On the model-based networked control for singularly perturbed systems

In this paper, the control of a two-time-scale plant, where the sensor is connected to a linear controller/actuator via a network is addressed. The slow and fast systems of singularly perturbed systems are used to produce an estimate of the plant state behavior between transmission times, by which one can reduce the usage of the network. The approximate solutions of the whole systems are derived and it is shown that the whole systems via the network control are generally asymptotically stable as long as their slow and fast systems are both stable. These results are also extended to the case of network delay.     

基于无源性分析的鲁棒控制系统设计

给出不确定非线性系统鲁棒控制的一种设计方法.经校正网络将一微分算子传递给对象后,采用bang-bang控制使余下的部分无源化,并引入适当的符号跟随系统,使直输回路严格无源.于是全反馈控制系统具有很强的鲁棒性.同时讨论了系统稳定、不稳定、逆不稳定的各种情况的设计.     

On the model-based networked control for singularly perturbed systems with nonlinear uncertainties

In this paper, the model-based networked control is addressed for a class of singularly perturbed control systems with nonlinear uncertainties. An approximate linear slow and fast control system of the plant, which can be obtained by omitting the nonlinear uncertainties, are used as a model to estimate the state behavior of the plant between transmission times. The stability of model-based networked control systems is investigated under the assumption that the controller/actuator is updated with the sensor information at constant time intervals. It is shown that there exists the allowable upper bound of the singular perturbation parameter such that the model-based networked control system is globally exponentially stable.     

Output feedback control of networked systems

This paper considers the problem of control of networked systems via output feedback. The controller consists of two parts: a state observer that estimates plant state from the output when it is available via the communication network, and a model of the plant that is used to generate a control signal when the plant output is not available from the network. Necessary and sufficient conditions for the exponential stability of the closed loop system are derived in terms of the networked dwell time and the system parameters. The results suggest simple procedures for designing the output feedback controller proposed. Numerical simulations show the feasibility and efficiency of the proposed methods.     

H2CM: A holonic architecture for flexible hybrid control systems

Several studies typically arise from the interaction of discrete planning algorithms or control and continuous processes, normally called hybrid control systems. It consists in three distinct levels, the controller, the plant and the interface. Hybrid control systems are conventionally modeled by switching patterns using the whole system instead of atomic resource. Therefore, the reconfiguration process is complex because it must take into account the system as a whole, making the hybrid control systems inflexible and more susceptible to uncertainties. The need for flexibility thus leads several teams to investigate the application of holonic paradigm to hybrid control systems. The objective of this paper is to demonstrate the possibility to apply almost directly a holonic discrete-event based reference architecture to hybrid control systems. A case study of industrial electricity generation process was taken, specifically a combined cycle plant (CCP) for verifying the proper operation of the proposed architecture.     

Input-to-state stability of switched systems and switching adaptive control

In this paper we prove that a switched nonlinear system has several useful input-to-state stable (ISS)-type properties under average dwell-time switching signals if each constituent dynamical system is ISS. This extends available results for switched linear systems. We apply our result to stabilization of uncertain nonlinear systems via switching supervisory control, and show that the plant states can be kept bounded in the presence of bounded disturbances when the candidate controllers provide ISS properties with respect to the estimation errors. Detailed illustrative examples are included.     

Distributed MPC of polytopic uncertain systems: handling quantised communication and packet dropouts

In this paper, we study the distributed model predictive control (MPC) of polytopic uncertain systems with quantised communication and packet dropouts. The model of the whole plant is divided into a certain number of incomplete subsystems. Due to the nature of the distributed control structure, there is generally a lack of information about the state of the overall system. Each subsystem shares its information with neighbour subsystems via reliable connection. Distributed MPC controllers are designed for each subsystem by solving the linear matrix inequalities optimisation problem. The distributed state feedback laws are quantised and transmitted via communication network. An iterative algorithm is presented to make coordination among distributed state feedback laws. The communication is assumed to be affected by random packet dropouts in a representation of Bernoulli distributed white sequences with known conditional probabilities. A case study is carried out to demonstrate the effectiveness of the proposed distributed MPC technique.     

一个基于神经网络模型的鲁棒自适应控制算法

对于具有不确定因素的离散非线性动态系统,通过校正神经网络预报器的输出,运用加权预报控制性能指标和网络辨识器模型局部线性化的思想,提出了一个间接鲁棒自适应神经网络控制算法,仿真研究证实了该控制策略的鲁棒性和有效性.     

Guaranteed Cost Control of Networked Control Systems Under Transmission Control Protocol with Active Queue Management

The problem of a guaranteed cost controller design of networked control systems under transmission control protocol (TCP) is derived. To improve the overall performance of the networked control system, a scheme to simultaneously control the plant and the network is presented. An analytical TCP model is used to design active queue management (AQM) to achieve the desired queue length, which is helpful in reducing the variation of the network induced delay. Moreover, a new closed loop model of networked control systems is obtained by augmenting the TCP communication model with the control plant. The guaranteed cost controller is addressed in terms of a linear matrix inequality (LMI) to render the system asymptotically stable based on Lyapunov‐Krasovskii theory and weighted technology. Finally, numerical simulations are included to demonstrate the theoretical results.     

Robust adaptive control of uncertain non-linear systems using neural networks

This paper presents a robust adaptive output feedback control design method for uncertain non-affine non-linear systems, which does not rely on state estimation. The approach is applicable to systems with unknown but bounded dimensions and with known relative degree. A neural network is employed to approximate the unknown modelling error. In fact, a neural network is considered to approximate and adaptively make ineffective unknown plant non-linearities. An adaptive law for the weights in the hidden layer and the output layer of the neural network are also established so that the entire closed-loop system is stable in the sense of Lyapunov. Moreover, the robustness of the system against the approximation error of neural network is achieved with the aid of an additional adaptive robustifying control term. In addition, the tracking error is guaranteed to be uniformly and asymptotically stable, rather than uniformly ultimately bounded, by using this additional control term. The proposed control algorithm is relatively straightforward and no restrictive conditions on the design parameters for achieving the systems stability are required. The effectiveness of the proposed scheme is shown through simulations of a non-affine non-linear system with unmodelled dynamics, and is compared with a second-sliding mode controller.     

基于无源性分析的鲁棒控制系统设计1)

An approach for the design of robust control for uncertain nonlinear systems is proposed.An appropriate differential operator is conveyed to the controlled plant by a network of cascade compensation,while its remained part is passivitized via optimum bangbang control.A proper signfollowing system is introduced to make the feedforward pass strictly passive.The whole feedback system is strongly robust.The cases for stable,unstable,inversely unstalbe plants are discussed.     

Non-fragile sampled-data control of network systems subject to time-delay

This paper studies the consensus problem of non-linear network systems subject to time-delay through aperiodic sampled-data control, which is more desirable and flexible than periodic sampling control. By receiving the relative information of the neighboring nodes, each node can obtain control input signals at the discrete sampling instants. A new stability criterion is established for network systems via using a time-dependent Lyapunov functional and the free-weighting matrix method. The maximum upper bound of sampling interval can be achieved by solving Linear matrix inequality optimization problem. Under the basic work of a non-fragile sampled-data controller, network systems with time-delay are asymptotically stable. In the end, two numerical simulation examples can be provided to illustrate the validity of the application of the proposed algorithm.     

Application of neural networks to temperature control in thermal power plants

In a thermal power plant with once-through boilers, it is important to control the temperature at the middle point where water becomes steam. However, there are many problems in the design of such a control system, due to a long system response delay, dead-zone and saturation of the actuator mechanisms, uncertainties in the system model and/or parameters, and process noise. To overcome these problems, an adaptive controller has been designed using neural networks, and tested extensively via simulations.

One of the key problems in designing such a controller is to develop an efficient training algorithm. Neural networks are usually trained using the output errors of the network, instead of using the output errors of the controlled plant. However, when a neural network is used to control a plant directly, the output errors of the network are unknown, since the desired control actions are unknown. This paper proposes a simple training algorithm for a class of nonlinear systems, which enables the neural network to be trained with the output errors of the controlled plant. The only a priori knowledge of the controlled plant is the direction of its output response. Due to its simple structure and algorithm, and good performance, the proposed controller has high potential for handling difficult problems in process-control systems.     

Concept of a crossbar switch for large-scale multiple processor systems in the field of process control

Large-scale multiple processor systems are already widely used in the field of process control. They are real-time systems and differ considerably from traditional computer systems. Such systems are used to perform a large variety of relatively complex tasks, many of them extremely time-critical. In the near future, the tasks will increase in number and size and will become even more time-critical, caused, for instance, by the expected integration of expert systems in process control. The question naturally arises as to whether or not such an increase of the requirements could be met by a straightforward enhancement of present designs [10], the interconnection network being in any case the key issue.

The paper describes the concept of a new crossbar switch design, based on the use of so-called configurational processors that would be extraordinarily well suited to the specific requirements in the field of process control. The new design principle is VLSI-compatible; the whole crossbar switch can be implemented with building blocks of one type. It is possible to arrange these components for the crossbar switch in close proximity to the processing elements, which are physically distributed throughout the whole area of a plant. This characteristic, in combination with the use of bitserial buses, allows a crossbar switch to be implemented in an advantageous manner, particularly without excessive cabling costs.     

Generating self-excited oscillations for underactuated mechanical systems via two-relay controller

A tool for the design of a periodic motion in an underactuated mechanical system via generating a self-excited oscillation of a desired amplitude and frequency by means of the variable structure control is proposed. First, an approximate approach based on the describing function method is given, which requires that the mechanical plant should be a linear low-pass filter–the hypothesis that usually holds when the oscillations are relatively fast. The method based on the locus of a perturbed relay systems provides an exact model of the oscillations when the plant is linear. Finally, the Poincaré map's design provides the value of the controller parameters ensuring the locally orbitally stable periodic motions for an arbitrary mechanical plant. The proposed approach is shown by the controller design and experiments on the Furuta pendulum.     

On singular perturbations due to fast actuators in hybrid control systems

A stability result is given for hybrid control systems singularly perturbed by fast but continuous actuators. If a hybrid control system has a compact set globally asymptotically stable when the actuator dynamics are omitted, or equivalently, are infinitely fast, then the same compact set is semiglobally practically asymptotically stable in the finite speed of the actuator dynamics. This result, which generalizes classical results for differential equations, justifies using a simplified plant model that ignores fast but continuous actuator dynamics, even when using a hybrid feedback control algorithm.     

On the model-based control of networked systems

In this paper the control of linear plants, where the sensor is connected to a linear controller/actuator via a network is addressed. Both, state and output feedback, are considered and results are derived for both continuous and discrete plants. A key idea is that knowledge of the plant dynamics is used to reduce the usage of the network. Necessary and sufficient conditions for stability are derived as simple eigenvalue tests of a well-structured test matrix, constructed in terms of the update time h, and the parameters of the plant and of its model. These tests are extended to include network delay as well.     

双率采样系统的基于观测器的网络化H∞控制

研究一类带有网络传输时滞和丢包的双率采样系统的网络化H∞控制问题. 假设对象状态变量被分成两个分向量, 同一分向量的状态变量由同一类传感器以相同周期采样, 且两类传感器的采样频率不同. 采样后的分向量分别通过非理想网络传输到控制器端. 考虑到双率采样、网络传输时滞和丢包现象, 引入同步观测器来估计对象状态并设计基于估计状态的控制器来镇定双率采样系统. 基于这个思路, 将双率采样的网络化控制系统建模为带有两个时变时滞的连续系统. 利用Lyapunov-Krasovskii泛函方法, 以矩阵不等式形式给出该系统的稳定性判据和控制器设计方法. 最后, 通过数值例子验证所提方法的有效性.     

Absolute stability of Lurie networked control systems

This paper is concerned with the absolute stability problem of networked control systems (NCSs) with the controlled plant being Lurie systems (Lurie NCSs), in which the network‐induced delays are assumed to be time‐varying and bounded. First, in consideration of both the time‐varying network‐induced delays and data packet dropouts, the Lurie NCSs can be modeled as a multiple‐delays Lurie system. Then, a delay‐dependent absolute stability condition is established by using the Lyapunov–Krasovskii method. Next, two approaches to controller design are proposed in the terms of simple algebra criteria, which are easily solved via the toolbox in Matlab. Furthermore, the main results can be extended to robust absolute stability of Lurie NCSs with the structured uncertainties, where robust absolute stability conditions and approaches to robust controller design are presented. Finally, two numerical examples are worked out to illustrate the feasibility and the effectiveness of the proposed method. Copyright © 2009 John Wiley & Sons, Ltd.     

基于快速输出采样反馈的网络控制系统滑模控制

针对网络控制系统的网络诱导时延问题,提出了一种新的滑模控制方法。通过快速采样被控对象输出,建立了具有线性时不变被控对象的网络控制系统的离散模型。在此基础上,用极点配置法给出了滑模切换面参数,设计了输出反馈离散滑模控制器。仿真结果验证了该控制方法的有效性。