and a first-order auxiliary subsystem compensates for the nonlinearity caused by asymmetric saturation. Theoretical analysis proves that all error signals of the closed-loop active suspension system are semi-globally uniformly ultimately bounded, and the Zeno phenomenon can be ruled out. Simulation results validate the effectiveness of the proposed control method. • The control scheme can realize dynamic event-triggered sampling and quantization. • A state observer is used to estimate the unavailable states of a suspension system. • The control scheme is easily implemented in automotive network systems. [ABSTRACT FROM AUTHOR]

This article studies the $\mathcal {H}_{\infty }$ control problem for linear networked and quantized control systems (NQCSs) with both communication delays and random packet losses. To deal with network-induced constraints and random packet dropouts, a novel discrete-time stochastic system model is developed for continuous-time networked control systems, and further overapproximated to a polytopic system with norm-bounded uncertainty. Based on the overapproximated system model, sufficient conditions are established for linear NQCSs in different cases to guarantee both input-to-state stability and $\mathcal {H}_{\infty }$ performance with respect to the network-induced errors. Furthermore, we propose an algorithm to minimize the stability gain and the $\mathcal {H}_{\infty }$ attenuation level simultaneously. Finally, a numerical example is given to illustrate the developed results. [ABSTRACT FROM AUTHOR]

Summary: The issue of fast finite‐time adaptive control is studied for quantized stochastic nonlinear systems. Unlike the existing works about fast finite‐time control, the input signals are quantized, and the stochastic disturbances and nonlinear functions are unknown. According to universal approximation capacity of fuzzy logic system, combined with backstepping technique, a novel fast finite‐time adaptive fuzzy control strategy of quantized stochastic nonlinear system is proposed. The nonlinear decomposition method is introduced to set up the relationship among the control signals and the quantization signals, which overcomes the technical difficulties result from the piecewise quantization input. The proposed tactics can assure the tracking error situate in a neighborhood of the origin point and the closed‐loop system signals keep bounded. Finally, an algorithm simulation is conducted to test the validity of the method. [ABSTRACT FROM AUTHOR]

This article tackles the finite-time bipartite synchronization (FTBS) of coupled competitive neural networks (CNNs) with switching parameters and time delay. Quantized control is utilized to achieve the FTBS at a small control cost and with limited channel resources. Since the effects of the time delay and switching parameters, traditional finite-time techniques cannot be directly utilized to the FTBS. By constructing a novel multiple Lyapunov functional (MLF), a sufficient criterion formulated by linear programming (LP) is established for the FTBS and the estimation of the settling time. To further improve the accuracy of the settling time, another MLF is designed by dividing the dwell time. With the aid of convex combination, a new LP is provided, which removes the requirement that the increment coefficient of the MLF at switching instants has to be larger than 1. In addition, to obtain the more precise settling time, an optimal algorithm is provided. Two numerical examples are put forward to demonstrate the reasonableness of the theoretical analysis. • The quantized controllers are constructed without using the error signals directly, which save communication resource and reduce the channel blocking greatly. • Different from most of existing algebraic inequalities, our finite-time results are formulated by LP, which are less conservative and enhance the accuracy of the estimation of the settling time. • The conservativeness of the FTBS criterion is further reduced by removing the usual requirement that the increment coefficient of the MLF at switching instants has to be greater than one. [ABSTRACT FROM AUTHOR]

This work focuses on the extended dissipative synchronization problem for chaotic neural networks with time delay under quantized control. The discretized Lyapunov–Krasovskii functional method, in combination with the free-weighting matrix approach, is employed to obtain an analysis result of the extended dissipativity with low conservatism. Then, with the help of several decoupling methods, a computationally tractable design approach is proposed for the needed quantized controller. Finally, two examples are provided to illustrate the usefulness of the present analysis and design methods, respectively. • The extended dissipative synchronization problem for chaotic time-delay neural networks under quantized control is investigated. • An analysis result of the extended dissipativity is derived by the discretized Lyapunov–Krasovskii functional method and the free-weighting matrix approach. • A computationally tractable design approach for the needed quantized controller is proposed by using some decoupling methods. [ABSTRACT FROM AUTHOR]

In this article, some new results of quantized discrete feedback control are revealed for stochastic delay systems via discrete-time state and mode observations (DSMO). Particularly, the coefficients of considered hybrid stochastic systems do not satisfy the linear growth condition (LGC). The main emphasis is to design a quantized feedback control law that ensures H ∞ stable and exponentially stable of the integrated systems. Based on DSMO, the desired controller can be fairly constructed. Finally, the correctness of presented results is testified by a numerical case. [ABSTRACT FROM AUTHOR]

This article investigates the coordination of a class of underactuated systems subject to limited energy supply and channel bandwidth and aims to stabilize system states and exclude Zeno behaviors simultaneously. First, by means of event-triggered (E-T) and quantized techniques, several novel quantized sampled-data-based E-T schemes are constructed, which only require discrete-time controller updates and partial quantized states, and thus efficiently mitigate the control and communication workloads. Then, in order to further lower the communication consumptions, several new triggered sampled-data-based communication rules under fixed and switched networks are established, where the communications are performed only at some specific instants and, thus, the ideally continuous-time signal transmission among neighbors can be avoided. Note that sufficient criteria for achieving the coordination of the underactuated systems are derived in terms of the Lyapunov–Krasovskii functional method. Finally, numerous simulations are carried out to demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This article focuses on static output feedback control for fuzzy Markovian switching singularly perturbed systems (FMSSPSs) with deception attacks and asynchronous quantized measurement output. Different from the previous work, both the logarithmic quantizer and the static output feedback controller are dependent on the operation system; by means of hidden Markov models, their modes run asynchronously with that of FMSSPSs. Additionally, the deception attacks are guided by a Bernoulli variable, and nonlinear characteristics are modeled by the Takagi–Sugeno fuzzy model. By resorting to a mode-dependent Lyapunov functional, several criteria are acquired and strictly $(\mathscr {Q},\mathscr {S},\mathscr {R})\text{--}\gamma$ -dissipative of FMSSPSs can be ensured. Finally, a dc motor model is expressed to illustrate the effectiveness of the asynchronous control scheme. [ABSTRACT FROM AUTHOR]