Hysteresis Quantizer Research Articles

Finite-time adaptive fuzzy event-triggered control for nonlinear time-delay system with input quantization via command filter

This paper investigates the problem of finite-time adaptive fuzzy event-triggered control for a class of uncertain nonlinear systems with input quantization. The nonlinear systems under consideration contain unmodeled dynamics and time-varying delays. Fuzzy logic systems are used to handle unknown nonlinear terms. Additionally, finite-time filters are introduced to solve the problem of “explosion of complexity” as well as to ensure that the derivative of the virtual controller can be approximated in finite time. The Lyapunov–Krasovskii function is used to handle time-varying delays. The hysteresis quantizer is used to ensure adequate precision when there is a low transmission rate requirement. The control strategy combines event-triggered mechanisms and quantization technology to ensure that all signals of the closed-loop system remain bounded in finite time. Finally, two examples are included to demonstrate the effectiveness of the proposed controller.

Adaptive Neural Cooperative Control of Multirobot Systems With Input Quantization.

This article develops the adaptive neural cooperative control scheme for a group of mobile robots with a limited sensing range in presence of input quantization by a dynamic surface control technique. First, to make the controller design feasible, the original robotic system is transformed into a new fully actuated system using a transverse function. Then, taking into consideration the effects of a hysteresis quantizer, an adaptive neural cooperative controller is developed based on the universal approximation property of the radial basis function neural networks and the connectivity preservation strategy. Furthermore, the proposed control scheme can guarantee that all closed-loop signals are semi-globally uniformly ultimately bounded. Meanwhile, desired constraints are not breached and tracking errors are within the predefined domains. Finally, several simulation results are carried out to testify the feasibility and efficiency of the theoretical findings revealed in this article.

Quantized control for interconnected PDE systems via mobile measurement and control strategies

This paper deals with the Rϵ-stabilization for interconnected partial differential equation systems (IPDESs) by mobile measurement and control strategies. First, compared to the static measurement and control strategies, the mobile ones are employed to improve the dynamic response of the closed-loop system, while reducing the number of sensors and actuators effectively. Moreover, considering the constrained transmission channel bandwidth, the hysteresis quantizer with an adjustable parameter is introduced to save the communication resources in the network, while can balance the quantization effect and the control performance by adjusting the parameter. Further, the output quantization controller is designed to ensure that the closed-loop system is Rϵ-stable, which converges to a given interval in finite time. Finally, a simulation example of chemical non-isothermal tubular reactors is provided to illustrate the validity of the presented design method.

Adaptive decentralized fixed‐time neural control for constrained strong interconnected nonlinear systems with input quantization

AbstractThis article investigates the problem of adaptive decentralized fixed‐time tracking control for strong interconnected nonlinear systems with full‐state constraints and input quantization. During the control design process, the assumption that the strong interconnection terms are bounded is removed via an inherent feature of the Gaussian function in neural networks. Unlike presvious nonlinear state‐dependent function (NSDF) that can only handle a single constraint, a novel form of NSDF is introduced to cope with more types of state constraints in this article. Meanwhile, the introduced NSDF is still available when the system states are unconstrained. Simultaneously, quantized input is directly handled by utilizing the intrinsic characteristics of the hysteresis quantizer. Then, based on the Lyapunov stability theory, all signals in the closed‐loop systems and tracking error are guaranteed to be bounded within fixed‐time. Finally, the feasibility of the proposed control scheme is illustrated by simulation results.

Adaptive neural quantized control for full-state constrained Markov jumping nonlinear systems with incomplete transition probabilities and unknown control directions

This article devises an adaptive quantized control scheme for full-state constrained Markov jumping nonlinear systems with incomplete transition probabilities and unknown control directions. First, the barrier Lyapunov functions were utilized to achieve full-state constraints on the investigated system, while the Nussbaum function has been adopted to overcome the issue of unknown control directions. Then, by means of command filtered backstepping control technique, an adaptive quantized controller is developed, where an improved error compensation mechanism was established to eliminate the effect of filter error, and a hysteresis quantizer was employed to diminish the transmission rate. Furthermore, it is demonstrated that the designed controller assures that all signals of the closed-loop system are bounded in the mean square sense. Finally, two illustrative examples were provided to validate the effectiveness of the proposed control method.

Command filter based input quantized adaptive tracking control for multi‐input and multi‐output non‐strict feedback systems with unmodeled dynamics and full state time‐varying constraints

SummaryThis paper addresses the problem of adaptive tracking control for multi‐input and multi‐output (MIMO) non‐strict feedback systems with unmodeled dynamics and full state time‐varying constraints. To tackle the interference of unmodeled dynamics, the dynamic signal generated by the auxiliary system is used. Hyperbolic tangent function is used as a nonlinear mapping tool to transform the constrained system into an unconstrained one. Hysteresis quantizer is introduced to mitigate the chattering phenomenon and quantization error in the quantization signal. The derivative of virtual signal can be approximated more efficiently by command filter. Furthermore, an error compensation mechanism is established to mitigate the error introduced by the command filter. Unknown nonlinear functions are approximated by radial basis function neural networks (RBFNNs). Stability analysis of the proposed controller is performed through the Lyapunov stability theory and the output tracking error can be constrained within a specified range. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method.

Fixed-time adaptive neural quantized formation control of USVs with tunnel prescribed performance

This paper investigates fixed-time adaptive neural quantized formation control of unmanned surface vehicles (USVs) with tunnel prescribed performance in the presence of leader’s information unknown, model dynamic uncertainties and input quantization. Initially, a fixed-time observer is constructed to rapidly estimate the leader’s unknown information. Following this, a control strategy is designed employing a fixed-time sliding mode differentiator-based backstepping technique combined with a neural network-based minimum parameter learning approach (MLP-NN). This strategy addresses the ”complexity explosion” issue arising from the differentiation of the virtual control law and model dynamic uncertainties separately. Moreover, a specialized prescribed performance function is incorporated to ensure that performance specifications of the formation tracking errors meet specific performance criteria. The integration of hysteresis quantizer aids in conserving save communication resources and significantly reduces the detrimental effects caused by quantization errors through the proposed control methodology. It is demonstrated that all closed-loop signals are practical fixed-time stable and formation tracking errors consistently adhere to the predetermined performance envelopes. The efficacy of proposed control strategy is validated through comprehensive numerical simulations.

Nonsingular adaptive finite-time consensus control for uncertain nonlinear multi-agent systems with input quantization

This paperinvestigates the distributed consensus control for nonlinear multi-agent systems with input quantization in a directed communication topology. A nonsingular adaptive finite-time control (NAFTC) scheme is proposed by combining the filtered backstepping method with neural network control. Both chattering and singularity problems of the control signals are avoided thanks to its [Formula: see text] continuity, so that the contradiction between discontinuous- and [Formula: see text] continuous finite-time control with input quantization does not exist in our scheme. At the same time, a novel inequality for the input-output relationship of hysteresis quantizer is derived, which can simply handle the input quantization without requiring additional stability analysis. What is more, some common assumptions, such as Lipschitz assumption and Holder-continuity assumption, are not required in our scheme. Finally, stability analysis and numerical simulation are provided.

Neural-Network-Based Adaptive Fixed-Time Control for a 2-DOF Helicopter System With Input Quantization and Output Constraints.

This study proposes a neural-network (NN)-based adaptive fixed-time control method for a two-degree-of-freedom (2-DOF) nonlinear helicopter system with input quantization and output constraints. First, a hysteresis quantizer is employed to mitigate chattering during signal quantization, and adaptive variables are utilized to eliminate errors in the quantization process. Subsequently, the system uncertainties are approximated using a radial basis function NN. Simultaneously, a logarithmic barrier Lyapunov function (BLF) is constructed to prevent the system outputs from violating the constraint boundaries. Based on a rigorous Lyapunov stability analysis and the fixed-time stability criterion, the signals of the closed-loop system are proven to be bounded within a fixed time. Finally, numerical simulations and experiments verified the feasibility of the proposed method.

Finite-time adaptive prescribed performance DSC for pure feedback nonlinear systems with input quantization and unmodeled dynamics

<abstract><p>This paper presents a new prescribed performance-based finite-time adaptive tracking control scheme for a class of pure-feedback nonlinear systems with input quantization and dynamical uncertainties. To process the input signal, a new quantizer combining the advantages of a hysteresis quantizer and uniform quantizer has been used. Radial basis function neural networks have been utilized to approximate unknown nonlinear smooth functions. An auxiliary system has been employed to estimate unmodeled dynamics by producing a dynamic signal. By introducing a hyperbolic tangent function and performance function, the tracking error was made to fall within the prescribed time-varying constraints. Using modified dynamic surface control (DSC) technology and a finite-time control method, a novel finite-time controller has been designed, and the singularity problem of differentiating each virtual control scheme in the existing finite-time control scheme has been removed. Theoretical analysis shows that all signals in the closed-loop system are semi-globally practically finite-time stable, and that the tracking error converges to a prescribed time-varying region. Simulation results for two numerical examples have been provided to illustrate the validity of the proposed control method.</p></abstract>

Adaptive Fuzzy Quantized Control for a Cooperative USV-UAV System Based on Asynchronous Separate Guidance

This paper focuses predominantly on the multi-tasks carried out by the cooperative unmanned surface vehicle-unmanned aerial vehicle (USV-UAV) system in which the input quantization is considered. The proposed cooperative scheme consists of the asynchronous separate guidance and adaptive fuzzy quantized control algorithm. The proposed guidance law takes full advantage of subsystems whilst considering the maneuverability of these subsystems in order to achieve the goal of executing multi-tasks. In contrast to previous guidance laws, although the same waypoint path is planned, the calculation for guidance law proposed is based on speed rather than time, which in reality is more relevant. As for the controls, an adaptive fuzzy quantized controller was developed to reduce undue exertion on the actuator. By fusing the dynamic surface control (DSC) and fuzzy logic system (FLS), a hysteresis quantizer has been introduced to reduce the transmission load. By properly adjusting the quantization density, the number of quantizations was reduced whilst maintaining a favorable control performance. All of the stated variables are semi-global uniform ultimate bounded (SGUUB) and the stability of the USV-UAV system is proofed through the Lyapunov theorem. Finally, the advantages of the proposed scheme are evaluated by two simulative experiments, exhibiting the favorable tracking accuracy and reduced wear on the actuators.

Anti-Attack Event-Triggered Control for Nonlinear Multi-Agent Systems With Input Quantization.

In this article, an anti-attack event-triggered secure control scheme for a class of nonlinear multi-agent systems with input quantization is developed. With the help of neural networks approximating unknown nonlinear functions, unknown states are obtained by designing an adaptive neural state observer. Then, a relative threshold event-triggered control strategy is introduced to save communication resources including network bandwidth and computational capabilities. Furthermore, a quantizer is employed to provide sufficient accuracy under the requirement of a low transmission rate, which is represented by the so-called a hysteresis quantizer. Meanwhile, to resist attacks in the multi-agent network, a predictor is designed to record whether an edge is attacked or not. Through the Lyapunov analysis, the proposed secure control protocol can ensure that all the closed-loop signals remain bounded under attacks. Finally, the effectiveness of the designed scheme is verified by simulation results.

Distributed adaptive control for multiple unmanned aerial vehicles with state constraints and input quantization

AbstractThis paper investigates the distributed fault‐tolerant tracking control (FTTC) problem for multiple unmanned aerial vehicles (UAVs) in the presence of state constraints, input quantization and actuator failures. The unified barrier function (UBF) is first developed to convert the original constrained system model into an unconstrained one, which accomplishes the dynamic constraints of all states. Moreover, a cerebellar model neural network (CMNN) is introduced to estimate the lumped effect of unknown disturbances and actuator failures. Then, a distributed formation flight fault‐tolerant controller (FTC) is designed to guide the UAVs along the desired trajectories based on sliding mode manifold and dynamic surface control technique. Meanwhile, the unknown input chattering and hysteresis quantizer errors in the UAVs system are simultaneously handled by adding the compensation term of FTC. Furthermore, the finite time stability analysis is implemented for all tracking errors and synchronization errors of the cooperative flight control system (CFCS). Finally, a comparative simulation is conducted to demonstrate the effectiveness of the FTTC scheme proposed in this study.

Output Synchronization of Heterogeneous Nonlinear Multiagent Systems With Input Quantization: A Universal Performance Guaranteed Control Scheme

This paper investigates the synchronization control for a class of heterogeneous nonlinear multi-agent systems with a directed communication topology. The practical problems of input quantization, uncertain dynamics and external disturbances are taken into account. A performance guaranteed distributed control scheme is developed by proposing a universal error transformation and applying neural network under the backstepping framework. The idea of minimum learning parameter is adopted and the sector characteristic of the hysteresis quantizer is cleverly used, so that the computational complexity of the control law is reduced. A reasonable relationship between the local synchronization error and the transformed error is established by the proposed error transformation, which is the key to solve the problem that the agents with different quantizers can achieve synchronization and meet the prescribed performance. All signals in the closed-loop system are proved to be uniformly ultimately bounded based on Lyapunov theory. Finally, the effectiveness of the proposed control law is verified by a numerical simulation example.

Adaptive Asymptotic Tracking Control for Input-Quantized Nonlinear Systems With Multiple Unknown Control Directions.

This study mainly concentrates on adaptive asymptotic tracking control for input-quantized strict-feedback nonlinear systems subjected to multiple unknown control directions. Novel improved lemmas, which relax the conditions for handling unknown control coefficients in the existing theoretical results, are certificated that can be applied to resolve the tracking problem for nonlinear systems under input quantification and unknown control directions simultaneously. Furthermore, by incorporating positive integral time-varying functions and the disintegration of the hysteresis quantizer into the controller design, the asymptotic tracking control is successfully achieved. Moreover, all signals in the closed-loop system are guaranteed to be bounded. Ultimately, a comparing numerical simulation and a practical simulation of a Nomoto ship model are presented to validate the feasibility of the proposed control algorithm.

Hysteresis quantified control for switched reaction–diffusion systems and its application

This article addresses the exponential input-to-state stabilization problem for switched reaction–diffusion systems, in which the systems’ mode jumping complies with the persistent dwell-time switching mechanism. On the basis of point measurement, a novel pointwise controller is designed to reduce the amount of sensors and actuators. Then, a hysteresis quantizer with an adjustable parameter is employed to balance the quantitative effect and system’s performance, which can improve the bandwidth utilization of the network, simultaneously. Finally, the effectiveness of the proposed approach is demonstrated by an application of the temperature control of power semiconductor chips.

Adaptive Sliding Mode Control for Unmanned Surface Vehicles with Predefined-Time Tracking Performances

This paper is concerned with the trajectory tracking control of unmanned surface vehicles (USVs) subject to input quantization, actuator faults and dead zones. In scenarios with dense marine facilities, there are constraints on the tracking performance and convergence time of USVs. First, the designed control signal is quantized by a hysteresis quantizer to reduce the transmission rate. Second, to guarantee the transient and steady-state tracking performance of the USV, a prescribed performance control technology with a predefined settling time is employed. Third, a predefined-time adaptive sliding mode control (SMC) method is designed by integrating the auxiliary function and the barrier function. Moreover, the lumped uncertainties caused by quantization, actuator faults, and dead zones are simultaneously processed using control gain based on barrier function. The proposed control method guarantees that the tracking error and sliding variable converge to the corresponding predefined bounds within a predefined time. The predefined bounds are independent of the upper bound on the lumped uncertainty. The stability of the controlled system is proven via the Lyapunov theorem. Finally, the effectiveness of the designed controller is verified by numerical simulations.

Decentralized Adaptive Quantized Dynamic Surface Control for a Class of Flexible Hypersonic Flight Vehicles with Input Quantization

A control strategy for a certain class of hypersonic flight aircraft dynamic models with unknown parameters is proposed in this article. The strategy is adaptive dynamic surface input quantization control. To address the issues in conventional inversion control, a first-order low-pass filter and an adaptive parameter minimum learning law are introduced in the control system design process. This method has the following features: (1) it solves the problem of repeated differentiation of the virtual control law in the conventional back-stepping method, greatly simplifying the control law structure; (2) by using the norm of the neural network weight vector as the adaptive adjustment parameter instead of updating each element online, the number of adaptive adjustment parameters is significantly reduced, improving the execution efficiency of the controller; (3) the introduced hysteresis quantizer overcomes the disadvantage of the quantization accuracy deterioration when the input value is too low in the logarithm quantizer, improving the accuracy of the quantizer. Stability analysis has shown that all signals in the closed-loop system are semi-globally uniformly bounded, and simulation results have verified the effectiveness of the proposed adaptive quantized control scheme.

Distributed consensus of heterogeneous switched nonlinear multiagent systems with input quantization and DoS attacks

In this paper, the consensus tracking control problem is investigated for a class of heterogeneous switched nonlinear multiagent systems (MASs) with input quantization under denial-of-service (DoS) attacks, where first-order agents and second-order agents exist simultaneously, and each agent shows the switching characteristic. Due to the prevalence of DoS attacks in communication channels, a DoS attack model is constructed in this study, and the upper bounds on the duration and frequency of DoS attacks are given. Meanwhile, taking the chattering phenomena of control inputs into account, a hysteresis quantizer is introduced to overcome the issue of the undesirable input chattering. Hence, a new anti-attack distributed consensus protocol is developed such that MASs with input quantization can effectively resist the impact of DoS attacks. Based on the Lyapunov stability theory, the boundedness of all signals in the closed-loop system is verified and the consensus target can be achieved. Finally, the effectiveness of the proposed control scheme is demonstrated by two simulation examples.

Prescribed performance fault‐tolerant attitude control for flexible spacecraft under limited communication network

AbstractThis paper investigates the prescribed performance fault‐tolerant attitude tracking control for flexible spacecraft under limited communication network, where the controller and the actuator are connected via wireless network. The hysteresis quantizer is employed to quantize the control signal, which can reduce the communication burden of the network onboard. First, a novel iterative learning observer is developed by combining with neural‐network approximation method to reconstruct the actuator faults and estimate the unmeasurable nonlinear rigid‐flexible dynamics simultaneously. Then, the signal quantization technique is introduced for control command quantization, and the concerned parametric quantization error is given. Based on the learning observer output, prescribed performance design procedure, and a quantization error compensation method, an active fault‐tolerant control strategy is developed for flexible spacecraft attitude tracking to deal with actuator faults, unmeasurable system nonlinearity, quantization errors, and external disturbances. The stability analysis of the closed‐loop system proves that the quantization errors, modal vibrations, disturbances can be effectively rejected, moreover, the convergence of the transformed performance states and angular velocity states can be guaranteed, which achieves the tracking problem with predetermined transient‐state and steady‐state performance requirements. Finally, a numerical simulation is conducted to illustrate the validness of the proposed control strategy.