Xiuxia Yin

Event-triggered controller design of nonlinear discrete-time networked control systems in T-S fuzzy model

HighlightsWe propose a more general mixed event-triggering communication scheme.Conditions for uniform ultimately bounded stability are derived in the event-triggered fuzzy control framework.A co-design algorithm is developed to design the desired fuzzy controller and event-triggering scheme simultaneously.A premise synchronizer has been delicately constructed to ensure the same premises with uniform time scales in both fuzzy model and fuzzy controller. This article is concerned with event-triggered fuzzy control design for a class of discrete-time nonlinear networked control systems (NCSs) with time-varying communication delays. Firstly, a more general mixed event-triggering scheme (ETS) is proposed. Secondly, considering the effects of the ETS and communication delays, based on the T-S fuzzy model scheme and time delay system approach, the original nonlinear NCSs is reformulated as a new event-triggered networked T-S fuzzy systems with interval time-varying delays. Sufficient conditions for uniform ultimately bound (UUB) stability are established in terms of linear matrix inequalities (LMIs). In particular, the quantitative relation between the boundness of the stability region and the triggering parameters are studied in detail. Thirdly, a relative ETS is also provided, which can be seen as a special case of the above proposed mixed ETS. As a difference from the preceding results, sufficient conditions on the existence of desired fuzzy controller are derived to ensure the asymptotic stability of the closed-loop system with reduced communication frequency between sensors and controllers. Moreover, a co-design algorithm for simultaneously determining the gain matrices of the fuzzy controller and the triggering parameters is developed. Finally, two illustrative examples are presented to demonstrate the advantage of the proposed ETS and the effectiveness of the controller design method.

Finite-time distributed event-triggered consensus control for multi-agent systems

In this paper, we study the finite-time distributed event-triggered consensus control for multi-agent systems. The controllers and the events are designed in a distributed manner, based only on local information. Compared with conventional asymptotic consensus, finite time consensus can reach consensus faster and have better disturbance rejection properties. Thus, we propose a new nonlinear distributed control protocol to achieve finite-time consensus. Moreover, in order to realize the consensus control for multi-agent systems, only the communication between the agent and its local neighbors is needed, therefore, the designed control is essentially distributed. Two sufficient conditions are proposed to reach finite-time consensus for multi-agent systems with fixed and switching network topologies, respectively. Finally, two simulation examples are presented to demonstrate the effectiveness of the theoretical results.

Adaptive periodic event-triggered consensus for multi-agent systems subject to input saturation

ABSTRACT This paper investigates the distributed adaptive event-triggered consensus control for a class of nonlinear agents. Each agent is subject to input saturation. Two kinds of distributed event-triggered control scheme are introduced, one is continuous-time-based event-triggered scheme and the other is sampled-data-based event-triggered scheme. Compared with the traditional event-triggered schemes in the existing literatures, the parameters of the event-triggered schemes in this paper are adaptively adjusted by using some event-error-dependent adaptive laws. The problem of simultaneously deriving the controller gain matrix and the event-triggering parameter matrix, and tackling the saturation nonlinearity is cast into standard linear matrix inequalities problem. A convincing simulation example is given to demonstrate the theoretical results.

MODEL-BASED EVENT-TRIGGERED PREDICTIVE CONTROL FOR NETWORKED SYSTEMS WITH DATA DROPOUT ∗

This paper proposes a novel structure of networked control systems (NCSs) with communication logic, which incorporates model-based networked control systems (MB-NCSs), predictive control, and an event-triggered communication scheme into a unified framework to consider the bandwidth reduction of the network communications. Within this framework, first an event-triggered communication scheme at the sensor side is introduced to determine whether or not the sensor measurement should be transmitted to the controller through the imperfect forward paths. Second, at the controller, a model of the plant is used to predict future state behavior of the plant, and based on the predicted state between successful transmission instants, a novel predictive event-triggering scheme is proposed to compress the size of the packetized control signals transmitted from the controller side to the actuator side through feedback paths. Finally, a unified model of NCSs is established. Based on this model, a codesign condition of th...

Stabilization of Neural-Network-Based Control Systems via Event-Triggered Control With Nonperiodic Sampled Data

This paper focuses on a problem of event-triggered stabilization for a class of nonuniformly sampled neural-network-based control systems (NNBCSs). First, a new event-triggered data transmission mechanism is designed based on the nonperiodic sampled data. Different from the previous works, the proposed triggering scheme enables the NNBCSs design to enjoy the advantages of both nonuniform and event-triggered sampling schemes. Second, under the nonperiodic event-triggered data transmission scheme, the nonperiodic sampled-data three-layer fully connected feedforward neural-network (TLFCFFNN)-based event-triggered controller is constructed, and the resulting closed-loop TLFCFFNN-based event-triggered control system is modeled as a state delay system based on time-delay system modeling approach. Then, the stability criteria for the closed-loop system is formulated using Lyapunov–Krasovskii functional approach. Third, the sufficient conditions for the codesign of the TLFCFFNN-based controller and triggering parameters are given in terms of solvability of matrix inequalities to guarantee the asymptotical stability of the closed-loop system and an upper bound on the given cost function while reducing the updates of the controller. Finally, three numerical examples are provided to illustrate the effectiveness and benefits of the proposed results.

Mode Dependent H∞ Filtering for Discrete-Time Networked Systems with Random Measurement Missing and Delays

The article investigates the H∞ filtering problem for a class of discrete-time networked systems with random measurement losses and delays. Markov chain is used here to model measurement losses and delays in a unified framework. Based on the mode-dependent Lyapunov function approach, the necessary and sufficient conditions are derived to guarantee the exponential stability with a prescribed H∞ disturbance attenuation performance for the filtering error system. By using a novel design scheme, the explicit expressions of mode dependent filter parameters are given in the form of linear matrix inequalities (LMIs) which can be readily solved by using the LMI TOOLBOX in MATLAB. At last, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method.

Observer-based control for networked systems with event-triggering predictive scheme

This paper studies the observer-based event-triggered predictive control problem for networked control systems (NCSs). First, we propose a discrete event-triggered transmission scheme for the observer by introducing a quadratic event-triggering function. Then, based on the above scheme, a novel class of event-triggered predictive control algorithms on the controller node are designed for compensating for the communication delays actively and achieving the desired control performance while using less network resources. The closed-loop systems with the proposed observer-based event-triggered predictive control scheme for the analysis are established correspondingly. The design problems of the controller and the event-triggering parameter are discussed by using the linear matrix inequality (LMI) approach and the switching Lyapunov functional method. Finally, a practical example is employed to demonstrate the compensation effect for the communication delays with the proposed scheme in this paper.

Event-triggered predictive control for networked systems with communication delays compensation

This paper addresses the event-triggered predictive control problem for networked control systems (NCSs). First, we propose a discrete event-triggered transmission scheme on the sensor node by introducing a quadratic function. Then, based on the above scheme, a novel class of event-triggered predictive control algorithms on the controller node are designed for compensating for the communication delays actively and achieving the desired control performance while using less network resources. Two cases in terms of the communication delays are considered respectively. For the two cases, the closed-loop systems with the proposed event-triggered predictive control scheme for the analysis are established correspondingly. The co-design problems of the controller and event-triggering parameter are discussed by using the linear matrix inequality (LMI) approach and the (switching) Lyapunov functional method. Finally, a practical example is employed to demonstrate the effects of the proposed scheme.

Neural network-based event-triggered control design of nonlinear continuous-time systems with variable sampling

This paper focuses on a problem of event-based stabilization for a class of sampled-data neural-network-based control systems. By using a new discrete event-triggering mechanism, an event-based sampled-data three-layer fully connected feedforward neural-network-based controller is constructed. Compared with the conventional periodic sampled-data neural-network-based control in previous works, the main advantage of this paper is that the proposed event-based sampling and transmission scheme not only reduces the updating frequency of the controller, but also guarantees the asymptotical stability of the closed-loop system without dramatically degrading the overall system performance. Based on a discontinuous Lyapunov Krasovskii functional, a convex combination technique and the Wirtinger-based integral inequality, some new criteria are derived to guarantee the asymptotical stability and certain performance of closed-loop system in terms of linear matrix inequalities (LMIs). Based on the proposed criteria, a co-design method is presented to obtain the triggering parameter and the connection weights of the neural network simultaneously while ensuring a certain system performance. Finally, simulation results are provided to show the effectiveness and advantage of the proposed theoretical results.