Distributed Kalman filters with adaptive strategy for linear time‐varying interconnected systems

We consider the problem of distributed state estimation over a sensor network in which a set of nodes collaboratively estimates the state of a continuous‐time linear time‐varying system. In particular, our work focuses on the benefits of weight adaptation of the interconnection gains in distributed Kalman filters. To this end, an adaptation strategy is proposed with the adaptive laws derived via a Lyapunov‐redesign approach. The justification for the gain adaptation stems from a desire to adapt the pairwise difference of state estimates as a function of their agreement, thereby enforcing an interconnection‐dependent gain. In the proposed scheme, an adaptive gain for each pairwise difference of the interconnection terms is used in order to address edge‐dependent differences in the state estimates. Accounting for node‐specific differences, a special case of the scheme is also presented, where it uses a single adaptive gain in each node estimate and which uniformly penalizes all pairwise differences of state estimates in the interconnection term. The filter gains can be designed either by standard Kalman filter or Luenberger observer to construct the adaptive distributed Kalman filter or adaptive distributed Luenberger observer. Stability of the schemes has been shown, and it is not restricted by the graph topology and therefore the schemes are applicable to both directed and undirected graphs. The proposed algorithms offer a significant reduction in communication costs associated with information flow by the nodes. Finally, numerical studies are presented to illustrate the performance and effectiveness of the proposed adaptive distributed Kalman filters. Copyright © 2015 John Wiley & Sons, Ltd.     

不确定组合时滞系统的鲁棒分散可靠控制

研究一类不确定组合时滞系统的状态反馈鲁棒分散可靠控制问题,系统包含状态时滞,非线性扰动和参数不确定性。目的是设计分散线性无记忆状态反馈控制器,使得对于任意容许的不确定性以及在每一个子系统中预先指定的执行器子集合内执行器的失效,相应的闭环系统渐近稳定。最后给出一个数值例子验证了本文所结结果的有效性。     

不确定时滞组合系统基于状态观测器的鲁棒分散可靠控制

研究一类包含状态时滞和参数不确定性的不确定时滞组合系统基于状态观测器的鲁棒分散可靠控制问题。目的是设计分散状态观测器和基于此观测器的线见性动态输出反馈控制器，使得对于任意容许的不确定性以及在每一个子系统中预先指定执行器子集合内执行器的失效，相应的闭环系统渐近稳定。     

不确定关联时滞广义大系统的分散鲁棒镇定

为了研究一类不确定性关联时滞广义大系统的分散鲁棒镇定问题,根据广义系统理论,采用线性矩阵不等式方法,研究了不确定性关联时滞广义大系统的鲁棒稳定性和分散鲁棒镇定控制器设计,分别导出了不确定性关联时滞广义大系统的鲁棒稳定和分散鲁棒镇定的充分条件,且都是一组严格的线性矩阵不等式。当条件成立时给出了分散鲁棒反馈控制律的表示式,并用数值实例对所得结果加以仿真,结果表明所得方法是有效的。     

Adaptive observer design for nonlinear interconnected systems by the application of LaSalle's theorem

In this article, a class of nonlinear interconnected systems with uncertain time varying parameters (TVPs) is considered. Both the interconnections and the isolated subsystems are nonlinear. The differences between the unknown TVPs and their corresponding nominal values are assumed to be bounded where the nominal value is not required to be known. A dynamical system is proposed and then, the error systems between the original interconnected system and the designed dynamical system are analysed. A set of conditions is developed such that the augmented systems formed by the error dynamical systems and the designed adaptive laws are uniformly ultimately bounded. Specifically, the state observation errors are asymptotically convergent to zero based on the LaSalle's Theorem while the parameter estimation errors are uniformly ultimately bounded, and the classical condition of persistent excitation is not required. A case study on a coupled inverted pendulum system is presented to demonstrate the developed methodology, and simulation shows that the proposed approach is effective and practicable.     

Adaptive fault tolerant control for a class of multi‐input multi‐output nonlinear systems with both sensor and actuator faults

Compared with the fault diagnosis, detection, and isolation literature, very few results are available to discuss control algorithms directly for multi‐input multi‐output nonlinear systems with both sensor and actuator faults in the fault tolerant control literature. In this work, we present a fault tolerant control algorithm to address the system output stabilization problem for a class of multi‐input multi‐output nonlinear systems with both parametric and nonparametric uncertainties, subject to sensor and actuator faults that can be both multiplicative and additive. All elements of the sensor measurements and actuator components can be faulty. Besides, the control input gain function is not fully known. Backstepping method is used in the analysis and control design. We show that under the proposed control scheme, uniformly ultimate boundedness of the system output is guaranteed, while all closed‐loop system signals stay bounded. In the cases where the sensor faults are only multiplicative, exponential convergence of the system state variables into small neighbourhoods around zero is guaranteed. An illustrative example on a robot manipulator model is presented in the end to further demonstrate the effectiveness of the proposed control scheme.     

Incipient sensor fault estimation and accommodation for inverter devices in electric railway traction systems

This paper proposes an incipient sensor fault estimation and accommodation method for three‐phase PWM inverter devices in electric railway traction systems. First, the dynamics of inverters and incipient voltage sensor faults are modeled. Then, for the augmented system formed by original inverter system and incipient sensor faults, an optimal adaptive unknown input observer is proposed to estimate the inverter voltages, currents and the incipient sensor faults. The designed observer guarantees that the estimation errors converge to the minimal invariant ellipsoid. Moreover, based on the output regulator via internal model principle, the fault accommodation controller is proposed to ensure that the v_od and v_oq voltages track the desired reference voltages with the tracking error converging to the minimal invariant ellipsoid. Finally, simulations based on the traction system in CRH2 (China Railway High‐speed) are presented to verify the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.     

MPC-based LFC for interconnected power systems with PVA and ESS under model uncertainty and communication delay

In this paper, a cloud-edge-end collaboration-based control architecture is established for frequency regulation in interconnected power systems (IPS). A model predictive control (MPC)-based load frequency control strategy for the IPS with photovoltaic aggregation and energy storage systems under model uncertainty and communication delay is proposed. This can efectively overcome the issues of model uncertainty, random load perturbation and communication delay. First, a state space model for the IPS is constructed. To coordinate the frequency and contact line power fuctuation of the IPS, a robust controller based on the theory of MPC is then designed. Then, considering the communication delay of frequency response commands during transmission, a predictive compensation mechanism is introduced to eliminate the efect of delay while considering model uncertainty. Finally, simulation results verify the efectiveness and robustness of the proposed control strategy.     

MPC-based LFC for interconnected power systems with PVA and ESS under model uncertainty and communication delay

In this paper, a cloud-edge-end collaboration-based control architecture is established for frequency regulation in interconnected power systems (IPS). A model predictive control (MPC)-based load frequency control strategy for the IPS with photovoltaic aggregation and energy storage systems under model uncertainty and communication delay is proposed. This can efectively overcome the issues of model uncertainty, random load perturbation and communication delay. First, a state space model for the IPS is constructed. To coordinate the frequency and contact line power fuctuation of the IPS, a robust controller based on the theory of MPC is then designed. Then, considering the communication delay of frequency response commands during transmission, a predictive compensation mechanism is introduced to eliminate the efect of delay while considering model uncertainty. Finally, simulation results verify the efectiveness and robustness of the proposed control strategy.     

Robust weighted H∞ filtering for networked systems with intermittent measurements of multiple sensors

In this paper, we investigate the robust weighted H_∞ filtering problem for networked systems with intermittent measurements under the discrete‐time framework. Multiple outputs of the plant are measured by separate sensors, each of which has a specific failure rate. Network‐induced delay, packet dropouts and network‐induced disorder phenomena are all incorporated in the modeling of the network link. The resulting closed‐loop system involves both delayed noise and non‐delayed noise. In order to make full use of the delayed information, we define a weighted H_∞ performance index. Sufficient delay‐dependent and parameter‐dependent conditions for the existence of the filter and the solvability of the addressed problem are given via a set of linear matrix inequalities. Two simulation examples are presented to illustrate the relationship between the minimal performance level and the weighting factor, which show the effectiveness of the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.     

Design of disturbance observer based on adaptive-neural control for large-scale time-delay systems in the presence of actuator fault and unknown dead zone

This article presents an adaptive neural compensation scheme for a class of large-scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov–Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.     

Finite‐time dissipativity‐based filter design for networked control systems

This paper investigates the problem of finite‐time boundedness and dissipativity‐based filter design for networked control systems together with parameter uncertainties and random packet dropouts. The packet transmission information is defined by using Bernoulli distributed white sequence which characterizes the measurement conditions. Some new sufficient conditions are established to ensure that the filtering error system is stochastically finite‐time bounded and strictly finite‐time dissipative. These sufficient conditions to design the filter parameters are derived by using linear matrix inequalities and reciprocally convex approach. Finally, an example is given to validate the effectiveness of the proposed filter design.     

具有数据丢失二维Roesser系统的量化状态反馈控制

探讨存在数据丢失二维Roesser系统的量化状态反馈控制器设计问题。假设二维系统在网络控制系统中运行且系统状态全部可测,测量到的状态数据量化后经网络进行传输,同时由于网络自身限制伴随数据包丢失现象的发生。首先将数据丢失描述成取值为0或1的随机伯努利序列,定义了随机意义下二维系统的均方稳定性。其次,采用扇形界方法处理数据量化造成的误差,给出一个闭环系统均方渐进稳定的充分条件,基于该条件可通过求解线性矩阵不等式设计状态反馈控制器。最后,仿真示例验证了所提设计方法的有效性。     

广义大系统的渐近稳定与镇定

针对广义大系统的渐近稳定与镇定问题,利用广义Lyapunov函数方法,研究了广义大系统的渐近稳定与镇定,给出了渐近稳定的判定定理,并设计了适当的反馈律。通过实例计算表明,该定理实现了广义大系统的镇定,且方法简单、直观具有可行性。     

Decentralized adaptive output‐feedback control for stochastic interconnected systems with stochastic unmodeled dynamic interactions

In this paper, we consider the problem of decentralized adaptive output‐feedback regulation for stochastic nonlinear interconnected systems with unknown virtual control coefficients, stochastic unmodeled dynamic interactions. The main contributions of the paper are as follows: (1) This paper presents the first result on decentralized output‐feedback control for stochastic nonlinear systems with unknown virtual control coefficients; (2) For stochastic interconnected systems with stochastic integral input‐to‐state stable unmodeled dynamics, and more general nonlinear uncertain interconnections which depend upon the outputs of subsystems and the stochastic unmodeled dynamics, a decentralized output‐feedback controller is designed to drive the outputs and states to the origin almost surely. Copyright © 2011 John Wiley & Sons, Ltd.     

Event-triggered Kalman consensus filter for sensor networks with intermittent observations

This article investigates the event-triggered Kalman consensus filtering (ET-KCF) problem for distributed sensor networks with intermittent observations. First, a novel ET consensus filtering structure is designed for sensor networks with intermittent observations. With the proposed consensus filtering structure, a new ET mechanism that is more efficient than the existed ones is designed to schedule transmissions of local estimates. Then, an optimal ET-KCF in the sense of minimum mean-square error is developed. For reducing the computational complexity of filtering algorithm, a suboptimal ET-KCF is further proposed. Moreover, the stability of the suboptimal ET-KCF is analyzed. Simulation results verify the validity and superiority of the proposed method.     

Energy efficient nonfragile control protocol for nonlinear large-scale systems with input quantization

The problem of robust stabilization for a class of nonlinear large-scale systems with the energy constraints and time-varying actuator faults is addressed in this article. In the considered system, the stabilization criteria is achieved via a sensor-network-based distributed fault-tolerant controller. Moreover to limit the consumption of energy by the sensors, the measurement size reduction technique and communication rate reduction approach are implemented such that the resulting closed-loop system is stable within a predefined extended passive performance index level. In particular, the sensor signal quantization technique is used to reduce the frequency and size of the transmitted data packet in the communication sequence. Based on the switched system theory and Lyapunov approach, a new set of sufficient conditions is obtained in the form of linear matrix inequality constraints to ensure the exponential stabilization of the considered system. The developed fault-tolerant resilient control gains are designed by resorting to the cone complementarity linearization algorithm. Moreover, a nonisothermal continuous stirred-tank reactor with interconnection is considered as an example to demonstrate the effectiveness of the proposed design technique.     

Fault detection filter design for networked control systems with time‐varying sampling periods and packet dropouts

This paper investigates the problem of fault detection for networked control systems under simultaneous consideration of time‐varying sampling periods and packet dropouts. By taking time‐varying sampling periods into consideration, a new closed‐loop model for the considered networked control systems is established. The sampling period switching‐based approach and the parameter uncertainty‐based approach are adopted to deal with time‐varying sampling periods. Based on the established model, the observer‐based fault detection filter design criteria are proposed to asymptotically stabilize the residual system in the sense of mean‐square. The designed observer‐based fault detection filter can guarantee the sensitivity of the residual signal to faults. The simulation results illustrate the effectiveness of the obtain results. Copyright © 2015 John Wiley & Sons, Ltd.     

Observer-based adaptive event-triggered neural tracking control for nonlinear cyber-physical systems with incomplete measurements

In this paper, an adaptive event-triggered neural networks (NNs) tracking control problem is investigated for cyber-physical Systems (CPSs) with incomplete measurements. The state variables can get unavailable or distorted in incomplete measurements because of data transmission problems, which can degrade the performance of the system. To solve these problems, the radial basis function neural networks (RBF NNs) control is used to approximate the unknown nonlinear function in CPSs, and the Butterworth Low-pass Filter (LPF) is used to construct the NNs observer, which can estimate the immeasurable states. By using the Lyapunov function, the tracking error of the controller has limited to a small boundary. Based on backstepping control theory and event-triggered theory, the control signal of the fixed threshold strategy is obtained and two adaptive controllers for CPSs are established, it can ensure that all the closed-loop signals are uniformly ultimately bounded (UUB) in mean square and avoid the Zeno-behavior. The simulation results confirm the feasibility and effectiveness of the controller.     

Neural observer-based small fault detection and isolation for uncertain nonlinear systems

Small faults (some weak faults with a tiny magnitude) are difficult to detect and may cause severe problems leading to degrading the system performance. This paper proposes an approach to estimate, detect, and isolate small faults in uncertain nonlinear systems subjected to model uncertainties, disturbances, and measurement noise. A robust observer is developed to alleviate the lack of full state measurement. Using the estimated state, a dynamical radial basis function neural networks observer is designed in form of LMI problem to accurately learn the function of the inseparable mixture between modeling uncertainty and the small fault. By exploiting the knowledge obtained by the learning phase, a bank of observers is constructed for both normal and fault modes. A set of residues is achieved by filtering the differences between the outputs of the bank of observers and the monitored system output. Due to the noise dampening characteristics of the filters and according to the smallest residual principle, the small faults can be detected and isolated successfully. Finally, rigorous analysis is performed to characterize the detection and isolation capabilities of the proposed scheme. Simulation results are used to prove the efficacy and merits of the proposed approach.