Event-triggered Input Research Articles

Accurate Finite-Time Motion Control of Hydraulic Actuators With Event-Triggered Input

Accurate Finite-Time Motion Control of Hydraulic Actuators With Event-Triggered Input

Adaptive fuzzy performance control based on a triggering mechanism with decreasing function and internal dynamic variable

The problem of fuzzy adaptive prescribed performance control with dynamic event-triggered input for a class of uncertain nonlinear systems is investigated in this article. A dynamic event-triggered scheme is proposed that integrates an internal dynamic variable and an exponentially decreasing function, effectively reducing unnecessary trigger events and minimizing the system of communication resources. Subsequently, a fuzzy estimator is designed to estimate the unmeasurable states of the system, while fuzzy logic systems are employed to approximate the uncertain nonlinear terms. The proposed scheme ensures semi-globally uniformly ultimately bounded (SGUUB) of the closed-loop system and converges the tracking error to a prescribed performance range based on a barrier Lyapunov function. Finally, a simulation example is given to justify the rationality of the proposed strategy.

Adaptive fuzzy event-triggered control for a class of nonlinear time-delay multi-agent systems with dead zone and partial state constraints

This paper presents an adaptive fuzzy event-triggered control (ETC) method for a class of time-delay nonlinear multi-agent systems (MASs) with dead zone (DZ) and partial state constraints (PSCs). It should be pointed out that the states considered in this paper are unmeasurable and part of the system states are constrained by time-varying boundary. By utilizing fuzzy logic systems (FLSs) and backstepping design framework, a fuzzy state observer is constructed to estimate the unmeasured states, while the introduction of a time-varying barrier Lyapunov function (BLF) addresses the partial state constrained problem. Furthermore, during the design process of the state observer, compensation for the influence of the unknown DZ input on the system is achieved through utilization of known designed event-triggered input signal and adaptive parameter. Building upon this foundation, employing the Lyapunov stability theory and adaptive backstepping technique with ETC strategy, the developed distributed control mechanism demonstrates that all variables of the closed-loop system are bounded, the consensus error of the system can converge to a small region of origin and Zeno behavior can be ruled out. Finally, two simulation examples further illustrate the proposed control strategy and theorem.

On State-Constrained Containment Control for Nonlinear Multiagent Systems Using Event-Triggered Input

On State-Constrained Containment Control for Nonlinear Multiagent Systems Using Event-Triggered Input

Decentralized Predictor Stabilization for Interconnected Networked Control Systems with Large Uncertain Delays and Event-Triggered Input

In this article, we propose a control scheme with predictors in a decentralized manner for coupled networked control systems (NCSs) under uncertain, large time-delays and event-triggered inputs. The network-induced delays are handled via the prediction; thus, the delay value is allowed to be large, and the burden of the network is relieved by the event-triggered input. Two methods are employed to deal with the large delay issue: the state and output feedback. When the state of each subsystem is measurable, full-state feedback is used, whereas when the plant state cannot be measured, output feedback is employed with the help of an observer, which is more common in practice. Instead of treating the interactive plants like a global system, the exponential stability of the coupled systems, under decentralized predictors with asynchronous sampled-data feedback, is analyzed in a decentralized way. Finally, the proposed methods are verified via an example of three interconnected cart–pendulum systems, while such systems would not be stabilizable by the traditional approach when the network-induced delays are relatively large.

Command filtered adaptive fuzzy tracking control for uncertain stochastic nonlinear systems with event-triggered input

Command filtered adaptive fuzzy tracking control for uncertain stochastic nonlinear systems with event-triggered input

Dynamic surface event-triggered control for unmanned sailboat path following based on adaptive tanh line-of-sight

In this article, a dynamic surface control algorithm based on the adaptive tanh line-of-sight with speed adjustment is proposed to solve the problem of path-following control of unmanned sailboat with event-triggered input, considering the dynamic model uncertainty and unknown external disturbance. First, the adaptive tanh line-of-sight method is used to improve the following accuracy and robustness, then an extended state observer is used to estimate the external disturbance and the uncertainty of the internal model, the heading error and the yaw angular velocity error are considered to design the recursive sliding mode dynamic surface controller, at the same time, the speed of the unmanned sailboat is constrained to improve its stability. In addition, to reduce the action frequency of the steering gear, the relative threshold event-triggered control is applied to the controller. According to the Lyapunov stability analysis, it is proved that the proposed control scheme can ensure that all errors in the closed-loop system are semi-globally consistent and ultimately bounded, while the Zeno behavior can be avoided. Finally, the effectiveness of the proposed scheme is verified by simulation and comparative experiments.

Adaptive Optimal Tracking Control for Spacecraft Formation Flying With Event-Triggered Input

Adaptive Optimal Tracking Control for Spacecraft Formation Flying With Event-Triggered Input

Event-Triggered Input–Output Finite-Time Stabilization for IT2 Fuzzy Systems Under Deception Attacks

This article addresses the issue of input–output finite-time stabilization for interval type-2 fuzzy systems in the presence of deception attack effects. Our main goal is to make efficient use of network resources by developing an event-triggered controller for interval type-2 fuzzy systems. For the stabilization process, an event-triggered controller that does not share the same membership functions as the system is designed by using affine transformation parameters. Following that, by using an asymmetric Lyapunov–Krasovskii functional and some advanced integral inequalities to establish the sufficient conditions for the existence of the proposed controller. Furthermore, an asymptotic stabilization result is presented and discussed in a comparative analysis as a special case. Due to the asymmetric Lyapunov–Krasovskii functional structure, the transmission delay interval is large compared with some recent studies. Finally, two simulation examples are carried out and the efficiency of designed controller is verified.

Leader-Following Exponential Consensus for Higher-Order Fractional-Order MASs With Mismatched Disturbances and Event-Triggered Input

The leader-following exponential output consensus (LFEOC) problem of nonlinear fractional-order MASs (FOMASs) with unknown mismatched disturbances and event-triggered control (ETC) input is investigated. First, a finite-time higher-order sliding observer is constructed for the estimation of the unknown matched and mismatched disturbances of each agent. Second, a distributed integral sliding-mode (ISM) surface is established to realize exponential stabilization of FOMASs, and a distributed event-based active anti-disturbance consensus protocol is designed to compensate the effects of unmatched uncertainties on the output and save energy. Third, it is proved that the output of all agents can be driven to achieve exponential consensus without Zeno behavior, and the corresponding event-triggered conditions are presented. Finally, the efficacy of conclusions is verified by the examples.

Event-based Predefined-time Deployment Control for Space Triangular Tethered Satellite System with Input Quantization

The problem of space triangular tethered satellite system deployment in the presence of external disturbances, input quantization, and limited communication from the controller to the actuator is investigated in this paper. A disturbancebased predefined time sliding mode controller is employed to ensure the system converges to the neighborhood of zero without depending on initial conditions. An input quantizer and an eventtriggered mechanism are integrated into the proposed controller to reduce the frequency and bandwidth of data transmission from controller to actuator. An adaptive law is considered to counteract the control input chattering produced by quantization errors and event-triggered input errors. The whole proposed control scheme guarantees that the closed-loop system is semi-globally stable and converges to a stable region at the predefined time. Finally, the effectiveness of the proposed scheme is confirmed by simulation tests.

Event-triggered adaptive neural command-filter-based dynamic surface control for state constrained nonlinear systems

In this article, an event-triggered adaptive neural command-filter-based dynamic surface control (CFDSC) is discussed for states constrained nonstrict-feedback nonlinear systems with dynamic uncertainties. The order-reduced compensation signals are designed to compensate the tracking error of the filter in original dynamic surface control (DSC). The hyperbolic tangent function is adopted as the invertible mapping to deal with the full state constraints. An auxiliary signal is employed to estimate the dynamical uncertainties produced by the unmodeled dynamics. The unknown smooth nonlinear terms are approximated by radial basis function neural networks (RBFNNs) at recursive each step. An event-triggered input is constructed in this novel CFDSC framework. With the help of the defined compact set in the stability analysis, the semi-global uniform ultimate boundedness (SGUUB) of all the signals in the adaptive closed-loop system is proved. Moreover, each state can be strictly limited within the time-varying state conditions. Two simulation verifications are employed to verify and clarify the theoretical findings.

Composite neural learning event-triggered control for dynamic positioning vehicles with the fault compensation mechanism

This paper focus on the novel composite neural learning event-triggered control for dynamic positioning vehicles with the fault compensation mechanism. In the proposed algorithm, the system uncertainties are tackled with by incorporating the dynamic surface control (DSC) and the robust neural damping techniques. Besides, the serial–parallel estimation model (SPEM) is developed to produce the prediction errors, which could be employed to derive the corresponding composite adaptive law. And the unknown actuator faults and gain uncertainties are effectively compensated for merits of the composite intelligent learning method. Furthermore, the idea of relative threshold strategy is utilized to construct the event-triggered mechanism. The event-triggered input could reduce the communication burden in the channel from controller to actuators. Through the direct Lyapunov theory, the parameters setting can be derived to guarantee the semi-global uniformly ultimately bounded (SGUUB) stability of all error signals in the closed-loop system. Finally, the effectiveness of the proposed algorithm is validated through the simulation experiments.

Fault Accommodation for a Class of Nonlinear Uncertain Systems With Event-Triggered Input

The event-triggered fault accommodation problem for a class of nonlinear uncertain systems is considered in this paper. The control signal transmission from the controller to the system is determined by an event-triggering scheme with relative and constant triggering thresholds. Considering the event-induced control input error and system fault threat, a novel event-triggered active fault accommodation scheme is designed, which consists of an event-triggered nominal controller for the time period before detecting the occurrence of faults and an adaptive approximation based event-triggered fault accommodation scheme for handling the unknown faults after detecting the occurrence of faults. The closed-loop stability and inter-event time of the proposed fault accommodation scheme are rigorously analyzed. Special cases for the fault accommodation design under constant triggering threshold are also derived. An example is employed to illustrate the effectiveness of the proposed fault accommodation scheme.

Control of high-order nonlinear systems under error-to-actuator based event-triggered framework

This paper solves the finite-time tracking problem of a class of high-order nonlinear systems under event-triggered input. Unlike existing event-triggered frameworks based on signal transmission channels from the sensor to the controller or from the controller to the actuator, these developed trigger mechanisms only compare the difference between the signal to be transmitted and the holding signal. By introducing internal trigger conditions and external trigger conditions, a novel error-to-actuator based event-triggered framework is proposed. It further considers the response of the trigger mechanism to system control performance such that the tracking performance of the system can be guaranteed while reducing the number of signal transmissions. In addition, filter-based techniques (such as the dynamic surface control method), for the first time, are utilised to eliminate some strong constraints that exist in the literature for most high-order nonlinear systems. The effectiveness of the proposed approach is evaluated on simulation examples including comparative studies and a practical example.

Adaptive neural fault-tolerant control for course tracking of unmanned surface vehicle with event-triggered input

This study investigates the course-tracking problem for the unmanned surface vehicle in the presence of constraints of the actuator faults, control gain uncertainties, and environmental disturbance. A novel event-triggered robust neural control algorithm is proposed by fusing the robust neural damping technique and the event-triggered input mechanism. In the algorithm, no prior information of the system model about the unknown yawing dynamic parameters and unknown external disturbances is required. The transmission burden between the controller and the actuator could be relieved. Moreover, the control gain-related uncertainties and the unknown actuator faults are compensated through two updated online adaptive parameters. Sufficient effort has been made to verify the semi-global uniform ultimate bounded stability for the closed-loop system based on Lyapunov stability theory. Finally, simulation results are presented to illustrate the effectiveness and superiority of the proposed algorithm.

LVS guidance principle and adaptive neural fault-tolerant formation control for underactuated vehicles with the event-triggered input

This paper presents a novel adaptive neural event-triggered formation control strategy for underactuated surface vehicles (USVs) in the presence of marine practical constraints, i.e. the waypoints-based planned path and actuator failures. The proposed strategy is composed of the guidance model and the control one. For the guidance part, the logic virtual ship (LVS) guidance principle is developed to implement the reference path programming for the formation pattern. For merit of the adaptive virtual ship, the smooth reference can be produced for followers without using the velocity information of leader. Furthermore, the proposed adaptive neural fault-tolerant control algorithm is to guarantee that the followers can maintain their pattern and converge to the corresponding reference path. In the algorithm, the neural networks (NNs)-based observer is designed to estimate the unmeasurable velocities of followers online. With the input event-triggered mechanism, two adaptive parameters are derived to compensate for the gain uncertainty and actuator failure. And that could effectively reduce the communication burden for the channel from controller to actuator. The derived closed-loop system has been proved to be with the semi-global uniform ultimate bounded (SGUUB) stability. Finally, the superiority of the proposed strategy can be verified by the simulation experiments.

Fuzzy adaptive event-triggered output feedback control for nonlinear systems with tracking error constrained and unknown dead-zone

ABSTRACT In this paper, an adaptive fuzzy event-triggered output feedback control scheme is studied for a class of non-strict feedback systems with tracking error constrained and unknown dead-zone. Fuzzy logical systems (FLSs) are employed to solve the uncertainties of the controlled plant, and the immeasurable states are estimated by employing the fuzzy observer. In the designed observer, to compensate for the effect of unknown dead-zone by invoking the known designed event-triggered input signal, an adaptive parameter is designed. Furthermore, due to event-triggered control (ETC) places a great burden on the computing space, the dynamic surface control (DSC) technique is used to solve the problem of ‘explosion of complexity’ in each step, and the simplified barrier Lyapunov function (SBLF) is utilised to restrict the tracking error within the prescribed boundaries. Consequently, the designed parameter adaptive laws and event-triggered controller can ensure that all signals of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Ultimately, two numerical simulation examples are provided to illustrate the effectiveness of the proposed event-triggered control theory.

Event-triggered decentralised control for large-scale systems with actuator failures

ABSTRACT This paper investigates the trajectory tracking problem for large-scale nonlinear systems in lower triangular form by employing the technology of static gain. For one thing, in the interest of improving the fault tolerance of the concerned system, a novel adaptive compensation method is considered in the tracking strategy for dealing with actuator failures. For another, in order to alleviate the communication burden and maintain satisfactory systems performance, the control method for handling event-triggered input is also added in the tracking strategy. Besides, this paper also proves that the designed strategy can achieve the control goal for large-scale nonlinear systems with bounded disturbance. The simulation example of the machine feed system is given to verify the validity of the proposed strategy.

Adaptive Fuzzy Consensus Tracking Control for Uncertain Fractional-Order Multiagent Systems With Event-Triggered Input

This article focuses on the problem of adaptive fuzzy consensus tracking control with event-triggered input for a class of fractional-order multiagent systems, where systems are considered to be with unmeasurable states, mismatched uncertain external disturbances, and unknown nonlinearities. An event-triggered mechanism with a decreasing threshold function is proposed in this article, which mitigates communication burden and avoids large control impulse. To achieve better tracking performance in distributed control strategy, a compensating fractional-order adaptation law associated with consensus tracking error is introduced when agents cannot obtain the information of reference trajectory directly. Under the fractional-order stability theory, a novel distributed adaptive event-triggered controller with fuzzy observers is constructed, which ensures the boundedness of tracking errors. A simulation example demonstrates the correctness of the presented scheme.