Proceedings of the 2011 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems March 20-23, 2011, Kunming, China
Codesign of Networked Control Systems: A Review from Different Perspectives Jiafu Wan*, a, b, Di Li c, Yuqing Tu c, Ping Zhang d
Hehua Yan a
College of Information and Engineering Guangdong Jidian Polytechnic Guangzhou, China
[email protected]
b
College of Automation Science and Engineering, c School of Mechanical and Automotive Engineering, d School of Computer Science and Engineering, South China University of Technology * Corresponding Author,
[email protected]
codesign, and the codesign between mechanical design and electrical design [4-7]. In recent years, the information sciences including control science and engineering, network engineering, and computer science have been incorporated into the design of NCSs[8, 9]. In this paper, we will explore an emerging field in terms of the codesign of control, computing, and communications (3C), which is motivated by the above requirements. From different perspectives, the majority of codesign methods for NCSs can be generally divided into three categories under some implied conditions: 1) CPU scheduling, 2) network bandwidth allocation, and 3) control theoretic approaches [10]. For example, when the control algorithm for codesign is exploited, CPU scheduling and network resource allocation are assumed to be known, and the operational modes are adopted by default. Although this assumption allows the control community to focus on its own problem domain without worrying about how task is scheduled and network resource is allocated, the control task does not always utilize the available computing and communication resources in an optimal way. In addition, from 3C perspectives, the some theory and methodology have been established for the codesign of NCSs, the aim in this way is to adequately utilize the system resource and optimize the quality of control (QoC) [11, 12].
Abstract—Networked control systems (NCSs) are characterized by sharing a communication network between sensors, actuators, and controllers, which involves several subjects such as control, communication, and computer sciences. To achieve the optimized system performance in unpredictable environments, a novel methodology, codesign among several subjects, is emerging in the context of integrating control, computing, and communications. The aim of this work is to provide a better understanding of this emerging methodology. Relevant research efforts from different perspectives are concisely discussed by being classified into three categories, i.e., CPU resource scheduling, network bandwidth allocation, and advanced control. Then the tools supporting codesign of NCSs are summarized. The codesign methods can be extended in many directions, and some suggestions for future work are also outlined. Keywords-codesign; networked control systems (NCSs); CPU scheduling; network bandwidth allocation; control algorithm
I. INTRODUCTION At present, networked control systems (NCSs) play an important role in modern control engineering and applications because using a sharing network has many advantages, such as higher reliability, and easier deployment and maintenance [1, 2]. Many NCSs, notably the majority of control community, are the embedded systems. Now, the requirements of functionality and performance in such systems are becoming higher in practice. Despite developing rapidly, embedded systems increasingly feature resource constraints and workload uncertainties because of increasing competition under some constraints (e.g., hardware cost and design methodology) [3]. This could cause the system workload to be highly varying so as to probably result in deteriorating the system performance. To deal with the resource constraints and improve the utilization of system resource in NCSs, codesign is necessary at different levels, e.g. hardware/software
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II. DIFFERENT PERSPECTIVES ON CODESIGN From the standpoint of technical implementation, the designers in different domains attempt to optimize the QoC from 3C perspectives respectively under some assumed conditions. Fig. 1 shows the different perspectives on codesign of NCSs. Approximately, the primary goal of computing is to ensure the certainties of task executions under CPU resource constraints. The focus of control theoretic approaches is on controller design, i.e., to design control algorithms that are tolerant to network-induced
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delay, jitter, and dropouts. The network bandwidth allocation mainly deals with how to improve network quality-of-service (QoS) such that the control system performance is guaranteed [1]. In a sense, the integration of 3C is a globally optimal method under considering all of the relevant factors, and this is a real codesign among several subjects.
and second to enhance the event-triggered system by a two-level reaction system while conserving the hard real-time capabilities. P. L. Tan et al.[22] propose a hybrid scheduling scheme for hard, soft and non-real-time tasks in the shape of hierarchical scheduling. In a similar way, in [23, 24], an innovative two-level hierarchical scheduling scheme is designed to reduce the output jitter of real-time tasks in priority-based preemptive systems. Although the open-loop scheduling has yielded some positive results, it is hardly adapted to the unpredictable environments of NCSs. 2) Closed-loop Scheduling For the closed-loop scheduling, the CPU utilization is usually chosen as the controlled variable. The feedback scheduler adjusts sampling periods or priorities of control tasks so that the CPU utilization is maintained at a desired level. In order to cope with the workload uncertainties, F. Lei et al.[25] develop a fuzzy controller to dynamically modify the priorities of control tasks according to control errors and their change rates. FXia et al. [26, 27] design the architecture of fuzzy feedback scheduling. Aside from the control loops, an outer feedback loop is introduced to implement the scheduling. The basic role of the scheduler is to dynamically regulate the sampling periods of the control loops to achieve a desired level of CPU utilization. The timing attributes of all non-control tasks cannot be changed by the feedback scheduler. In [28], a fuzzy feedback scheduling based on the look-up table method is designed for a software-based computer numerical control (CNC) system in order to improve machining accuracy. Some feedback scheduling schemes, particularly the most widely studied optimal feedback scheduling [29-31], are proposed based on the following information: 1) the control cost function as a function of sampling period for each control loop; 2) the execution times of control tasks; and 3) the CPU utilization[26]. However, some of this information may not always be available in practical systems. Considering these shortcomings, the neural network technologies are incorporated into the framework of feedback scheduling to solve the optimization problem, which is realized by offline mode in [32, 33]. Another notable work in this field is the fuzzy feedback scheduling based on output jitter, which is formulated in [34]. The scheduler that the output jitter serves as a controlled variable is designed only when both of the following criteria are met, i.e., CPU resource constraint and the limitation of jitter range. Once the system resource changes, the key periodic real-time tasks still meet the expected jitter ranges by means of dynamically regulating the task periods.
(C C PU om sc put he in du g lin g)
l tro on
C
Networked Control System
Co m
m un ic a
tio n
n ) sig n de ratio o g C te (In
Fig. 1 Different perspectives on codesign of NCSs
A.
CPU Scheduling The NCSs considered here usually consists of a set of digital control loops. Each controller in NCSs is realized as a separate period task. Therefore, the main computing resource of concern in these systems is the CPU time [3, 13]. The primary goal of CPU scheduling is to obtain the optimized QoC under CPU resource constraints. The research results in real-time scheduling theory are summarized in [14]. According to the priority of task, the algorithms include fixed-priority scheduling and dynamic-priority scheduling. From the loop point of view, the algorithms are classified into two kinds: open-loop scheduling and closed-loop scheduling [15]. In the literature, we will review the CPU scheduling from two aspects, i.e., both open-loop and closed-loop scheduling algorithms. 1) Open-loop Scheduling In 1973, Liu and Layland [16] published a paper on the scheduling of periodic tasks that is generally regarded as the foundational and most influential work. One of the algorithms is the rate monotonic (RM) scheduling, and the other is the earliest deadline first (EDF) scheduling. On this basis, some extended scheduling algorithms are modified for special real-time system environments [17-19]. These algorithms are used widely in the field of control [20]. Apart from above-mentioned algorithms, some work for open-loop scheduling has presented the two-level hierarchical scheduling schemes, e.g. [21-24]. C. Siemers et al.[21] introduce some hardware enhancements that allow first to substitute the time-triggered system by an event-triggered system,
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Further, this scheme has been applied to the CNC system [35]. In addition, D. Henriksson et al. [36] present the preliminary results on dynamic scheduling of model predictive controllers (MPCs). The control signal is obtained by online optimization of a cost function, and the MPC task may experience very large variations in execution time from sample to sample.
C.
Advanced Control for NCSs The survey of recent results on the advanced control of NCSs can be found in [2]. This survey is primarily summarized from the standpoint of control, and attempts to systematically address several key issues that make NCSs distinct from other control systems. Broadly speaking, these research results from sampling, delay, and dropout perspectives mainly include two aspects: stability of NCSs and controller synthesis. 1) Stability of NCSs In this subsection, we give a brief exposition on the effects of data sampling, network delay, and packet dropouts on the stability of NCSs. Considering a variety of assumptions, the closed-loop models are represented by Markovian jump linear systems, linear time-varying systems, switched systems, nonlinear systems with resets, asynchronous dynamical systems, linear time-invariant systems with stochastic structured uncertainty, and linear systems with delayed inputs [2, 12, 46-48]. Many of the results depend on Lyapunov-based techniques, and only provide sufficient conditions for stability of the NCSs. In unpredictable environments, the packet dropouts sometimes are unavoidable, especially in unreasonable resource allocation. Packet dropouts are usually modeled either as stochastic or deterministic phenomena [2]. The simplest stochastic model assumes that dropouts are realizations of a Bernoulli process [49]. Both finite-state Markov chains and Poisson processes are used to model correlated dropouts [50, 51]. Apart from these, the deterministic models for dropouts have also been proposed, either specified in terms of time-averages or in terms of worst-case bounds on the number of consecutive dropouts [52, 53]. 2) Controller Synthesis In [54-56], the feedback controllers with two-channel are designed for NCSs. Based on this, A. Cervin et al.[57] propose the feedback-feedforward scheduling of control tasks, and the scheduler based on a linear-quadratic (LQ) optimization of the control tasks periods is designed to change on-line the sampling periods of the controller according to the resource availability. In [58] a processor load regulation is proposed and applied for real-time control of a robot arm. In [59], some results are given using the lifting technique for output-feedback synthesis for linear parameter varying (LPV) sampled-data systems, where the sampling period may also be parameter varying. The methods to design sampling period dependent controllers have been proposed by using the H Ğ control approach LPV polytopic systems in [60-62]. Further, similar references to this are
B.
Network Bandwidth Allocation In the literature, we are only interested in the real-time Ethernet to communicate between sensors, actuators, and controllers from the standpoint of application layer. In recent years, the different communication networks used for control community have been developed and summarized in [37]. The rough application framework of real-time Ethernet exemplified by the motion control systems is given in [38, 39]. The traditional algorithms of network bandwidth allocation are designed in the context of a given network workload [40, 41]. Once the sampling periods are set, they do not vary with the system resources at runtime, which may jeopardize the QoC of NCSs in unpredictable environments. Recently, effort has been made on closed-loop network scheduling that features dynamic bandwidth allocation via sampling period adaptation, e.g. [42-44]. Most of them generally employ the fuzzy control theory, and regulate the sampling periods of control loops to maximize the overall QoC under the constraint on a pre-set level of network utilization. It is worth while to note that F. Xia et al.[1, 45] design an innovative resource assignment strategy for NCSs subject to bandwidth limitations and workload fluctuations. By means of allocating available bandwidth resources flexibly, the sampling periods and priorities of control loops are adapted simultaneously. Fig. 2 depicts the architecture of network bandwidth allocation, and the feedback scheduler consists of two main components: period adjustment and priority modification.
S:Sensor, C:Controller, A:Actuator, P:Process Fig. 2 Architecture of network bandwidth allocation
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presented in [63, 64].
control algorithm, communication protocol, and scheduling policy in the model may be modified, and the QoC shows the effect of different factors such as sampling, delay, and packet dropouts, on NCSs. According to Fig. 3, the evaluation procedure from influence analysis to implementation architecture forms the closed-loop that is convenient for studying the relationship between influencing factors and system performance and further optimizing the QoC.
III. INTEGRATED CONTROL, COMPUTING, AND COMMUNICATIONS NCSs integrate both the control technologies and communication theories. Traditionally, the control theory focuses on the study of interconnected dynamical systems linked through “ideal channels”, whereas the communication theory studies the transmission of information over “imperfect channels” [2]. Therefore, the design from the standpoint of 3C integration facilitates the optimization of QoC. The original reference about codesign is proposed in [31]. Now, most work is concentrated in control/scheduling codesign[65, 66, 11, 3] . In [65], the emerging field of integrated control and CPU-time scheduling is introduced. A. Cervin [66] designs a general feedback scheduling structure for codesign. The resources are distributed among the tasks based on feedback from the actual resource use. The tasks can be use feedforward to notify the scheduler about changes in their resource demands. M. E. M. B. Gaid et al.[11] address the problem of the optimal control and scheduling of networked control systems over limited bandwidth deterministic networks. F. Xia et al.[3] review the recent progress in this field. Additionally, some work on codesign conducts in the light of either the network scheduling or the design of controller [1, 67, 68]. Another way for codesign is realized by establishing the estimation model that integrates 3C sciences [69, 70]. Analogously, J. F. Wan et al. [71, 72] propose a model-based method to codesign of 3C as shown in Fig. 3, which includes the distributed controller nodes connected by control network. The
IV. TOOLS FOR CODESIGN The tools supporting codesign should deal with the complex dependencies among different subjects. The key is to find rational abstractions that allow control system requirements and assumptions, as well as platform constraints, to be communicated across the domains and that are useful for analysis and synthesis [73]. Apart from an increasing interest in the academic communities, there are also strong industrial needs for tools supporting a range of issues including analysis of temporal behavior, code generation, testing, and so on. But none of the available tools is enough to support the whole process from modeling to implementation in an optimized way. To sum up, the existing tools related to the area of real-time control mainly include two categories, i.e., some commercial tools and research tools in academia. A.
Commercial Tools The existing commercial tools are applied widely to automobile, aerospace and other fields, and provide a broad range of capabilities such as system modeling, estimation, etc. The ETAS Group provides comprehensive and integrated tools and solutions for the development and service of automotive ECUs [74]. Targetlink works as an add-on to Matlab/Simulink, being part of the dSPACE tool chain [75, 76]. In [77], TTTech’s time-triggered technologies well provide solutions for new functions that address complexity and enable modularity. Other commercial tools comprise National Instruments hardware and LabView software [78], Sildex [79], Quanser [80], etc.
Analysis Performance Evaluation Function
Time Characteristic
Computing Resource Constraint
Influence Analysis
Analysis Model
Network Resource Constraint
Implementation Architecture
Networked Control System
B.
Research Tools in Academia In [81-83, 73], the research tools in academia are summarized as following: AIDA and XILO from the Department of Machine Design at the Royal Institute of Technology, Sweden; TrueTime and Jitterbug from the Department of Automatic Control at Lund University, Sweden; RTSIM from the RETIS Laboratory, Pisa, Italy; Syndex and Orccad from INRIA, France; and Ptolemy II from the Department of Electrical Engineering and Computer Sciences at Berkeley, California. Some of these tools have stopped further investigation. Now, the tools, Ptolemy II and
Modeling Main Controller
Computing Multi-factor
Network Interface Communication Control Network
Network Interface
Network Interface Controller
Control Object
x x x
Controller Control Control Object
Fig. 3 Model-based method to codesign of 3C
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TrueTime, are still the focuses of study [84, 85].
applications. In this literature, we concisely review the existing research results that involve CPU scheduling, network resource allocation, and control method. On this basis, the integration of 3C is illuminated from the standpoint of optimized QoC. The related tools for codesign are discussed by being classified into commercial tools and research tools in academia. Then, we propose three research issues and encourage more insight into the integration of 3C.
V. SUGGESTIONS FOR FUTURE WORK A.
Control Theoretical Issues The long-time separation between control, communication, and computing communities introduces conservatism and leads to non-optimal solutions. The controller design adopting the variable sampling rate in NCSs has achieved important research results, but most results investigate the stability for a given worst-case interval, which leads to conservative results. This is improved by taking into account a stochastic characterization for the inter-sampling times. In addition, some flexible scheduling schemes dynamically adjust the sampling periods of control tasks during run time, which directly results in sampling jitters within control loops so as to sometimes degrade the QoC. Therefore, the theory and method of controller design must be developed to compensate for sampling jitters.
ACKNOWLEDGMENT The authors would like to thank the National Natural Science Foundation of China (No. 50875090, 50905063), National 863 Project (No. 2009AA4Z111), Key Science and Technology Program of Guangdong Province (No. 2010B010700015), China Postdoctoral Science Foundation (No. 20090460769) and Open Foundation of Guangdong Key Laboratory of Modern Manufacturing Technology (No. GAMTK201002) for their support in this research.
B.
Tools Development In order to facilitate the codesign of NCSs, in recent years, such research tools that allow co-simulation, e.g., TrueTime, RTSIM, and Ptolemy II, have begun to emerge from 3C perspectives. But every tool has shortcomings. For example, Jitterbug is only applicable to linear systems, while TrueTime needs to estimate the execution time of control tasks. Therefore, more efforts are required to enhance the capability of existing simulation tools or develop definitely new ones that integrate a variety of control algorithms, communication protocols, and scheduling policies, and support modeling, code generation, customization, estimation, etc.
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