Adaptive Event-triggered Control Research Articles

Predefined-Time Event-Triggered Adaptive Fuzzy Formation Control for Nonholonomic Multirobot Systems

Predefined-Time Event-Triggered Adaptive Fuzzy Formation Control for Nonholonomic Multirobot Systems

Dynamic Event-Triggered Adaptive Fixed-Time Practical Tracking Control for Nonlinear Systems Through Funnel Function

Dynamic Event-Triggered Adaptive Fixed-Time Practical Tracking Control for Nonlinear Systems Through Funnel Function

Neighbor event-triggered adaptive distributed control for multiagent systems with dead-zone inputs

<abstract><p>The paper focused on the distributed tracking problem for a specific class of multi-agent systems, characterized by bandwidth constraint and dead zone actuators, where the bandwidth limitations exist in neighbor agents and the dead zone nonlinearity refers to a generalized mathematical model. Initially, a series of event-triggered mechanisms with relative thresholds were established for neighbor agents, ensuring that control signals were transmitted only when necessary. Next, the generalized dead zone models were decomposed into two parts: indefinite terms with control coefficients and disturbance-like terms, resulting in unpredictability and damaging effects. Subsequently, based on the backstepping procedure, final consensus controllers with multiple polynomial compensators were constructed. These controllers offset the coupling coefficients caused by event-triggered mechanisms and dead zone non-smooth. Stability analysis was given to substantiate the theoretical correctness of this method and support the claim of Zeno behavior avoidance. Finally, simulation studies were performed for the feasibility of our proposed methodology.</p></abstract>

A New Event-Triggered Adaptive Fixed-Time Control Design for Uncertain Nonlinear Systems.

This article investigates the problem of dynamic memory event-triggered (DMET) fixed-time tracking control within time-varying asymmetric constraints for nonaffine nonstrict-feedback uncertain nonlinear systems with unmodeled dynamics and unknown disturbances. The existing dynamic event-triggered control methods cannot handle the nonlinear systems with unmodeled dynamics and nonaffine inputs, which greatly limits the applicability of the strategy. To this end, a novel DMET adaptive fuzzy fixed-time control protocol is constructed based on the idea of command filtered backstepping, in which a new dynamic signal function is established to deal with the unmodeled dynamics and an improved DMET mechanism (DMETM) is designed to solve the problem of nonaffine inputs. It is proved that the newly DMET control strategy ensures the tracking error converges to an arbitrarily small compact set in a fixed time and all the signals of the closed-loop systems are bounded. The effectiveness of the proposed approach is demonstrated by two simulation examples.

Event-triggered Adaptive Tracking Control for Stochastic Nonlinear Systems With State Constraints

Event-triggered Adaptive Tracking Control for Stochastic Nonlinear Systems With State Constraints

Decentralized Dynamic Event-Triggered Output Feedback Adaptive Fixed-Time Funnel Control for Interconnection Nonlinear systems.

A decentralized dynamic event-triggered output feedback adaptive fixed-time (DDETOFAFxT) funnel controller is described for a class of interconnected nonlinear systems (INSs). A novel dynamic event-triggered mechanism is designed, which includes a triggering control input, fixed threshold, decreasing function of tracking error, and a dynamic variable. To obtain the unknown states, a decentralized linear filter is designed. By introducing a prescribed funnel and using an adding a power integrator technique and a neural network method, a DDETOFAFxT funnel controller is designed to obtain better tracking performance and effectively alleviate the computational burden. Furthermore, it is ensured that the tracking error falls into a preset performance funnel. A simulation example is presented to demonstrate the availability of the designed control scheme.

A Novel Adaptive Event-Triggered Consensus Control Approach to Multi-Agent Systems With Guaranteed Positive MIET

A Novel Adaptive Event-Triggered Consensus Control Approach to Multi-Agent Systems With Guaranteed Positive MIET

Exponential Asynchronous Stabilization for Delayed Semi-Markovian Neural Networks via DAEIC.

The exponential asynchronous stabilization (EAS) issue for a category of neural networks (NNs) with semi-Markov jump (SMJ) parameters and additive time-varying delays (ATDs) is addressed in this article. Here, the SMJ parameters in the controller gain are supposed to be distinct from those in the system structure, which is more consistent with the actual situation. To further relieve the communication load of the network, a new discrete adaptive event-triggered impulsive control (DAEIC) scheme is proposed, where the impulsive moments are the sampling instants satisfying event-triggered constraints, and the triggering threshold can be dynamically adjusted by an adaptive update rule (AUR) related to the current sampling state and the last triggered state. A more flexible looped Lyapunov-Krasovski functional (LLKF) is constructed to commendably capture the available information about impulsive instants, triggering state, sampling interval, ATDs, and heterogeneous SMJ parameters. Combined with the LLKF, DAEIC scheme, and other inequality analysis approaches, some novel results guaranteeing the EAS of the underlying systems are exported. Finally, three explanatory examples are presented to check the validity of our results.

Adaptive Event-Triggered Fuzzy Control for Stochastic Highly Nonlinear Systems With Time Delay and Nontriangular Structure Interconnections

The problem of adaptive fuzzy control based on the switching event-triggered mechanism for stochastic highly nonlinear system with time-varying delays, actuator faults and non-triangular structure interconnections is investigated in this article. By constructing the corresponding Lyapunov-Krasovskii functional and variable separation method, the complex nonlin- ear functions with time-varying delays and highly nonlinearity can be handled. With the help of the fuzzy logic systems and adaptive compensation technique, the processing difficulty of the unknown nonlinear terms and the parameters of actuator faults is reduced. Simultaneously, the proposed control scheme can greatly decrease the waste of communication resources by a switching event-triggered mechanism. Then, all signals in the system are ensured to be bounded under Lyapunov stability theorem. Ultimately, the simulation example demonstrates the availability of the developed control scheme.

Adaptive Event-Triggered Fixed-Time Fault-Tolerant Consensus Control for a Class of Multiagent Systems

Adaptive Event-Triggered Fixed-Time Fault-Tolerant Consensus Control for a Class of Multiagent Systems

Adaptive Event-Triggered PIO-Based Anti-Disturbance Fault-Tolerant Control for MASs With Process and Sensor Faults

Adaptive Event-Triggered PIO-Based Anti-Disturbance Fault-Tolerant Control for MASs With Process and Sensor Faults

Adaptive Event-Triggered Resilient Control of Heterogeneous Multiagent Under DoS Attacks and Link Faults on Directed Graphs

Adaptive Event-Triggered Resilient Control of Heterogeneous Multiagent Under DoS Attacks and Link Faults on Directed Graphs

Consensus for Multiple Random Mechanical Systems With Applications on Robot Manipulator

This paper studies the consensus problem of multiple random mechanical/Euler-Lagrange systems. First, an event-triggered adaptive distributed observer is proposed. It can estimate both the system matrix and states of the leader. Compared with the existing works, the leader system contains unknown parameters, which makes the design of the observer challenging. Second, an event-triggered adaptive control method is proposed. The proposed method not only can deal with the random disturbance, but also can save the energy and execution times of the actuators by event-triggered mechanisms. Finally, a practical application on a real multiple-joint robot is conducted. Each joint of the robot is regarded as a mechanical system. We show that by the presented controller, the angular position of each joint can track a reference signal despite uncertainties and random disturbances.

Command Filtered Event-Triggered Adaptive Control for a Class of MIMO Nonlinear Systems Based on Neural Network Model

Command Filtered Event-Triggered Adaptive Control for a Class of MIMO Nonlinear Systems Based on Neural Network Model

Integral Barrier Lyapunov Function-Based Adaptive Event-Triggered Control of Flexible Riser Systems

Integral Barrier Lyapunov Function-Based Adaptive Event-Triggered Control of Flexible Riser Systems

Distributed Adaptive Event-Triggered Control of Connected Automated Vehicle Platoon Systems with Spoofing Cyber Attacks

Distributed Adaptive Event-Triggered Control of Connected Automated Vehicle Platoon Systems with Spoofing Cyber Attacks

Adaptive Event-Triggered Control for PDE-ODE Cascade Systems via Hierarchical Sliding Mode

Adaptive Event-Triggered Control for PDE-ODE Cascade Systems via Hierarchical Sliding Mode

Sampled-based adaptive event-triggered resilient control for multiagent systems with hybrid cyber-attacks

The multiagent systems have shared broad application in many practical systems including unmanned aircraft clusters, intelligent robots, and intelligent transportation. However, many unexpected cyber-attacks may disturb or disrupt the normal communication of the agents, thus reducing the interacting efficiency of multiagent systems. Ever since the cyber-attacks have been proposed, the resilient control problem for multiagent systems has been intensively explored in light of the communication network growth. However, most of the consequences only focused on denial-of-service (DoS) attacks or deception attacks independently. Distinguished from the existing resilient control mechanisms, the current investigation represents the first attempt at designing an adaptive resilient controller for multiagent systems according to the sampled-based adaptive event-triggered manner, where denial-of-service (DoS) attacks and deception attacks are both considered. First, the hybrid cyber-attacks model and its impact on the closed-loop system are addressed. And then, an adaptive event-triggered strategy is proposed to reduce network resource consumption and ease the communication burden, where the designed adaptive law can automatically adjust the triggering threshold. Finally, the consensus state of multiagent systems is capable of achieving via a series of reasonable control rules formulated through Lyapunov functional approach despite suffering hybrid cyber-attacks. And a simulation example is given to substantiate the feasibility of the proposed method.

Event-Triggered Adaptive Neural Control for Full State-Constrained Nonlinear Systems with Unknown Disturbances

Event-Triggered Adaptive Neural Control for Full State-Constrained Nonlinear Systems with Unknown Disturbances

Observer-based dynamic event-triggered adaptive control for uncertain nonlinear strict-feedback systems

This paper proposes an observer-based dynamic event-triggered adaptive control approach for uncertain nonlinear strict-feedback systems. Initially, an observer for unmeasurable states is constructed. Subsequently, employing backstepping technique, the output-feedback adaptive control law and parameter adaptive law are developed. The tuning function method is used to avoid the over-parameterization problem in parameter adaptive law design. To lower the data exchange rate within the network, a dynamic event-triggered scheme is formulated to allow real-time update control signal. Through Lyapunov theory analysis, the resulting closed-loop system is demonstrated to be stabilized, and the occurrence of Zeno behavior is effectively prevented. Finally, the simulation example validates the obtained result of the presented design approach.