Self-triggered Strategy Research Articles

Self-triggering strategy design for an n-dimensional quantized linear system under bounded noise.

This paper investigates the self-triggered control for stabilizing an n-dimensional linear time-invariant system under communication constraints, including finite bit rates and transmission delay. The concerned system is further perturbed by bounded process noise. To resolve these issues, a self-triggering strategy is proposed. Specifically the proposed self-triggering strategy selects the next sampling time from a set of pre-designed time instants based on the sampled system states. By fully exploiting the encoded information of receive time instants of feedback packets, we can achieve the desired input-to-state stability (ISS) at a lower bit rate than that of periodic sampling. Moreover, the proposed self-triggering strategy is free of the burdens of continuously monitoring the system state compared with event-triggered sampling strategies. The efficiency of the proposed self-triggering strategy is further confirmed by simulations.

Secure state estimation of memristive neural networks with dynamic self-triggered strategy subject to deception attacks

This paper is dedicated to addressing state estimation for memristive neural networks (MNNs) featuring dynamic self-triggered mechanisms (DSTM) subject to deception attacks (DA). Taking into account the constrained channel bandwidth, the data sampling controller by dynamic self-triggering is proposed for measurement output. The network transmission of data among sensor and estimator is susceptible to deception attacks, and a corresponding state estimator is developed. Utilizing Lyapunov stability theory, it is demonstrated that the state error system is exponentially ultimately bounded in the mean square, and the dynamic self-triggered strategy avoids Zeno behavior. Furthermore, the estimation gains are obtained using a linear matrix inequality (LMI) approach. Lastly, simulated examples are provided to demonstrate the efficacy of the proposed approach.

Adaptive impulsive consensus of nonlinear multi-agent systems via a distributed self-triggered strategy

This article investigates the consensus problem of nonlinear leader-following multi-agent systems (MASs) via adaptive impulsive control. A distributed impulsive controller is employed, in which an adaptive updating law in the discrete-time setting is designed for the impulsive gain. Moreover, in order to reduce the communication cost, a distributed self-triggered strategy is proposed to determine when the impulsive instant occurs. Some criteria are derived to guarantee consensus of leader-following MASs based on Lyapunov stability theory and algebraic Riccati equation. It is proved that the self-triggered impulsive sequence does not exhibit Zeno behaviour. Finally, an illustrative example is presented to empirically validate the effectiveness of the theoretical results.

Periodic Self-Triggered Impulsive Synchronization of Hybrid Stochastic Complex-Valued Delayed Networks

This paper studies the synchronization problem for hybrid stochastic delay complex-valued networks with time-varying multi-links (HSDCNT) via periodic self-triggered impulsive control (PSIC). Therein, PSIC combines with the benefits of impulsive control and periodic self-triggered strategy. Moreover, time delays, stochastic disturbances, and time-varying multi-links are considered simultaneously. Particularly, the synchronization for HSDCNT is studied on the complex space and no split real and imaginary parts. Actually, different from the previous PSIC-based results, this paper extends the real-valued neural networks to HSSs, delayed systems, and complex-valued networks, which is significant and challenging. Then, as a practical application, we consider the synchronization of a kind of Cohen-Grossberg neural network in the complex space. Finally, a numerical example is utilized for demonstration.

Periodic self-triggered strategy for highly non-linear hybrid stochastic delayed networks based on impulsive control

This article investigates the stabilization issue of highly non-linear hybrid stochastic delayed networks (HSDNs) via periodic self-triggered control under impulse (PS-TCI). Firstly, the existence of a unique global solution for highly non-linear HSDNs under PS-TCI is studied. Then, a stabilization criterion for highly non-linear HSDNs is established, by combining a graph-theoretic approach with a novel Lyapunov-based analysis, based on a ‘genuine’ Lyapunov function defined by introducing an auxiliary timer. Therein, the less conservative polynomial growth condition and local Lipschitz condition for the drift and diffusion coefficients are used than the linear growth condition and global Lipschitz condition. Meanwhile, the design idea of PS-TCI is based on the evolution of an upper bound of the mathematical expectation for Lyapunov function (not directly Lyapunov function or system state), which implies that the triggered instant of PS-TCI is not a random variable. Finally the theoretical results are employed to study the stability of a class of FitzHugh–Nagumo circuits networks and the central pattern generators networks of a hexapod robot, and correlative numerical simulations are provided for demonstration.

Intermittent Sampled-Data Stabilization of Highly Nonlinear Delayed Stochastic Networks via Periodic Self-Triggered Strategy

Intermittent Sampled-Data Stabilization of Highly Nonlinear Delayed Stochastic Networks via Periodic Self-Triggered Strategy

Intermittent Stabilization for Stochastic Complex Dynamical Networks via Asynchronously Periodic Self-Triggered Strategy

Intermittent Stabilization for Stochastic Complex Dynamical Networks via Asynchronously Periodic Self-Triggered Strategy

Adaptive self-triggered control-based cooperative output regulation of heterogeneous multi-agent systems under sensor and actuator attack

The cooperative output regulation problem for heterogeneous multi-agent systems under sensor and actuator attacks with an adaptive self-triggering strategy is considered in this article. The attack considered is the false data injection attack. To resolve the system, a dynamic internal model is introduced for each agent, and a distributed adaptive self-triggering control scheme is proposed. The results show that intermittent communication is possible for all agents using the proposed control mechanism and no agent shows Zeno behavior. The adaptive controller is used to guarantee that the regulated output for the heterogeneous multi-agent systemss is achieved with cooperative uniform ultimate boundedness under both actuator and sensor attacks. Given the whole state of information is unavailable, the adaptive dynamic system is designed based on receivable compromised or uncompromised output information. The effectiveness of the given control scheme is illustrated by two examples.

Self-triggered multi-mode control of Markovian jump systems

This paper is concerned with the problem of self-triggered multi-mode control for a class of Markovian jump systems, in which the system states are transmitted to the controller via a communication network with limited bandwidth. In order to achieve the desired performance of the closed-loop system via utilizing a controller with fewer modes, a mode monitoring mechanism related to the system modes is employed to describe the jumping characteristics among different controller modes. Based on the mode monitoring mechanism, a multi-mode controller is constructed, in which the number of controller modes may be fewer than the one of system modes. Moreover, a self-triggered strategy is developed to reduce network loads, according to which the next triggering instant on state transmission is calculated via the past and current transmitted states at the triggering instants. By introducing a sufficiently small scalar, a solving algorithm is proposed for obtaining the self-triggered multi-mode controller gains to ensure the stochastic stability of the closed-loop system with the given L2 gain performance. Finally, the simulation results are provided to illustrate the proposed self-triggered control scheme.

Fully Distributed Formation Control of General Linear Multiagent Systems Using a Novel Mixed Self- and Event-Triggered Strategy

In this study, the state formation control issue of general linear networked multi-agent systems (MASs) is considered. Combining self-triggered strategy with general event-triggered strategy, a novel fully distributed asynchronous mixed self- and event-triggered control strategy is proposed, which can make the MASs achieve the prescribed state formation structure. The control strategy proposed in this study does not depend on any global network information. Therefore, no matter how large scale of the network is, the control strategy is feasible. Meanwhile, the control strategy uses the sampled state information at triggering instants instead of the real-time state information, which efficiently eliminates continuous communication among agents. Different from the existing studies, the event-triggered detector starts to work if and only if the next self-triggering instant comes in the control strategy proposed in this article. Thus, the control strategy can save more communication resources between sensor and event-triggered detector within the same inter-event interval compared with the previous event-triggered strategies. In addition, the control strategy designs a exponential decay term in the triggering function to rule out the unexpected Zeno behavior and reduce the triggering number. Finally, the numerical simulation result of multi-robot formation is given, which demonstrates the feasibility and performance of the proposed control strategy.

Secure Synchronization for Cyber-Physical Complex Networks Based on Self-Triggering Impulsive Control: Static and Dynamic Method

This paper investigates a self-triggered control scheme for a class of cyber-physical complex networks under deception attacks. The false data generated by attacks in both sensor-to-controller channels and controller-to-actuator channels are assumed to obey the Bernoulli distribution. By jointly combining distributed impulsive control scheme, time-varying impulsive effects and self-triggered strategy, a novel distributed self-triggered impulsive controller is elaborately designed. By integrating the definition of average impulsive gain, the contradiction analysis and Lyapunov stability theorem, sufficient conditions are derived for ensuring the secure synchronization within a given error bound. In addition, for further decreasing the triggering frequency and energy consumption, the advanced dynamic self-triggered criteria are obtained. It is worth mentioning that the updating laws adopted for dynamic self-triggered protocol in this paper is non-monotonic, which benefits to deal with the bounded synchronization. Finally, two numerical simulations are given to illustrate the feasibility of derived results.

Self-triggered model predictive control for nonlinear continuous-time networked system via ensured performance control samples selection

Self-triggered and event-triggered control have been widely implemented in various fields recently, especially in networked control systems. In order to save communication resources, trigger control conducts aperiodic control strategies instead of traditional time-driven scheme. Based on the self-triggered control mechanism, this paper investigates self-triggered strategy in model predictive control for nonlinear continuous-time networked system, selects aperiodic samples to ensure system performance while reducing computing energies and improving the resource usage efficiency. The usage of communication resources is quantified as a damping parameter that can characterize the communication effects in total cost function, such that the optimal sampling time and input trajectory can be obtained synchronously. In theoretical results, the feasibility of the proposed self-triggered strategy and the asymptotic stability of the nonlinear system are guaranteed. Furthermore, a simulation is given to illustrate the effectiveness of the proposed approach.

Aperiodic sampled-data control of distributed networked control systems with time-delay

This paper proposes a hybrid aperiodic sampled-data mechanism for the control of interconnected subsystems with time-delay. The proposed aperiodic sampled-data mechanism comprises of two stages. In the first stage, the next sampling instant for each subsystem is computed using self-triggering strategy. Thereafter, in the second stage, an event-triggering condition is checked at these sampling instants for each subsystem and signal is transmitted to the controller only if the event-triggering condition is violated. Further, to reduce the computational complexity involved in the proposed triggering mechanism, another triggering mechanism with integrated event-triggering and self-triggering is developed. Also, an upper bound on delay for each subsystem is computed to ensure the stability of distributed networked control system. The results proposed are validated using a simulation example. A comparison of the proposed technique with other triggering mechanisms in terms of sampling instants, number of transmissions to the controller, maximum delay bound and other performance measures is drawn through simulation example.

Self-Triggered Sliding Mode Control for Networked PMSM Speed Regulation System: A PSO-Optimized Super-Twisting Algorithm

This article is concerned with the design of a super-twisting algorithm (STA) based sliding mode controller for permanent magnet synchronous motor (PMSM) speed regulation system under the self-triggered mechanism. By using the strict Lyapunov function approach, it is shown that the tracking error converges to an ultimate domain within the finite-time sense under the proposed self-triggered STA. A feasible self-triggered strategy is designed for both cases with and without external perturbation. Moreover, a nonlinear optimization problem is formulated in terms of the tradeoff between the ultimate domain and the communication burden. The optimized STA gains are obtained by solving the above-formulated optimization problem via a particle swarm optimization algorithm. Finally, the applicability of the proposed self-triggered STA for PMSM is verified by simulation and experiment results.

Model-Free Self-Triggered Control Co-Design for Probabilistic Boolean Control Networks

In this letter, a model-free co-design scheme of triggering-driven controller is proposed for probabilistic Boolean control networks (PBCNs) in order to achieve feedback stabilization with minimum controller efforts. Specifically, Q-learning (QL) algorithm is exploited to devise a self-triggered strategy wherein the controller update time is computed in advance by using the current state information. A new self-triggered QL (STQL) algorithm is presented to achieve the co-design of feedback controller and self-triggered scheme rendering the closed-loop system stable at a given equilibrium point. Finally, some examples are presented to demonstrate the effectiveness of the proposed method.

Emerging Strategies in Anticancer Combination Therapy Employing Silica-Based Nanosystems.

Combination therapy has emerged as one of the most promising approaches for cancer treatment. However, beyond remotely-triggered therapies that require advanced infrastructures and optimization, new combination therapies based on internally triggered cell-killing effects have also demonstrated promising therapeutic profiles. In this revision, the focus is on self-triggered strategies able to improve the therapeutic effect of drug delivery nanosystems. As reviewed, ferroptosis, hypoxia, and immunotherapy show potency enough to treat satisfactorily tumors in vivo. However, the interest of combining those with chemotherapeutics, especially with carriers based on mesoporous silica, has provided a new generation of therapeutic nanomedicines with potential enough to achieve complete tumor remission in murine models.

Event-triggered attitude consensus with absolute and relative attitude measurements

In this paper, we consider the event-triggered attitude consensus of multiple rigid-body systems. Two event-triggered attitude consensus protocols are designed under the absolute attitude and relative attitude measurement, respectively. For the first case, the gnomonic projection is utilized to project the attitude to the Euclidean plane almost globally. Then, a distributed attitude consensus protocol based on the projections is proposed under the event-triggered mechanism. By using the proposed protocol and event-triggered condition (ETC), the almost global attitude consensus is achieved on a positively invariant set. Next, in order to remove the requirement of the absolute attitude information, we propose an event-triggered attitude protocol with relative attitude measurements. The Riemannian gradient descent approach is utilized to design the attitude consensus protocol on a geodesically convex set of attitude configuration space. Moreover, to overcome the continuous monitoring in the event-detection, a self-triggered strategy is presented based on the event-triggered protocol only with the relative attitude measurement. Finally, simulation studies are conducted to verify the effectiveness of the proposed protocols.

Aperiodic sampled-data control of distributed networked control systems under stochastic cyber-attacks

This paper examines the stabilization problem of a distributed networked control system under the effect of cyber-attacks by employing a hybrid aperiodic triggering mechanism. The cyber-attack considered in the paper is a stochastic deception attack at the sensor-controller end. The probability of the occurrence of attack on a subsystem is represented using a random variable. A decentralized hybrid sampled-data strategy is introduced to save energy consumption and reduce the transmission load of the network. In the proposed decentralized strategy, each subsystem can decide independently whether its state should be transmitted to the controller or not. The scheme of the hybrid triggering mechanism for each subsystem composed of two stages: In the first stage, the next sampling instant is computed using a self-triggering strategy. Subsequently, in the second stage, an event-triggering condition is checked at these sampling instants and the control signal is computed only if the event-triggering condition is violated. The self-triggering condition used in the first stage is dependent on the selection of event-triggering condition of the second stage. Finally, a comparison of the proposed approach with other triggering mechanisms existing in the literature is presented in terms of the sampling instants, transmission frequency and performance measures through simulation examples.

A self-triggered control scheme for Markov jump systems under multiple range performance restrictions

This paper proposes a multi-frequency controller design scheme for Markov jump systems (MJSs) based on the self-triggered strategy in a resource-aware way. Firstly, a derandomization technique is introduced to make sure the transition probability information is included in the finite frequency specification analysis. Then, a self-triggered policy is developed to update the control input of the system via the history measurement. Finally, sufficient conditions are deduced that guarantee the multiple range frequency performances and the reduction of computation and communication occupation for the controlled MJSs, simultanously. The cart- spring system is employed to illustrate the effectiveness of the proposed approach.

Distributed Event-Triggered Circular Formation Control for Multiple Anonymous Mobile Robots With Order Preservation and Obstacle Avoidance

This article investigates circular formation control problems for a group of anonymous mobile robots in the plane, where all robots can converge asymptotically to a predefined circular orbit around a fixed target point without collision, and maintain any desired relative distances from their neighbors. Given the limited resources for communication and computation of robots, a distributed event-triggered method is firstly designed to reduce dependence on resources in multi-robot systems, where the controller's action is determined by whether the norm of the event-trigger function exceeds zero through continuous sampling. And then, to further minimize communications costs, a self-triggered strategy is proposed, which only uses discrete states sampled and sent by neighboring robots at their event instants. Furthermore, for the two proposed control laws, a Lyapunov functional is constructed, which allows sufficient stability conditions to be obtained on the circular formation for multi-robot systems. And at the same time, the controllers are proved to exclude Zeno behavior. At last, numerical simulation of controlling uniform and non-uniform circular formations by two control methods are conducted. Simulation results show that the designed controller can control all mobile robots to form either a uniform circular formation or a non-uniform circular formation while maintaining any desired relative distances between robots and guaranteeing that there is no collision during the whole evolution. One of the essential features of the proposed control methods is that they reduce the update rates of controllers and the communication frequency between robots. And also, the spatial order of robots is also preserved throughout the evaluation of the system without collision.