Distributed extended state observer design and output feedback anti-disturbance control for nonlinear interconnected systems: A dynamic memory event-triggered mechanism

This paper investigates a distributed extended state observer design and output feedback anti-disturbance control for nonlinear interconnected systems (ISs) with multiple disturbances. First, a distributed extended state observer with a sparse observer communication network is established to estimate the system states and external disturbances. Then, an output feedback controller with a disturbance compensation (DC) item is designed, where a novel dynamic memory event-triggered mechanism (DMETM) is proposed to save communication resources. Third, under the proposed controller, the closed-loop signals are bounded and the system states and the estimation errors converge to zero. Additionally, the system outputs satisfy $$ performance. Finally, the simulation examples demonstrate the effectiveness of the DMETM in reducing the number of data transmissions compared to the dynamic event-triggered mechanism (ETM).     

A nonlinear observer design for an activated sludge wastewater treatment process

This paper treats the problem of estimating simultaneously the state and the unknown inputs of a class of nonlinear discrete-time systems. An observer design method for nonlinear Lipschitz discrete-time systems is proposed. By assuming that the linear part of this class of systems is time-varying, the state estimation problem of nonlinear system is transformed into a state estimation problem for LPV system. The stability analysis is performed using a Lyapunov function that leads to the solvability of linear matrix inequalities (LMIs). Performances of the proposed observer are shown through the application to an activated sludge process model.     

Nonlinear observer design for a general class of discrete-time nonlinear systems with real parametric uncertainty

This paper is a geometric study of the local observer design for a general class of discrete-time nonlinear systems with real parametric uncertainty. Explicitly, we study the observer design problem for a general class of discrete-time nonlinear systems with real parametric uncertainty and with an input generator (exosystem). In this paper, we show that for the classical case, when the state equilibrium does not change with the parametric uncertainty, and when the plant output is purely a function of the state, there is no local asymptotic observer for the plant. Next, we show that in sharp contrast to this case, for the general case of problems where we allow the state equilibrium to change with the parametric uncertainty, there typically exist local exponential observers even when the plant output is purely a function of the state. We also present a characterization and construction procedure for local exponential observers for the general class of discrete-time nonlinear systems with real parametric uncertainty under some stability assumptions. We also show that for the general class of discrete-time nonlinear systems considered, under some stability assumptions, the existence of local exponential observers in the presence of inputs implies, and is implied by the existence of local exponential observers in the absence of inputs. Finally, we generalize our results to a general class of discrete-time nonlinear systems with input generator, and with exogenous disturbance.     

Dynamic surface control for discrete-time strict-feedback systems with network-induced delays using predictive event-triggered strategy

In this paper, a novel dynamic surface control methodology with a predictive event-triggered strategy is proposed for discrete-time strict-feedback systems. In order to evaluate immeasurable state variables, a least square evaluation method is adopted. A dynamic surface controller is designed for discrete-time strict-feedback systems. When the transmission time of Sensor-to-Controller (S–C) or Controller-to-Actuator (C–A) channel increases, the stability and dynamic performance of the system deteriorates are improved by the proposed predictive event-triggered control strategy. Furthermore, the proposed methodology decreases the use of network resources while the whole system still keeps stable and exhibits an acceptable dynamic performance. The simulation results demonstrate that the proposed methodology is effective.     

Model-based periodic event-triggered stabilization for continuous-time stochastic nonlinear systems

In this paper, we investigate a model-based periodic event-triggered control framework for continuous-time stochastic nonlinear systems. In this framework, an auxiliary approximate discrete-time model of stochastic nonlinear systems is constructed in the controller module, which is utilized not only to design a discrete-time controller but also as a state predictor within trigger intervals. This discrete controller design approach, the strategy of state prediction, and the periodic detection strategy for the trigger rule not only provide a manner of more direct and easier implementation on the digital platform but also effectively reduce the communication load while a satisfactory control performance is maintained. Additionally, the mean-square exponentially stabilization for continuous-time stochastic nonlinear systems is achieved, in which a guideline for determining the maximum admissible sampling period is provided and the periodic event trigger rule is designed. The final numerical simulation also supports the effectiveness of our proposed framework.     

离散输入受限系统的增益调度事件触发和自触发控制

针对离散输入受限系统,分别设计静态和动态的增益调度事件触发和自触发控制算法.首先设计一种基于离散参量Lyapunov方程的静态增益调度事件触发控制算法,该算法通过事件触发机制更新控制增益,使得在增大闭环系统收敛速率的同时节约通讯资源.为了避免对采样状态和测量误差的连续监测,设计了相应的静态增益调度自触发控制算法;同时,...     

Input-to-state practical stability analysis and controller design of switched affine systems: An observer-based approach

This paper addresses the problems of the input-to-state practical stability (ISPS) analysis and output feedback controller design for switched affine systems (SASs) subject to external disturbances. First, a switched affine observer is developed to estimate unmeasurable states. Then by combining the sampled-data control approach, a less conservative mode-dependent dynamic event-triggered mechanism (ETM) is established. The proposed dynamic ETM cannot only avoid Zeno behavior but also reduce the network transmission burden effectively. Further, based on time-dependent Lyapunov-Krasovskii functional and state-dependent switching laws, a set of feasible ISPS conditions are presented in the LMI forms by means of singular value decomposition. The designed switching law depends upon the sampled-data information of the estimated state and gets rid of the chattering phenomenon. Finally, an application example of the DC-DC flyback converter is given to verify the efficacy of the proposed algorithm.     

Event-triggered Networked H∞ Output Tracking Control Based on Dynamic Compensation Controller

In this paper, the event-triggered H_∞ output tracking problem is investigated for networked control systems. In order to reduce the output tracking error as well as to improve network resource utilization, we propose an idea of dynamic compensation controller with the discrete-time event-triggered mechanism, that is, the integral term of tracking error and the state of the reference system are introduced to form states of the augmented system. We first examine the dynamic compensation idea by the H_∞ output tracking control problem for linear time-invariant (LTI) systems. Then, we model the closed-loop event-triggered networked control system as a time-delay augmented linear system. By constructing a Lyapunov-Krasovskii functional with the delay fractioning technique, the stability conditions with lower conservatism are derived in the form of the linear matrix inequalities (LMIs). Furthermore, a method is proposed to design the H_∞ dynamic compensation controllers and the discrete-time event-triggered mechanisms. Finally, the satellite tracking control problem is used as an example to show that the dynamical compensation idea is effective in reducing the tracking error and that the proposed method in this paper can achieve better performance than that in the existing literature.

     

Global stabilization of a class of delay discrete-time nonlinear systems via state and output feedback

This paper deals with stabilization of a class of delay discrete-time nonlinear systems through state and output feedback. We provide an explicit bounded state feedback law as an extension of the Jurdjevic-Quinn method, from nonlinear theory, to this class of systems. Next, we present a useful and systematic approach to design an observer for the same class of systems. Then, we show how the global stabilization problem via dynamic output feedback can be solved by using the two previous results. Finally, numerical examples are given to illustrate the effectiveness of the proposed design method.     

随机时延下多输入多输出多智能体系统事件触发双向编队

针对动力学模型未知的多输入多输出非线性离散时间多智能体系统的随机时延问题,提出一种输入增益补偿策略,并针对其通讯受限问题,提出一种带有死区操作器的事件触发控制机制.首先,采用伪偏导技术沿时间轴方向,在智能体的每个工作点上建立一种紧格式动态线性化数据模型,并给出该数据模型的参数估计算法.在此数据模型的基础上结合符号图论,研究智能体之间的合作与竞争关系,设计一种事件触发的数据驱动双向编队控制算法.最后,通过李雅普诺夫稳定性理论、矩阵理论以及压缩映射原理论证所提出算法的收敛性,并通过仿真实验和实物实验进一步验证该算法的正确性与有效性.     

不确定非线性系统自适应动态事件触发输出反馈镇定

本文研究了一类不确定非线性系统的动态事件触发输出反馈镇定问题. 显著不同的是系统具有依赖于不可测状态的增长且增长率为输出的未知多项式. 尽管已有一些连续自适应控制器, 但需要巧妙融合非线性状态观测器、系统未知性的动态补偿以及非线性的抵御, 因此这些控制器具有一定的脆弱性, 不能平凡地拓展到不连续情形 (采样误差导致). 为此, 首先通过引入动态高增益和基于高增益的观测器来分别抵御未知增长率和重构系统不可测状态. 进而, 意识到静态事件触发机制的无效性, 通过引入动态事件触发机制, 成功设计出了事件触发输出反馈控制器, 确保了系统状态的全局有界性和收敛性. 数值仿真验证了所设计控制器的有效性.     

Robust output tracking control for discrete-time nonlinear systems with time-varying delay: Virtual fuzzy model LMI-based approach

This paper proposes an output tracking control via virtual reference model for a class of nonlinear discrete-time systems with time-varying delay and disturbance. First, a Takagi–Sugeno (T–S) fuzzy model represents the discrete-time nonlinear system with time-varying delay. Second, a fuzzy observer design estimates immeasurable state. Third, a virtual desired variable (VDV) based tracking controller design guarantees H_∞ control performance for the overall system. Using Lyapunov’s stability analysis, we obtain sufficient conditions for stability of estimation, control, and robustness. The advantages are as follows: (i) allow unknown time-varying delay; (ii) ensure robust output tracking; (iii) provide relaxed conditions via a three-steps procedure to find observer and controller gain. To verify the theoretical derivation, we carry out numerical simulations on a stirred tank reactor system as an example. Satisfactory results validate the claims.     

多智能体系统的事件触发无模型迭代学习双向一致性

针对未知模型的非线性离散时间多智能体系统,研究基于事件触发迭代学习双向一致性问题.首先,利用紧凑形式动态线性化方法,建立多智能体系统的动态线性化数据模型,提出一种该数据模型的参数估计算法;其次,基于该数据模型设计输出观测器和死区控制器,并结合信号图论,构建一种事件触发分布式无模型迭代学习双向一致性控制策略;然后,通过设计李雅普诺夫函数对该控制策略的收敛性进行严格的证明;最后,通过数值仿真进一步验证该控制协议的正确性和有效性.     

Event-triggered-based integral reinforcement learning output feedback optimal control for partially unknown constrained-input nonlinear systems

In this paper, an adaptive output feedback event-triggered optimal control algorithm is proposed for partially unknown constrained-input continuous-time nonlinear systems. First, a neural network observer is constructed to estimate unmeasurable state. Next, an event-triggered condition is established, and only when the event-triggered condition is violated will the event be triggered and the state be sampled. Then, an event-triggered-based synchronous integral reinforcement learning (ET-SIRL) control algorithm with critic-actor neural networks (NNs) architecture is proposed to solve the event-triggered Hamilton–Jacobi–Bellman equation under the established event-triggered condition. The critic and actor NNs are used to approximate cost function and optimal event-triggered optimal control law, respectively. Meanwhile, the event-triggered-based closed-loop system state and all the neural network weight estimation errors are uniformly ultimately bounded proved by Lyapunov stability theory, and there is no Zeno behavior. Finally, two numerical examples are presented to show the effectiveness of the proposed ET-SIRL control algorithm.     

DoS攻击下网络化控制系统基于观测器的记忆型事件触发预测控制

本文研究了DoS攻击下网络化控制系统记忆型事件触发预测补偿控制问题. 首先, 由于网络带宽资源有限 和系统状态不完全可观测性, 引入了记忆型事件触发函数, 为观测器提供离散事件触发传输方案. 然后, 分析了网络 传输通道上发生的DoS攻击. 结合上述记忆型事件触发方案, 在控制节点设计一类新颖的预测控制算法, 节省网络 带宽资源并主动补偿DoS攻击. 同时, 建立了基于观测器的记忆型事件触发预测控制的闭环系统, 并且分析稳定性. 通过线性矩阵不等式(LMI)和Lyapunov稳定性理论, 建立了控制器、观测器和记忆型事件触发矩阵的联合设计方案, 并验证了该方案的可行性. 仿真结果表明, 该方案结合记忆型事件触发机制可以有效补偿DoS攻击, 节约网络带宽 资源.     

A discrete-time multivariable neuro-adaptive control for nonlinear unknown dynamic systems

First, we assume that the controlled systems contain a nonlinear matrix gain before a linear discrete-time multivariable dynamic system. Then, a forward control based on a nominal system is employed to cancel the system nonlinear matrix gain and track the desired trajectory. A novel recurrent-neural-network (RNN) with a compensation of upper bound of its residue is applied to model the remained uncertainties in a compact subset /spl Omega/. The linearly parameterized connection weight for the function approximation error of the proposed network is also derived. An e-modification updating law with projection for weight matrix is employed to guarantee its boundedness and the stability of network without the requirement of persistent excitation. Then a discrete-time multivariable neuro-adaptive variable structure control is designed to improve the system performances. The semi-global (i.e., for a compact subset /spl Omega/) stability of the overall system is then verified by the Lyapunov stability theory. Finally, simulations are given to demonstrate the usefulness of the proposed controller.     

Observers for state-affine systems

A system whose discrete-time model is linear in state variables and nonlinear in control variables is considered. Under the assumption of generic observability (under single experiment) an exact observer algorithm is suggested. Also, it is shown that the existence of a general nonlinear asymptotic observer implies the existence of a linear one. Two design procedures for asymptotic observers are presented. Surprisingly, one of them extends to a rather complete solution for continuous-time systems with piecewise constant controls provided discrete output measurements are more frequent than switches of control values.     

A framework for nonlinear sampled-data observer design via approximate discrete-time models and emulation

We study observer design for sampled-data nonlinear systems using two approaches: (i) the observer is designed via an approximate discrete-time model of the plant; (ii) the observer is designed based on the continuous-time plant model and then discretized for sampled-data implementation (emulation). We investigate under what conditions, and in what sense, these designs achieve convergence for the unknown exact discrete-time model. We present examples which show that designs that violate our conditions may indeed lead to instability when implemented on the exact model.     

基于区间观测器的动态系统故障诊断技术综述

介绍一种基于区间观测器的故障诊断技术.为突出区间观测器在故障诊断方面的优势,总结归纳针对不同类型系统构造的可以达到故障诊断目的的区间观测器,并讨论观测器的设计条件.例如,事件触发机制下的故障检测区间观测器,针对线性参数变化系统设计的基于区间观测器的故障检测机制,在区间观测器的框架下实现带有未知互联项的T-S模糊大系统的故障隔离,以及利用区间观测器对多智能体系统实现故障隔离.最后探讨该领域亟待解决的问题.     

Event-triggered adaptive fuzzy control for nonstrict-feedback nonlinear systems with output hysteresis

This paper concentrates upon the issue of adaptive fuzzy tracing control for a class of nonstrict-feedback nonlinear systems output with hysteresis via an event-triggered strategy. To handle the difficulty caused by the nonstrict nonlinear systems, the variable separation technique is introduced. The design difficulty of output hysteresis is addressed by employing a hysteresis inverse function and Nussbaum function to compensate unmeasurable state signal. Meanwhile, the fuzzy logic system (FLS) is used to estimate the unknown function at each step of recursion. Moreover, by devising the relative threshold event-triggered mechanism (ETM), the frequency of actuators and controllers can be largely decreased. Thus, the adaptive fuzzy event-triggered tracing control strategy is proposed by combining the barrier Lyapunov function and backstepping technique. With the proposed scheme, it is theoretically demonstrated that all signals in the closed-loop system are bounded, and the tracing errors are driven to a small neighborhood of the origin under the output constraint. Eventually, two examples demonstrate the efficacy of the proposed control strategy.