H∞ observer-based event-triggered sliding mode control for a class of discrete-time nonlinear networked systems with quantizations

This paper investigates the problem of H_∞ observer-based event-triggered sliding mode control (SMC) for a class of uncertain discrete-time Lipschitz nonlinear networked systems with quantizations occurring in both input and output channels. The event-triggered strategy is used to save the limited network bandwidth. Then, based on the zero-order-hold (ZOH) measurement, a state observer is designed to reconstruct the system state, which facilitates the design of the discrete-time sliding surface. Considering the effects of quantizations, networked-induced constraints and event-triggered scheme, the nonlinear state error dynamics and sliding mode dynamics are converted into a unified linear parameter varying (LPV) time-delay system with the aid of a reformulated Lipschitz property. By using the Lyapunov-Krasovskii functional and free weighting matrix, a new sufficient condition is derived to guarantee the robust asymptotic stability of the resulting closed-loop system with prescribed H_∞ performance. And then the observer gain, event-triggering parameter and sliding mode parameter are co-designed. Furthermore, a novel SMC law is synthesized to force the trajectories of the observer system onto a pre-specified sliding mode region in a finite time. Finally, a single-link flexible joint robot example is utilized to demonstrate the effectiveness of the proposed method.     

H∞ non-fragile observer-based dynamic event-triggered sliding mode control for nonlinear networked systems with sensor saturation and dead-zone input

This paper considers the problem of robust non-fragile observer-based dynamic event-triggered sliding mode control (SMC) for a class of discrete-time Lipschitz nonlinear networked control systems subject to sensor saturation and dead-zone input nonlinearity. First, an improved dynamic event-triggered scheme (DETS) in consideration of sensor saturation is proposed to reduce the number of data transmission. Next, a non-fragile observer is designed to estimate the system state, which facilitates the construction of the discrete sliding surface. By using a reformulated Lipschitz property, the error dynamics and sliding mode dynamics are modeled as a unified linear parameter varying (LPV) networked system with time-varying delays. Then, based on this model, sufficient conditions are established to guarantee the resulting closed-loop system to be asymptotically stable with a given disturbance attenuation level. Furthermore, an observer-based event-triggered SMC law is designed to drive the trajectories of the observer system onto a region near equilibrium point in a finite time in the presence of dead-zone input nonlinearity. Finally, two practical examples are employed to demonstrate the effectiveness of the proposed method.     

A new approach to event-triggered static output feedback control of networked control systems

This paper is concerned with the event-triggered static output feedback control of networked control systems. The event-triggered mechanism is represented by a time-delay model and some latest techniques are employed to deal with the induced time-delay. Furthermore, a novel strategy is developed to eliminate the coupling among control gain, input matrix and output matrix. With these techniques, a new sufficient condition for system stability is established in the framework of linear matrix inequalities. The effectiveness of the proposed method is shown by two numerical examples.     

Formation-containment control of networked Euler–Lagrange systems: An event-triggered framework

To improve the concurrency of leaders’ formation and followers’ containment, a difficult problem of designing the formation controller and the containment controller simultaneously should be addressed for networked systems. Motivated by this, this paper presents an even-triggered control framework for networked Euler–Lagrange systems to achieve formation-containment control even in the presence of uncertain parameters. An event-triggered formation controller is firstly designed for leaders to achieve the desired configuration. An event-triggered containment control law is then developed to guarantee that all the followers can converge to the convex hull formed by leaders. The key feature of the containment control law is that it does not necessitate any relative velocity information with respect to neighbor followers. Each controller’s gains are adaptively tuned using only local information. The parametric uncertainties are accommodated by using the adaptive updating law. Zeno behaviors of the triggering time sequences are also excluded. As a result, the communication burden of formation-containment system can be reduced. Numerical simulation is finally presented to verify the effectiveness of the proposed event-triggered formation-containment control framework.     

Event-triggered consensus control of disturbed multi-agent systems using output feedback

In this article, the event-triggered consensus control is investigated for general linear multi-agent systems with external disturbances, by using the accessible measurement outputs. In particular, a novel protocol is proposed using the local observed state variables at event-triggered instants, which are respectively derived by adopting an observer to each agent based on output signal. Then it is proved that under the designed event-triggered control protocol consensus can be achieved with the desired disturbance attenuation ability and no Zeno behavior occurs. A numerical simulation is given to demonstrate the effectiveness of the developed control strategy     

Constrained off-line synthesis approach of model predictive control for networked control systems with network-induced delays

This paper investigates the off-line synthesis approach of model predictive control (MPC) for a class of networked control systems (NCSs) with network-induced delays. A new augmented model which can be readily applied to time-varying control law, is proposed to describe the NCS where bounded deterministic network-induced delays may occur in both sensor to controller (S–A) and controller to actuator (C–A) links. Based on this augmented model, a sufficient condition of the closed-loop stability is derived by applying the Lyapunov method. The off-line synthesis approach of model predictive control is addressed using the stability results of the system, which explicitly considers the satisfaction of input and state constraints. Numerical example is given to illustrate the effectiveness of the proposed method.     

基于无线网络状态时延的控制器性能研究

为了研究无线网络控制系统在不同网络负载下时延对系统性能的影响，首先根据无线网络负载状态对网络时延进行分析，给出系统的数学模型，并结合网络负载状态对系统模型作了分析，然后给出了控制器性能指标，利用DLQR（离散线性二次最优设计）方法优化控制器，并就时延对控制性能的影响进行了分析，实现了通过对网络负载状态的调节使系统控制器性能达到最优。最后应用数学模型，通过仿真验证了该研究方法和结果的有效性。     

Distributed optimal coordination control for nonlinear multi-agent systems using event-triggered adaptive dynamic programming method

This paper is concerned with the design of distributed optimal coordination control for nonlinear multi-agent systems (NMASs) based on event-triggered adaptive dynamic programming (ETADP) method. The method is firstly introduced to design the distributed coordination controllers for NMASs, which not only avoids the transmission of redundant data compared with traditional time-triggered adaptive dynamic programming (TTADP) strategy and minimizes the performance function of each agent. The event-triggered conditions are proposed based on Lyapunov functional method, which is deduced by guaranteeing the stability of NMASs. Then a new adaptive policy iteration algorithm is presented to obtain the online solutions of the Hamiton–Jocabi–Bellman (HJB) equations. In order to implement the proposed ETADP method, the fuzzy hyperbolic model based critic neural networks (NN) are utilized to approximate the value functions and help calculate the control policies. In critic NNs, the NN weight estimations are updated at the event-triggered instants leading to aperiodic weight tuning laws so that computation cost is reduced. It is proved that the weight estimation errors and the local neighborhood coordination errors is uniformly ultimately bounded (UUB). Finally, two simulation examples are provided to show the effectiveness of the proposed ETADP method.     

Event-triggered adaptive control of a class of nonlinear systems

In this paper, the event-triggered adaptive control for a class of nonlinear systems in Brunovsky form is considered. The sensors are event-triggered thus the states are transmitted only at the discrete triggering points, which are more efficient in using communication bandwidth. To solve this problem, we design a set of event-triggered conditions and based on which the controller and parameter estimator are designed without the ISS assumption. It is shown that the proposed control schemes guarantee that all the closed-loop signals are semi-globally bounded and the stabilization error converges to the origin asymptotically. The Zeno behavior is also excluded. Simulation results illustrate the effectiveness of our scheme.     

Asynchronous update based networked predictive control system using a novel proactive compensation strategy

Networked predictive control system (NPCS) has been proposed to address random delays and data dropouts in networked control systems (NCSs). A remaining challenge of this approach is that the controller has uncertain information about the actual control inputs, which leads to the predicted control input errors. The main contribution of this paper is to develop an explicit mechanism running in the distributed network nodes asynchronously, which enables the controller node to keep informed of the states of the actuator node without a priori knowledge about the network. Based on this mechanism, a novel proactive compensation strategy is proposed to develop asynchronous update based networked predictive control system (AUBNPCS). The stability criterion of AUBNPCS is derived analytically. A simulation experiment based on Truetime demonstrates the effectiveness of the scheme.     

Networked control of industrial automation systems—a new predictive method

A new method for real-time prediction of uncertain network transmission time delays and a method for closed-loop control of manufacturing and industrial plants through networks are introduced. The proposed delay prediction method is based on the multilayer perceptron neural model. In order to minimize the number of neurons in the first layer of the network and hence reducing the computational burden in a real-time implementation, a method for determination of the Markov order of the time delay sequence is presented. Using the predicted delay, and a zero-order hold equivalent discrete-time model of the plant, a time varying state feedback control algorithm with a real-time gain updating strategy is proposed. A sufficient condition for closed-loop stability is also derived using the switching theorem for linear systems. The proposed method is shown, through two industrial networked case studies, namely, a DC motor driving a transportation roller for paper sheets and a milling machine. Simulation studies depict the efficacy of the proposed method in controlling such challenging problems.     

Improved stability and stabilization design for networked control systems using new quadruple-integral functionals

This paper investigates stability analysis and stabilization for networked control systems. By a refined delay decomposition approach, slightly different Lyapunov–Krasovskii functionals (LKFs) with quadruple-integral terms and augmented vectors containing triple-integral forms of state are constructed. New integral inequalities are proposed to estimate the cross terms from derivatives of the LKFs, which can be proved to offer tighter bounds than what the Jensen one produces theoretically. Moreover, the non-strictly proper rational functions in deriving process are fully handled via reciprocally convex approach. A state feedback controller design approach is also developed. Numerical examples and applications to practical power and oscillator systems demonstrate the superiority of the proposed criteria in conservatism reduction compared to some existing ones.     

Model predictive control of non-linear systems over networks with data quantization and packet loss

This paper studies the approach of model predictive control (MPC) for the non-linear systems under networked environment where both data quantization and packet loss may occur. The non-linear controlled plant in the networked control system (NCS) is represented by a Tagaki-Sugeno (T-S) model. The sensed data and control signal are quantized in both links and described as sector bound uncertainties by applying sector bound approach. Then, the quantized data are transmitted in the communication networks and may suffer from the effect of packet losses, which are modeled as Bernoulli process. A fuzzy predictive controller which guarantees the stability of the closed-loop system is obtained by solving a set of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method.     

Event-triggered decentralized adaptive fault-tolerant control of uncertain interconnected nonlinear systems with actuator failures

This paper investigates the event-triggered decentralized adaptive tracking problem of a class of uncertain interconnected nonlinear systems with unexpected actuator failures. It is assumed that local control signals are transmitted to local actuators with time-varying faults whenever predefined conditions for triggering events are satisfied. Compared with the existing control-input-based event-triggering strategy for adaptive control of uncertain nonlinear systems, the aim of this paper is to propose a tracking-error-based event-triggering strategy in the decentralized adaptive fault-tolerant tracking framework. The proposed approach can relax drastic changes in control inputs caused by actuator faults in the existing triggering strategy. The stability of the proposed event-triggering control system is analyzed in the Lyapunov sense. Finally, simulation comparisons of the proposed and existing approaches are provided to show the effectiveness of the proposed theoretical result in the presence of actuator faults.     

Closed-loop control of constrained discrete event systems: application to a networked automation system

In this paper, we are interested in the design of closed-loop control laws to satisfy a set of temporal constraints in discrete event systems modeled by timed event graphs (TEGs). The dynamic behavior of the TEG is represented by a system of linear equations in Max-Plus algebra. Temporal constraints are imposed on some paths of the TEG and are expressed by a set of Max-Plus linear inequalities. The proposed approach is applied to the control of a networked automation producer/consumer system under a temporal constraint. The temporal constraint to be satisfied is imposed on the response time of the considered networked automation system (NAS). The calculated control laws are causal feedbacks and can be represented by monitor places connected to the NAS model.     

Robust model predictive control design with input constraints

The paper addresses the problem of designing a robust output/state model predictive control for linear polytopic systems with input constraints. The new predictive and control horizon model is derived as a linear polytopic system. Lyapunov function approach guarantees the quadratic stability and guaranteed cost for closed-loop system. The invariant set and an algorithm approach similar to Soft Variable-Structure Control (SVSC), ensures input constraints for the model predictive plant control system. In the proposed control scheme, the required on-line computation load is significantly less than in MPC literature, which opens the possibility to use these control design schemes not only for plants with slow dynamics, but also for faster ones.     

Event-triggered distributed receding horizon control for tracking and formation of homogeneous multi-agent systems

This paper proposes an event-triggered distributed receding horizon control (DRHC) approach for the formation and tracking problems of homogeneous multi-agent systems. For each agent, an event-triggering condition, based on assumed predictive information of the neighbours, is derived from stability analysis. Considering the uncertain deviation between the assumed and true predictive information, we design a time-varying compatibility constraint for the individual optimization problem. In the event-triggered DRHC algorithm, each agent solves the optimization problem and communicates with its neighbours only when the event-triggering condition is satisfied, so the communication and computation burden are reduced. Moreover, guarantees for the recursive feasibility and asymptotic stability of the overall system are proved. A simulation example is provided to illustrate effectiveness of the proposed approach.     

一类网络控制系统的稳定性分析

在基于多包传输的网络控制系统中,传感器采用时间驱动,控制器和执行器采用事件驱动.假定传输时延可以忽略,则整个网络控制系统可以描述为一个具有N个事件的异步动态系统.该文针对受控对象状态均可测,控制律采用输出反馈的情况,利用双线性矩阵不等式方法,讨论了网络控制系统指数稳定的问题,并给出了稳定性的充分条件,最后通过仿真实验加以验证.     

基于隐马尔可夫的网络控制系统稳定性分析

讨论了一类具有随机通信时延的网络控制系统的建模及稳定性分析,其中网络诱导时延受控于一概率分布未知的马尔可夫链,其概率分布可通过Baum-Welch算法计算.基于隐马尔町夫模型理论,将采用状态反馈的闭环网络控制系统建模成跳变线性系统,给出了这类网络控制系统随机稳定的允分条件,并将状态反馈控制器的求解问题转化为线性矩阵不等式的解的问题.最后,通过一个仿真算例说明了上述判定系统稳定性条件的有效性.     

面向网络化制造的企业集成控制模型研究

在分析企业网络化集成控制的基础上,提出了基于CORBA与MAS的企业内部各异构系统的集成控制模型,构建了基于CORBA的MAS通信与协作模型和企业内部的集成控制体系结构,论述了内部通信的实现机制,并通过联网加工试验,验证了所构建的集成控制模型中的若干理论和实现技术。