Event-triggering Condition Research Articles

Event-triggered dual-mode predictive control for constrained nonlinear NCSs subject to disturbances and packet dropouts

ABSTRACT In this paper, we propose an event-triggered dual-mode predictive control strategy to deal with two-channel packet dropouts for discrete-time constrained nonlinear networked control systems. The main focus is on the construction of network compensation mechanisms and control algorithms. Firstly, two types of novel event generators are set, respectively, in sensor node and controller node to reduce resource consumption while preserving the desired system performance. In order to establish corresponding event-triggering conditions, two special states, successful transmitted states and reconstructed states, are introduced. Subsequently, a dual-mode control scheme, where the switch of modes depends on reconstructed states, is designed. The recursive feasibility of optimization problems is ensured by determining relationships between actual states and reconstructed states. By constructing actual control laws under two system modes, the stability property of closed-loop systems is guaranteed. Furthermore, effects of packet dropouts and disturbances on system performance are analyzed. At last, the efficiency of the proposed control strategy is illustrated by a cart-damper-spring simulation example.

Cooperative Fault-Tolerant Output Regulation of Linear Heterogeneous Multiagent Systems via an Adaptive Dynamic Event-Triggered Mechanism.

This article considers the problem of cooperative fault-tolerant output regulation of linear heterogeneous multiagent systems with actuator faults where the exosystem matrix is known by part of the followers. An adaptive dynamic event-triggered scheme is proposed to avoid continuous communication between followers, removing the assumption that global information should be known for controller design. Different from the existing open-loop estimation method for obtaining intermittent communication, the approach proposed in this article does not require that the system matrices of all agents be the same and known a prior. Compared with the existing static or periodic event-triggered mechanism (ETM), by introducing dynamic variables, a more general form than an exponential function, the proposed adaptive dynamic event-triggered condition provides the potential to save energy and obtain better performance. It is demonstrated that the suggested event-triggered control (ETC) method performs cooperative fault-tolerant output regulation for all followers in a completely distributed way with intermittent communication while excluding Zeno behavior. Finally, a simulation example is carried out to illustrate the efficacy of the suggested strategy.

A Decentralized Learning Control Scheme for Constrained Nonlinear Interconnected Systems Based on Dynamic Event-Triggered Mechanism

This article presents a decentralized learning control method for a class of partially unknown nonlinear systems with asymmetric control input constraints and mismatched interconnections via a novel dynamic event-triggering condition. By employing an integral reinforcement learning strategy, the system drift dynamics can be avoided in the learning process. Meanwhile, a critic neural network is designed to obtain the approximated value function and tuned by using the gradient descent approach. Furthermore, a novel dynamic event-triggering condition is designed to determine the occurrence of an event by introducing a dynamic variable. By using the Lyapunov theory, all signals in the closed-loop system are proved to be uniformly ultimately bounded. Finally, we present a nonlinear interconnected system and an interconnected power system to verify the effectiveness of the proposed method.

Event-driven fully distributed optimal coordinated control for Euler–Lagrange multi-agent systems with connectivity preservation

This paper studies the distributed optimal coordinated control problem for Euler–Lagrange multi-agent systems with connectivity preservation. The aim is to force agents to achieve the optimal solution minimizing the sum of the local objective functions while guaranteeing the connectivity of the communication graph. For practical purposes, the gradient vector of the local objective function is allowed to use only at the real-time generalized position instead of at the auxiliary system state. To make the control parameters independent of the global information and guarantee the fully distributed manner of controller, the adaptive control is introduced to update the coupling weights of the relative states among neighbors. Moreover, to reduce the resource for control updates, the event-driven communication is employed for the updates of both the relative states and the gradient of the connectivity-preserving potential function. Based on the Lyapunov analysis framework, it is proved that agents can converge to the optimal solution with connectivity preservation and Zeno behavior is excluded for the two event-triggering conditions. Finally, the effectiveness of the proposed method is verified by a numerical simulation example.

Event-triggered finite-time [formula omitted] control for positive Markov jump systems with partly known transition probability

The article studies the event-triggered finite-time L1 control design for positive Markov jump systems(PMJSs) with partly known transition probability. Firstly, with regard to the unique positive characteristic, a novel 1-norm based event-triggering condition is proposed for PMJSs. Then, sufficient conditions of positivity and finite-time L1 boundedness are provided for the closed-loop system under the constructed even-triggered control law. Based on this, by applying the matrix decomposition technology to the event-triggered controller gain matrix, an effective mode-dependent event-triggered finite-time L1 control scheme in the solvable linear programming(LP) form is presented for PMJSs while preserving the positivity and finite-time L1 boundedness in closed-loop. Further, an effective LP-based event-triggered finite-time L1 control scheme is also obtained for PMJSs with completely known transition probability and completely unknown transition probability, respectively. Finally, two examples are presented to verify the validity of the obtained results.

Event-triggered secure control of nonlinear multi-agent systems under sensor attacks

In this paper, a security consistent tracking control scheme with event-triggered strategy and sensor attacks is developed for a class of nonlinear multi-agent systems. For the sensor attacks on the system, a security measurement preselector and a state observer are introduced to combat the impact of the attacks and achieve secure state estimation. In addition, command filtering technology is introduced to overcome the “complexity explosion” caused by the use of the backstepping approach. Subsequently, a new dynamic event-triggered strategy is proposed, in which the triggering conditions are no longer constants but can be adjusted in real time according to the adaptive variables, so that the designed event-triggered mechanism has stronger online update ability. The measurement states are only transmitted through the network based on event-triggered conditions. The proposed adaptive backstepping algorithm not only ensures the security of the system under sensor attacks but also saves network resources and ensures the consistent tracking performance of multi-agent systems. The boundedness of all closed-loop signals is proved by Lyapunov stability analysis. Simulation examples show the effectiveness of the control scheme.

Event trigger based adaptive neural trajectory tracking finite time control for underactuated unmanned marine surface vessels with asymmetric input saturation

An adaptive finite time trajectory tracking control method is presented for underactuated unmanned marine surface vessels (MSVs) by employing neural networks to approximate system uncertainties. The proposed algorithm is developed by combining event-triggered control (ETC) and finite-time convergence (FTC) techniques. The dynamic event-triggered condition is adopted to avert the frequent acting of actuators using an adjustable triggered variable to regulate the minimal inter-event times. While solving the system uncertainties and asymmetric input saturation, an adaptive neural networks based backstepping controller is designed based on FTC under bounded disturbances. In addition, via Lyapunov approach it is proved that all signals in the closed-loop system are semi-global uniformly ultimately bounded. Finally, simulations results are shown to demonstrate the effectiveness of this proposed scheme.

Observer-based event-triggered control for continuous-time switched linear parameter-varying systems

This paper researches the interval observer–based event-triggered control of switched linear parameter-varying (LPV) systems, which involve unknown external disturbance and measurement noise. First, we offer an event-triggering condition and a controller which are correlated with state estimations. Then, by analyzing the relationship between switching sequences and sampling sequences, we model the resulting systems as switched time-delay systems. It deserves to note that the mismatches of the switching signal for the controllers and the systems may occur due to the sampling of switching signal. Using the merging switching signal technique and the multiple Lyapunov–Krasovskii functional method, and thinking over both synchronous and asynchronous switching of the systems and the controllers, we acquire relevant conditions which are used to realize the stability of the whole switched LPV closed-loop systems under the mode-dependent average dwell time (MDADT) switching. Next, we give a specific algorithm to design the controller gains. Finally, a numerical example is provided to verify the superiority of the outcomes.

Adaptive NN-Based Event-Triggered Containment Control for Unknown Nonlinear Networked Systems.

This article systematically addresses the distributed event-triggered containment control issues for multiagent systems subjected to unknown nonlinearities and external disturbances over a directed communication topology. Novel composite distributed adaptive neural network (NN) event-triggering conditions and event-triggered controller are raised meanwhile. Furthermore, the designed event-triggered controller is updated in an aperiodic way at the moment of event sampling, which saves the computation, resources, and transmission load. On the basis of the NN-based adaptive control techniques and event-triggered control strategies, the uniform ultimate bounded containment control can be achieved. In addition, the Zeno behavior is proven to be excluded. Simulation is presented to testify the effectiveness and advantages of the presented distributed containment control scheme.

Group Consensus of Second-Order Sample MASs via Distributed Event-Triggered Mechanism

The group consensus of second-order sample multi-agent systems is investigated by using the distributed event-triggered mechanism. Firstly, a consensus protocol is proposed with distributed event-triggered mechanism. Consensus protocol is updated only when the event-triggered condition is met, and the update of consensus protocol only depends on the data collected by the sample holder at the moment of triggering. Secondly, the sufficient condition to realize group consensus is obtained. Thirdly, the differential equation is constructed by comparison theorem to obtain a minimum positive and lower bound for any two trigger time intervals. The Zeno phenomenon is also avoided. Finally, the effectiveness is verified by a simulation example.

Event-Triggered Sliding Mode Load Frequency Control of Multiarea Power Systems Under Periodic Denial-of-Service Attacks

A sliding mode load frequency control (SMLFC) problem is committed for interconnected power systems under periodic denial-of-service (DoS) attacks. A suitable sliding surface is designed to deduce the state space equation in the multiarea power systems. An event-triggered device is used to determine whether the sampling state should be transmitted, and a new event-triggered condition is provided to match periodic DoS attacks. By utilizing the multiple Lyapunov–Krasovskii function approach, the globally exponential stability is proved based on matrix inequalities and the exponential stability criterion is subsequently obtained in terms of linear matrix inequalities. A suitable control law is established to ensure the reachability of power system. The feasibility and superiority of the proposed SMLFC are illustrated by numerical examples.

Asynchronous Control of Fuzzy Singularly Perturbed System With a Dynamic Event-Triggered Strategy

This paper is mainly concerned with the asynchronous control problem of fuzzy singularly perturbed systems (FSPSs) by designing an effective dynamic event-triggered control (ETC) strategy. Different from the existing results, a sample-based dynamic eventtriggering condition related to the singular perturbation parameter (SPP) " is proposed. Particularly, an auxiliary variable is employed in the eventtriggered mechanism (ETM), which changes with the fluctuation of the system state. By constructing a novel parameter-dependent Lyapunov functional, some sufficient conditions are derived to stabilize FSPSs and solve the "-dependent control gain. Furthermore, the upper bound of SPP _" related with the proposed control gain is determined. The Zeno behavior can be naturally avoided by applying the sample-based dynamic ETC. Finally, two examples including a nonlinear circuit are presented to validate the effectiveness of obtained results.

Event-triggered economic MPC with relaxed contractive constraints

This paper proposes a novel event-triggered economic model predictive control (EMPC) scheme of constrained nonlinear systems. Some auxiliary positive-definite functions at economic optimal equilibrium points are defined for economic performance functions that are generally non-convex and non-positive-definite. The optimal value function of the auxiliary function is used to design an adjustable stability constraint which is imposed on the original EMPC optimization problem. The design of the contractive constraint can avoid Zeno behaviors during the triggered process. Especially, the event-triggered condition is designed by relaxed contractive constraint and the updated control laws. Sufficient conditions guaranteeing the recursive feasibility and stability of the EMPC are derived. Finally, the effectiveness of the obtained results is illustrated by a simulation example of a continuously stirred tank reactor.

Energy function-based and norm-free event-triggering for scheduling control data transmissions

ABSTRACT This paper studies the problem of scheduling control data transmissions from embedded processors to physical systems. For this problem, we propose novel state feedback and output feedback control architectures that are predicated on energy function-based and norm-free event-triggering conditions, where the embedded processor broadcasts a sampled data of its control signal value through a zero-order-hold operator to the physical system when the left side of the event-triggering condition equals to its right side. In this context, the energy function-based feature means that the right sides of the proposed event-triggering conditions involve an energy function as well as its time-derivative to make the selection of these right sides user-adjustable. Furthermore, the norm-free feature means that the left sides of the proposed event-triggering conditions do not depend on signal norms to allow for better control data transmission reduction. System-theoretical analyses of our event-triggered state feedback and output feedback control architectures are shown using the same energy functions and their time-derivatives used in the proposed event-triggering conditions, where illustrative numerical examples are also presented to demonstrate the efficacy of our contributions. To the best of our knowledge, the results given in this paper explore how event-triggering conditions are linked not only to energy functions but also to their time-derivatives for the first time.

Compound learning based event-triggered adaptive attitude control for underwater gliders with actuator saturation and faults

This paper proposes an event-triggered compound learning adaptive attitude control scheme for underwater gliders (UGs) with unknown dynamics, actuator saturation, and actuator faults. To begin with, in the framework of adaptive dynamic surface control (DSC), a historical input–output data based neural network (NN) is employed to estimate and compensate for unknown dynamics, external disturbance and actuator faults as a whole. For the merit of the composite learning and integral concurrent learning techniques, the NN weights are updated in a compound learning fashion where instant tracking errors, prediction errors, and historical estimation errors are combined in the adaptive law, making the NN weights converge to their true value with a good transient performance. Furthermore, a hyperbolic tangent function is utilized to explicitly handle the input saturation constraint, and the singular problem is circumvented by a Nussbaum gain function. Finally, a novel event-triggering condition is designed to save communication resource and reduce energy consumption. Theoretical proofs are provided to ensure the semi-global uniformly ultimately bounded (SGUUB) stability of the closed-loop system. The effectiveness of the proposed scheme is verified by simulation studies under various conditions.

Event-Triggered Optimal Control for Temperature Field of Roller Kiln Based on Adaptive Dynamic Programming.

Temperature field control is crucial for the comprehensive performance of Ni-Co-Mn layered cathode material that is the most important part of lithium-ion batteries. Starting from the aspect of a class of distributed parameter systems described by highly dissipative partial differential equations (PDEs), an event-triggered optimal control (ETOC) method based on adaptive dynamic programming (ADP) for the roller kiln temperature field is proposed. First, we formulate the event-triggered control problem of the temperature field under the general framework of PDE systems. Then, an event-triggered condition is designed based on the stability of the closed-loop PDE system, which also guarantees the upper bound of the performance index. Subsequently, ADP technology is adopted to realize the ETOC, where the critic network is employed to approximate the optimal value function. Since the studied system can be regarded as an impulsive dynamic system with flow dynamics and jump dynamics simultaneously, the stability of the impulsive dynamic system combined with the ADP-based closed-loop PDE system is proved. Finally, simulation results on the temperature field verify the effectiveness of the proposed method.

Fully distributed event-triggered cooperative output regulation for switched multi-agent systems with combined switching mechanism

The cooperative output regulation for switched heterogeneous multi-agent systems under aperiodic triggering and combined switching tactics is studied in this article. Since the global information of Laplacian matrix is not available in design, the fully distributed event-triggered protocol including integral-type term is implemented under directed topology that contain a spanning tree. Based on the relative estimator state between neighbors at triggering instants, the time-varying coupled weight of each agent is used to compensate the global information via designing the adaptive laws. Meanwhile, the integral-type term in event-triggering condition can further enlarge the inter-event times and avoid Zeno behavior. To reduce the switching frequency, the combined switching rules constituted by the dwell time and agent-dependent switching strategies are then established based on time-varying multiple Lyapunov functions. Further, the cooperative output regulation performance for switched multi-agent systems is achieved through the joint action of the combined switching laws and controller. Finally, two examples are used to clearly verify the effectiveness of results.

Distributed event-triggered consensus control for a class of nonlinear multi-agent systems under switching topologies

A distributed event-triggered consensus control protocol is proposed for a class of nonlinear multi-agent systems with switching topologies. The nonlinear dynamics and switching topologies are considered simultaneously to adapt to some actual scenarios. To economize the communication resources, a distributed event-triggered mechanism is established to decrease the consumption of communication resources. The new event-triggered mechanism is constructed by the information of distributed errors, which can fully utilize communication relationships among agents to control the timing of events. A class of negative exponential functions is added to the event-triggered condition as the event-triggered mechanism is applied for the presented multi-agent systems. The design of event-triggered condition can reduce the occurring frequency of events when the adjusting time of control scheme begins at an initial small amount period of time. Finally, the simulation results are provided, which verify the feasibility and effectiveness of the designed event-triggered consensus control strategy for the presented multi-agent systems with switching topologies.

Distance-Based Elliptical Circumnavigation Control for Non-Holonomic Robots With Event-Triggered Unknown System Dynamics Estimators

How to efficiently collect multi-dimensional information of traffic accidents with high dangers has triggered intensive concerns in intelligent transportation community. This paper provides a viable and continuous observation solution using low-budgeted mobile robots. At the kinematic level, a relative position estimator utilizing available distance data is constructed with an exponential error convergence, removing the dependency of using global position. To proceed, resorting to estimation results, an elliptical encircling guidance rule with a time-varying radius is established to preserve circumnavigation concerning targets. At the kinetic level, a new fuel-saving uncertainty mitigation scheme, i.e., event-triggered unknown system dynamics estimators (ETUSDEs) with the feature of a reduced transmission load and a concise structure are respectively developed in velocity and angular rate subsystems to reconstruct uncertainties with a prescribed decaying rate, where event-triggering conditions are enforced to schedule updating frequency of actuators and measurements in an aperiodic manner instead of a fixed time interval. Then, an event-triggered robust kinetic control protocol is synthesized to achieve an accurate command tracking without incurring Zeno behaviors. Finally, the convergence of entire system is illustrated through input-to-state stable (ISS) criterion. Simulation results are delivered to testify the effectiveness of proposed method.

Event-triggered fixed-time distributed output feedback sliding mode cooperative control for multiple surface vessels with input saturation

Aiming at the problems of cooperative control for multiple surface vessels (MSVs), such as unknown environmental disturbances, unavailable velocities, model uncertainties, actuator saturation, and limited action times of the actuator, an event-triggered fixed-time distributed output feedback sliding mode cooperative control method with input saturation is proposed. The scheme ensures the practical fixed-time stability of the cooperative control system. First, a fixed-time extended state observer (FxESO) is designed to provide the estimations of velocities and lumped disturbances including unknown environmental disturbances and model uncertainties. Second, to deal with the actuator saturation, a fixed-time auxiliary dynamic system is designed. Third, a fixed-time non-singular terminal sliding mode manifold (FxNTSMM) is introduced to effectively eliminate singularity and improve chattering of the system. Finally, an event-triggered distributed controller based on FxNTSMM and FxESO is proposed. An event-triggered condition can avoid unnecessary actions of the actuator, and Zeno phenomenon is avoided by theoretical proof. In addition, the upper bound of the convergence time is independent of the initial state. Simulation results are given to demonstrate the effectiveness of the proposed control scheme.