Analysis and synthesis of networked control systems: A survey of recent advances and challenges

A networked control system (NCS) is a control system which involves a communication network. In NCSs, the continuous-time measurement is usually sampled and quantized before transmission. Then, the measurement is transmitted to the remote controller via the communication channel, during which the signal may be delayed, lost or even sometimes not allowed for transmission due to the communication or energy constraints. In recent years, the modeling, analysis and synthesis of networked control systems (NCSs) have received great attention, which leads to a large number of publications. This paper attempts to present an overview of recent advances and unify them in a framework of network-induced issues such as signal sampling, data quantization, communication delay, packet dropouts, medium access constraints, channel fading and power constraint, and present respective solution approaches to each of these issues. We draw some conclusions and highlight future research directions in end.     

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.     

轮毂电机网络化控制器研究

对四轮独立驱动汽车中轮毂电机的控制需求进行分析,设计了一种具有FlexRay总线接口的网络化轮毂电机控制器,给出了控制器的软硬件结构和通信协议; 采用广义预测控制策略对轮毂电机速度环控制参数进行校正,以消除负载扰动对轮毂电机控制过程的影响,通过对指令数据进行实时预测,补偿传输过程中的延迟和丢包带来的数据缺失;构建了测试平台,通过模拟指令突变、负载扰动及数据丢包等工况,对所设计的网络化轮毂电机控制器性能进行评估。实验结果表明,预测控制策略和指令实时预测能使轮毂电机控制系统获得准确稳定的控制性能。     

Robust control of saturating systems with Markovian packet dropouts under distributed MPC

In this work, a robust control methodology is presented for saturating systems with packet dropouts under distributed model predictive control framework. The sequence of time instants when data dropout happens is modeled by a Markov chain. A packet dropout compensation strategy and an augmented Markov jump linear model are considered simultaneously. To design distributed model predictive controllers, the entire system is decomposed into coupled subsystems. Considering the influences of neighbor subsystems, a distributed predictive control synthesis involving packet dropouts and Markovian probabilities is developed by minimizing the worst-case performance index at each time instant. The input saturation constraints are also incorporated into the robust controller design under distributed model predictive control framework. Furthermore, both the recursive feasibility of the proposed robust control under distributed model predictive control and the closed-loop mean-square stability are proved. To show the effectiveness, the proposed methodology is validated by simulations on a continuous stirred tank reactor process and a DC control system.     

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.     

Consensus-based filter designing for wireless sensor networks with packet loss

In wireless sensor networks (WSNs) and networked control systems (NCSs), packet loss is inevitable. If the node loses measurement data, then the estimation performance will degrade. In this paper, a novel filter based on consensus algorithm for packet loss is designed in order to increase estimation accuracy and reliability. Firstly we develop a packet loss model with a stochastic Bernoulli binary switching sequence. Then a novel filter that can tolerate packet loss is designed with consensus strategy and the orthogonal analysis approach. Finally, simulation results show that comparing with existing methods the proposed filter has superior performance.     

Two-layer networked learning control using self-learning fuzzy control algorithms

Since the existing single-layer networked control systems have some inherent limitations and cannot effectively handle the problems associated with unreliable networks, a novel two-layer networked learning control system （NLCS） is proposed in this paper. Its lower layer has a number of local controllers that are operated independently, and its upper layer has a learning agent that communicates with the independent local controllers in the lower layer. To implement such a system, a packet-discard strategy is firstly developed to deal with network-induced delay and data packet loss. A cubic spline interpolator is then employed to compensate the lost data. Finally, the output of the learning agent based on a novel radial basis function neural network （RBFNN） is used to update the parameters of fuzzy controllers. A nonlinear heating, ventilation and air-conditioning （HVAC） system is used to demonstrate the feasibility and effectiveness of the proposed system.     

Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach

In this paper, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and partial least squares (PLS) approaches are applied to predictive control of a drying process. In the proposed approaches, the PLS analysis is used to pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of this study is assigned to the modeling with the aim at predicting the granule particle size and executing by ANFIS and ANN. ANN holds the promise of being capable of producing non-linear models, being able to work under noise conditions, and being fault tolerant to the loss of neurons or connections. Also, the ANFIS approach combines the advantages of fuzzy system and artificial neural network to design architecture and is capable of dealing with both limitation and complexity in the data set. The efficiencies of ANFIS and ANN approaches in prediction are compared and the superior approach is selected. Finally, by deploying the preferred approach, several scenarios are presented to be used in predictive control of spray drying as an accurate, fast running, and inexpensive tool. This is the first study that presents a flexible intelligent approach for predictive control of drying process by ANN, ANFIS, and PLS. The approach of this study may be easily applied to other production process.     

Distributed synchronization control of complex networks with communication constraints

This paper is concerned with the distributed synchronization control of complex networks with communication constraints. In this work, the controllers communicate with each other through the wireless network, acting as a controller network. Due to the constrained transmission power, techniques such as the packet size reduction and transmission rate reduction schemes are proposed which could help reduce communication load of the controller network. The packet dropout problem is also considered in the controller design since it is often encountered in networked control systems. We show that the closed-loop system can be modeled as a switched system with uncertainties and random variables. By resorting to the switched system approach and some stochastic system analysis method, a new sufficient condition is firstly proposed such that the exponential synchronization is guaranteed in the mean-square sense. The controller gains are determined by using the well-known cone complementarity linearization (CCL) algorithm. Finally, a simulation study is performed, which demonstrates the effectiveness of the proposed design algorithm.     

Novel delay-partitioning stabilization approach for networked control system via Wirtinger-based inequalities

This paper studies the problems of stability analysis and state feedback stabilization for networked control system. By developing a novel delay-partitioning approach, the information on both the range of network-induced delay and the maximum number of consecutive data packet dropouts can be taken into full consideration. Various augmented Lyapunov–Krasovskii functionals (LKFs) with triple-integral terms are constructed for the two delay subintervals. Moreover, the Wirtinger-based inequalities in combination with an improved reciprocal convexity are utilized to estimate the derivatives of LKFs more accurately. The proposed approaches have improved the stability conditions without increasing much computational complexity. Based on the obtained stability criterion, a stabilization controller design approach is also given. Finally, four numerical examples are presented to illustrate the effectiveness and outperformance of the proposed approaches.     

Fuzzy model predictive control of non-linear processes using convolution models and foraging algorithms

In this paper, a fuzzy model predictive control (FMPC) approach is introduced to design a control system for nonlinear processes. The proposed control strategy has been successfully employed for representative, benchmark chemical processes. Each nonlinear process system is described by fuzzy convolution models, which comprise a number of quasi-linear fuzzy implications (FIs). Each FI is employed to describe a fuzzy-set based relation between control input and model output. A quadratic optimization problem is then formulated, which minimizes the difference between the model predictions and the desired trajectory over a predefined predictive horizon and the requirement of control energy over a shorter control horizon. The present work proposes to solve this optimization problem by employing a contemporary population-based evolutionary optimization strategy, called the Bacterial Foraging Optimization (BFO) algorithm. The solution of this optimization problem is utilized to determine optimal controller parameters. The utility of the proposed controller is demonstrated by applying it to two non-linear chemical processes, where this controller could achieve better performances than those achieved by similar competing controller, under various operating conditions and design considerations. Further comparisons between various stochastic optimization algorithms have been reported and the efficacy of the proposed approach over similar optimization based algorithms has been concluded employing suitable performance indices.     

One-step receding horizon H(∞) control for networked control systems with random delay and packet disordering

The receding horizon H(∞) control (RHHC) problem is investigated in this paper for a class of networked control systems (NCSs) with random delay and packet disordering. A new model is proposed to describe the NCS with random delay which may be larger than one sampling period. The random delay is modeled as a Markov chain while the closed-loop system is described as a Markovian jump system. Sufficient conditions for the closed-loop NCS to be stochastically stable and the performance index to be upper bounded are derived by using the receding optimization principle. Furthermore, by solving a semi-definite programming (SDP) with linear matrix inequalities (LMIs) constraint, a piecewise-constant receding horizon H(∞) controller is obtained, and the designed piecewise-constant controller ensures that the closed-loop NCS achieves a prescribed H(∞) disturbance attenuation level. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.     

Effects of wireless packet loss in industrial process control systems

Timely and reliable sensing and actuation control are essential in networked control. This depends on not only the precision/quality of the sensors and actuators used but also on how well the communications links between the field instruments and the controller have been designed. Wireless networking offers simple deployment, reconfigurability, scalability, and reduced operational expenditure, and is easier to upgrade than wired solutions. However, the adoption of wireless networking has been slow in industrial process control due to the stochastic and less than 100% reliable nature of wireless communications and lack of a model to evaluate the effects of such communications imperfections on the overall control performance. In this paper, we study how control performance is affected by wireless link quality, which in turn is adversely affected by severe propagation loss in harsh industrial environments, co-channel interference, and unintended interference from other devices. We select the Tennessee Eastman Challenge Model (TE) for our study. A decentralized process control system, first proposed by N. Ricker, is adopted that employs 41 sensors and 12 actuators to manage the production process in the TE plant. We consider the scenario where wireless links are used to periodically transmit essential sensor measurement data, such as pressure, temperature and chemical composition to the controller as well as control commands to manipulate the actuators according to predetermined setpoints. We consider two models for packet loss in the wireless links, namely, an independent and identically distributed (IID) packet loss model and the two-state Gilbert-Elliot (GE) channel model. While the former is a random loss model, the latter can model bursty losses. With each channel model, the performance of the simulated decentralized controller using wireless links is compared with the one using wired links providing instant and 100% reliable communications. The sensitivity of the controller to the burstiness of packet loss is also characterized in different process stages. The performance results indicate that wireless links with redundant bandwidth reservation can meet the requirements of the TE process model under normal operational conditions. When disturbances are introduced in the TE plant model, wireless packet loss during transitions between process stages need further protection in severely impaired links. Techniques such as retransmission scheduling, multipath routing and enhanced physical layer design are discussed and the latest industrial wireless protocols are compared.     

Adaptive sampling rate control for networked systems based on statistical characteristics of packet disordering

This paper investigates an adaptive sampling rate control scheme for networked control systems (NCSs) subject to packet disordering. The main objectives of the proposed scheme are (a) to avoid heavy packet disordering existing in communication networks and (b) to stabilize NCSs with packet disordering, transmission delay and packet loss. First, a novel sampling rate control algorithm based on statistical characteristics of disordering entropy is proposed; secondly, an augmented closed-loop NCS that consists of a plant, a sampler and a state-feedback controller is transformed into an uncertain and stochastic system, which facilitates the controller design. Then, a sufficient condition for stochastic stability in terms of Linear Matrix Inequalities (LMIs) is given. Moreover, an adaptive tracking controller is designed such that the sampling period tracks a desired sampling period, which represents a significant contribution. Finally, experimental results are given to illustrate the effectiveness and advantages of the proposed scheme.     

不可靠WSN时钟同步网络化输出反馈MPC量化分析

在Cyber-Physical环境下,时钟同步双向信息交换过程中,包含时钟信息的数据包丢失将对时钟同步性能产生影响。讨论了现代控制理论状态空间模型的输出反馈Tubes-MPC时钟同步方法。由分离原理,设计了本地化的状态估计器与控制器,实现了输出反馈Tubes-MPC时钟同步的指数稳定。以不完全量测下的观测模型为基础,定量分析了统计意义下的同步误差方差上界与下界,并采用MPC中Set-Theory-in-Control方法,将完全量测下的干扰误差集合运算于由丢包所引入的附加的估计误差集合,建立了集合约束下的模型预测优化模型。已构建的统一框架下的输出反馈Tubes-MPC时钟同步系统化方法,综合考量了控制理论在线计算复杂度与网络控制观点应用的可行性,对无线网络的不可靠性、网络规模、收敛性能具有鲁棒性,进一步容易扩展为网络级绝对时钟状态空间模型。     

Robust output feedback distributed model predictive control of networked systems with communication delays in the presence of disturbance

In this work, an output feedback cooperative distributed model predictive control is developed for a class of networked systems composed of interacting subsystems interconnected through their states, in which it handles bounded disturbances and time varying communication delays. A distributed buffer based prediction strategy is used to compensate bounded delays and predict those states, which are coupled between subsystems that their actual values may not available due to delays. In the design of robust distributed model predictive control, distributed moving horizon estimation is employed so that convergence and boundedness of the estimation error are ensured. Furthermore, robust exponential stability of the closed loop system is established. The effectiveness of the proposed method is illustrated using two interconnected continuous stirred tank reactors.     

Application of Wavelet Packet Energy Spectrum to Extract the Feature of the Pulse Signal

The wavelet packet is presented as a new kind of multi-scale analysis technique followed by Wavelet analysis. The fundamental and realization arithmetic of the wavelet packet analysis method are described in this paper. A new application approach of the wavelet packed method to extract the feature of the pulse signal from energy distributing angle is expatiated. It is convenient for the microchip to process and judge by using the wavelet packet analysis method to make the pulse signals quantized and analyzed. Kinds of experiments are simulated in the lab, and the experiments prove that it is a convenient and accurate method to extract the feature of the pulse signal based on wavelet packed-energy spectrum analysis.     

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.     

Model-free adaptive integral terminal sliding mode predictive control for a class of discrete-time nonlinear systems

In this paper, a new model-free adaptive digital integral terminal sliding mode predictive control scheme is proposed for a class of nonlinear discrete-time systems with disturbances. The characteristic of the proposed control approach is easy to be implemented because it merely adopts the input and output data model of the system based on compact form dynamic linearization (CFDL) data-driven technique, while the technique of perturbation estimation is applied to estimate the disturbance term of the system. Moreover, by means of combining model predictive control and CFDL digital integral terminal sliding mode control (CFDL-DITSMC), the CFDL digital integral terminal sliding mode predictive control (CFDL-DITSMPC) method is proposed, which can further improve the tracking accuracy and disturbance rejection performance in comparison with the CFDL model-free adaptive control, neural network quasi-sliding mode control and the CFDL-DITSMC scheme. Meanwhile, the stability of the proposed approach is guaranteed by theoretical analysis, and the effectiveness of the proposed method is also illustrated by numerical simulations and the experiment on the two-tank water level control system.     

Covariance upperbound controllers for networked control systems

This paper deals with designing covariance upperbound controllers for a linear system that can be used in a networked control environment in which control laws are calculated in a remote controller and transmitted through a shared communication link to the plant. In order to compensate for possible packet losses during the transmission, two different techniques are often employed: the zero-input and the hold-input strategy. These use zero input and the latest control input, respectively, when a packet is lost. For each strategy, we synthesize a class of output covariance upperbound controllers for a given covariance upperbound and a packet loss probability. Existence conditions of the covariance upperbound controller are also provided for each strategy. Through numerical examples, performance of the two strategies is compared in terms of feasibility of implementing the controllers.