Event-triggered Control Algorithm Research Articles

Event-triggered fault-tolerant control for nonlinear systems with semi-Markov process

ABSTRACT In this paper, the collaborative control of multiple nonlinear Euler-Lagrange systems with actuator faults and stochastic semi-Markov jump networks (SMJNs) is concerned. In order to fulfil high reliability and low consumption, several novel event-triggered fault-tolerant control (ETFTC) algorithms are established to simultaneously eliminate the undesirable faults and save control resources, where only quantised partial-state signal of neighbours is required for the control process. Moreover, some ubiquitous constraints, such as unknown control directions and time-varying communication delays, are also comprehensively considered and effectively excluded so as to further enhance the control reliability. Meanwhile, some sufficient criteria on the algorithm synthesis and stability analysis without involving Zeno behaviour are successfully built with the aid of Lyapunov–Krasovskii functional. Finally, some illustrative simulations are performed to validate the feasibility and effectiveness of the derived ETFTC algorithms.

Event-Triggered Distributed H∞ Constrained Control of Physically Interconnected Large-Scale Partially Unknown Strict-Feedback Systems

In this paper, an event-triggered distributed <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> constrained control algorithm is designed for physically interconnected large-scale partially unknown strict-feedback systems with constrained-input and external disturbance. The advantage of both physical interconnection and communication is synchronously exploited for the scheme. First, an event-triggered feedforward control policy is proposed to transform control of physically interconnected large-scale systems into equivalent event-triggered control of decoupled multiagent systems. Then, an event-triggering condition and an event-triggered feedback control algorithm are designed to learn the optimal control policy and the disturbance policy in the worst case. The algorithm eliminates identifier, actor, and disturber neural networks and also relaxes the persistent excitation condition. It guarantees that the closed-loop dynamics is stabilized and the cost function is converged to the bounded <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -gain optimal value while the Zeno phenomenon is excluded. Finally, the effectiveness of the proposed algorithm is verified through simulation results of event-triggered distributed control of a physically interconnected constrained-torques multimobile robot system.

Neural-Network Based Adaptive Self-Triggered Consensus of Nonlinear Multi-Agent Systems With Sensor Saturation

This paper aims to propose a self-triggered consensus control scheme for a class of nonlinear multi-agent systems with sensor saturation. Because of the existence of unknown nonlinear dynamics, this study borrows the approximation capability of neural networks to design the consensus control protocol. This paper adopts neural network to approximate the ideal controller, instead of using the combination of neural network and adaptive method to approximate the unknown system dynamics. Thus, the extended approximation property of neural network for event-based sampling can be beneficially introduced. Moreover, the designed controller only updates at discrete time, which enables that the system can be modeled as a hybrid system with impulsive dynamics. Thus, the stability theory of impulsive systems can be used to analyze the convergence of the system. It should be noted that this is the first time to propose an effective event-triggered consensus control algorithm based on neural network. Furthermore, this paper also considers a frequently encountered phenomenon of sensor saturation. The convex hull method is adopted to deal with sensor saturation problem, instead of the widely used sector condition method. Finally, the performance of the proposed neural-network based self-triggered consensus control algorithm is demonstrated by the numerical examples.

Event-Triggered Control for Nonlinear Time-Delay Systems

This article studies the event-triggered control problem of general nonlinear systems with time delay. A novel event-triggering scheme is presented with two tunable design parameters, based on a Lyapunov functional result for the input-to-state stability of time-delay systems. The proposed event-triggered control algorithm guarantees the resulting closed-loop systems to be globally asymptotically stable, uniformly bounded, and/or globally attractive for different choices of these parameters. Sufficient conditions on the parameters are derived to exclude Zeno behavior. Two illustrative examples are studied to demonstrate our theoretical results.

Decentralized event-triggered frequency regulation for multi-area power systems

To achieve a better frequency control performance for multi-area power systems, this paper proposes a novel decentralized event-triggered control (ETC) algorithm for automatic generation control (AGC) to replace the traditional periodic sampling/communication mechanism. For each control area, decentralized event-triggering conditions that only depend on locally available information are designed for the control center to determine time instants when to sample the frequency and for each communication channel to decide instants when to broadcast corresponding information. Further, a strictly positive dwell time is introduced to prevent Zeno behaviors. Small disturbance frequency stability of the interconnected power network under the developed ETC law is analyzed, and a sufficient stability criterion on the selection of event-triggered parameters is established. Case studies on a three-area test system illustrate the effectiveness and efficiency of our method.

Distributed Event-Triggered Voltage Control with Limited Re-Active Power in Low Voltage Distribution Networks

A high proportion of photovoltaic (PV) connections to a low-voltage distribution network (LVDN) causes serious voltage problems. In order to ensure voltage stability for renewable energy networks, we propose a distributed reactive voltage control strategy that is event-triggered. The voltage information of the PV nodes is transmitted to the upper layer of the communication network, where the agent calculates the output set value of the PV inverter. The underlying control strategy is based on the voltage sensitivity matrix, and the upper-level control strategy is based on an event-triggered consensus protocol. This strategy can accommodate the requirements for multi-time modeling and control. We verified the convergence of the event-triggered control algorithm using numerical analysis and proved the reduction of the communication times. We conducted case studies and simulation experiments to verify the effectiveness of our proposed voltage control strategies.

Event-Triggered and Self-Triggered Control of Discrete-Time Systems With Input Constraints

This article designs the static and dynamic event-triggered control (ETC) and self-triggered control (STC) algorithms to achieve the semiglobal stabilization of discrete-time systems with input constraints. First, a novel static ETC algorithm based on the discrete-time parametric Lyapunov equation (DPLE) is designed. In order to further increase the interevent times (IETs), the corresponding dynamic ETC is designed. Next, both static and dynamic STC, where the next control law updates depend on the previous triggered states, are proposed to avoid monitoring the measurement errors. The proposed algorithms are not only capable of reducing the number of transmissions significantly but also build a very simple and clear relationship between the only design parameter and the nontrivial IET (NIET). This allows us to change regularly IETs by adjusting the design parameter so that the nontriviality of static and dynamic ETC and STC is guaranteed and a tradeoff between the IETs and the control performance can be easily found. Specifically, by exploring the properties of DPLE, the designed algorithms avoid the complex relationship between the nontrivial condition and the system matrices. Finally, the designed static and dynamic ETC and STC algorithms are applied to the design of the spacecraft rendezvous control system and their effectiveness is verified by simulation results.

Event-triggered consensus strategy for uncertain topological fractional-order multiagent systems based on Takagi–Sugeno fuzzy models

This article investigates the event-triggered consensus strategy (ETCS) for uncertain topological fractional-order multiagent systems based on Takagi–Sugeno fuzzy models. First, a novel distributed ETCS, in which the event generator only depends on the information from neighboring agents at event-triggering instants, is designed to reduce the frequency of information transmission among all agents and to ensure a positive lower bound on the interval between any two event-triggering instants for all agents. Then, by applying the fractional Lyapunov method and the designed ETCS, the consensus problem for fractional-order multiagent systems is handled with less waste of system resources compared to the previous related works. According to the proposed consensus criteria, the Zeno behavior is precluded for ETCS. Finally, numerical simulations are given to demonstrate the effectiveness of the designed event-triggered control algorithm.

Event-triggered and self-triggered formation control of a multi-agent system

In this paper, a distributed event-triggered formation control algorithm for a multi-agent system which consists of linear discrete-time agents is proposed based on control theory. For agents to achieve a formation, it is necessary to communicate with each other to feedback their information. For communication, wireless communications are desirable for various tasks since wired networks can constrain their movement. Also, each agent have to observe their states from sensors. Those energy consumption due to unnecessary calculation and communication of agents may shorten battery life of agents. By using the event-triggered protocol, which updates the control input aperiodically only when certain triggering condition is satisfied, we aim to reduce those calculation and communication by reducing the input update frequency. Also the multi-agent system is guaranteed to achieve a formation by determining the triggering condition of the control input based on Lyapunov’s stability theorem. At the end of the paper, the effectiveness of the proposed algorithm is verified by numerical simulation.

Neural Network–Based Event-Triggered Adaptive Control Algorithms for Uncertain Nonlinear Systems with Actuator Failures

The adaptive control for strict-feedback nonlinear systems has drawn a lot of attention in various communities. Since neural network is a useful universal-approximator to approximate unknown plant model, the neural network–based adaptive control for nonlinear systems has attracted substantial interest over decades. Furthermore, to reduce the controller updating and save the control resource, the event-triggered mechanism has been widely applied. In this paper, the RBF neural network is applied to construct the state and composite disturbance observers and the back-stepping and Lyapunov-like method are applied to design the event-triggered adaptive controller. The theoretical framework of adaptive fault-tolerant control issue for strict-feedback nonlinear system that suffer from both unknown mismatched disturbance and actuator failures is formulated. This paper comes up with a novel event-triggered control strategy to guarantee that the tracking issue is resolved with better desired performance. In this study, a unified theoretical mechanism is developed to tackle the case where some factors consisting of unknown state variables, unknown mismatched disturbance, and actuator failures as well as event-triggered effects are merged together. We expect to extend the proposed method for the self-triggered case.

Fixed‐time event‐triggered consensus of second‐order multi‐agent systems with fully continuous communication free

This study deals with fixed-time consensus problems of second-order multi-agent systems with uncertain bounded disturbances. Instead of the time-triggered control algorithm, a distributed event-triggered control algorithm is developed in the presence of uncertain disturbance. By designing a novel threshold, a triggering function is proposed, which is fully distributed and does not require continuous communication. To tackle the chattering problem, a saturation function is defined to improve the control algorithm. Several conditions are derived and the rigorous proof is given by using Lyapunov stability theory and fixed-time stability theory. Then, to reduce the disturbance effect, an event-triggered control algorithm is proposed by using a disturbance observer. Finally, two simulation examples are given to verify the efficiency of the proposed control method.

Parametric Lyapunov equation based event‐triggered and self‐triggered control of input constrained linear systems

SummaryThis article studies semiglobal stabilization of input constrained linear systems based on the static and dynamic event‐triggered control (ETC) and self‐triggered control (STC). First, a static ETC based on the parametric Lyapunov equation (PLE) is designed. Then the corresponding dynamic ETC is also proposed. The main advantage of the proposed static and dynamic ETC algorithms is that the minimum interexecution time (MIET) as a decreasing function of the parameter in the PLE is given, which allows us to find easily a trade‐off between the interexecution times (IETs) and the control performance. The fact that the MIET of the dynamic ETC is never less than that of the static ETC is also guaranteed. Next, in order to avoid continuous monitoring of the states, both static and dynamic STC are designed. The influence of the parameters on IETs of the static and dynamic STC is analyzed in detail. The nonoccurrence of the Zeno phenomenon is proved in all the control algorithms. Finally, the proposed algorithms are applied to the design of spacecraft rendezvous. Numerical simulations on the original nonlinear model of the spacecraft rendezvous control system show the control system effectiveness of the algorithms.

New trends in modeling and control of hybrid systems

New trends in modeling and control of hybrid systems

Decentralized Event-Triggered Frequency Control With Guaranteed L ∞-Gain for Multi-Area Power Systems

This letter proposes a novel decentralized event-triggered control algorithm to replace the conventional periodic sampling/communication mechanism of automatic generation control (AGC) for multi-area power systems. For each control area, event-triggering rules that only rely on local measurements are designed to decide the sampling/communication instants. Further, a strictly positive dwell time is introduced to exclude Zeno behaviours. The £ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -stability of the multi-area power system with the developed event-triggered control law and essentially bounded net load disturbances is studied, and a sufficient stability criterion is established. Finally, case studies demonstrate the effectiveness and efficiency of the proposed method.

A novel distributed event-triggered control for reactive power sharing based on hierarchical structure in islanded microgrid

Due to line impedance mismatches, nonlinear loads and other reasons, the traditional droop control algorithms have great limitations on the control of reactive power sharing. Distributed control algorithms based on hierarchical structure have become an effective approach for reactive power sharing compared with traditional centralized control methods. In this paper, an event-triggered control algorithm based on stability analysis of Lyapunov method is put forward in order to satisfy the demand of low-bandwidth communication for distributed generator in islanded microgrid. Subsequently, a distributed hierarchical control scheme adopting proposed event-triggered strategy is designed to achieve proportional reactive power sharing in an islanded microgrid. Finally, the feasibility and validity of the proposed algorithm are further verified in MATLAB/Simulink environment.

Distributed Event-Triggered Control of DC Microgrids

A dc microgrid needs to be well controlled to fully unlock its potential. This article presents a distributed event-triggered control algorithm for accurate load current sharing and voltage profile regulation of dc microgrid. According to the control design, a subsystem controller only communicates with its neighboring controller(s) when a certain condition evaluated using local measurements is triggered. Thereafter, control signals are updated when such events are triggered locally or at its neighboring subsystem(s). Since the algorithm can significantly lower the requirements for communication and computation, it is efficient and easy to implement. Lyapunov synthesis is performed to show that the reduced communication does not compromise the control performance. Simulation with a detailed switch-level dc microgrid model further validates the effectiveness of the proposed control algorithm.

Improved AET Robust Control for Networked T-S Fuzzy Systems With Asynchronous Constraints.

This article proposes a novel improved adaptive event-triggered (AET) control algorithm for networked Takagi-Sugeno (T-S) fuzzy systems with asynchronous constraints. First, taking the limited bandwidth of the network into consideration, an improved AET mechanism is proposed to save the communication resource. Superior to the existing event-triggered mechanism, the improved AET scheme introduces two adjusting parameters, which further contribute to the economization of the communication resource. Second, with consideration of asynchronous premise variables, a reconstructed approach is applied to synchronize the time scales of membership functions of the fuzzy system and the fuzzy controller. Third, to derive a less conservative sufficient condition for the controller design, a new augmented Lyapunov-Krasovskii functional with event-triggered information and triple integral terms is constructed. Meanwhile, by applying a Bessel-Legendre inequality and extended reciprocally convex matrix inequality together, a new control algorithm is derived with less conservatism. Finally, simulations on a cart-damper-spring system are implemented to evaluate and verify the performance and advantages of the proposed algorithm.

Robust neural event-triggered control for dynamic positioning ships with actuator faults

This paper presents a novel robust neural event-triggered control algorithm to achieve the dynamic positioning operation of marine surface ships in the presence of actuator faults. In the algorithm, the model uncertainty and the “explosion of complexity” are addressed by fusion of the robust neural damping and dynamic surface control techniques. The gain related adaptive law is constructed to compensate the gain uncertainties and the unknown actuator faults, which improved the stability of the ship dynamic loop system. Furthermore, the event-triggered mechanism is introduced to reduce the communication load between the controller and actuator. For merits of the aforementioned design, the proposed algorithm is with the advantages of concise form and easy to be implemented in practical ocean engineering. Based on the Lyapunov theory, rigorous analysis is proved to guarantee the semi-global uniform ultimate boundedness (SGUUB) of the closed-loop system. Finally, numerical simulations are conducted to demonstrate the validity and feasibility of the proposed algorithm.

Attitude Control of UAVs Based on Event-Triggered Supertwisting Algorithm

An event-triggered attitude control algorithm is developed for quadrotor unmanned aerial vehicles (UAVs) subject to external disturbances. In this article, first an event-triggered supertwisting stabilizing control strategy for a class of second-order nonlinear systems is proposed. Then, a Lyapunov-based stability analysis is provided for the closed-loop system, and the Zeno-free execution of triggering sequence is guaranteed via rigorous analysis. Furthermore, the proposed control strategy is applied on attitude control of UAVs to reduce the computing cost without degrading the performance of the system. Finally, the efficiency of the developed method is validated by numerical simulation.

Adaptive event-triggered distributed model predictive control for multi-agent systems

Event-triggered control is an effective control approach with control update instants determined by pre-defined event-triggering conditions, and the triggering conditions are usually required to be tested continuously. In this paper, we present an adaptive event-triggered distributed model predictive control algorithm for nonlinear continuous-time multi-agent systems. All the agents are able to adaptively determine when to check the triggering conditions, which means the triggering conditions are tested intermittently with intervals determined adaptively. We prove the feasibility of the proposed algorithm, and also prove that the multi-agent system under the algorithm will converge to an invariant set in finite time by utilizing variable prediction horizon approach instead of the Lyapunov function method, such that the event-triggering conditions are less conservative. Finally, we provide numerical examples to verify the effectiveness and superiority of the proposed algorithm, revealing that the proposed algorithm can efficiently reduce the communication and computation cost as well as the sensing load.