- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 715484, 7 pages
Control for Multirate Networked Control Systems with Deadband Scheduling
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Received 7 January 2013; Revised 17 April 2013; Accepted 26 April 2013
Academic Editor: Engang Tian
Copyright © 2013 Xiaobo Zhang and Qingwei Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This paper is concerned with the problem of control for multirate networked control systems with deadband scheduling policy and transmission delay. The transmission deadbands are set at the sensor nodes and the controller nodes to reduce the network traffic, and then the systems are modeled as discrete-time switch system with uncertain parameters. Using the Lyapunov theory, a robust controller is designed to maintain the asymptotic stability of the closed-loop systems. Finally, a simulation example is presented to show the advantage of the deadband scheduling policy and the efficiency of the proposed theoretical results.
Networked control systems (NCSs) are the complex systems in which the controlled plants, sensors, controllers, and actuators are distributed in different places and connected through a communication network . Compared with the traditional control systems, NCSs have many advantages such as the flexibility of structure, high reliability, and simple installation. As the rapid development of information technology, NCSs have been applied to various applications including mobile robots, unmanned aerial vehicles, and remote surgeries [2–5]. However, the data transmission through a network brings many challenges, such as time delay, packet dropout, and data disorder, which will cause the system performance degradation and even, make the systems unstable. In recent years, the stability analysis and controller design of NCSs have been researched by many scholars [6–10].
When the bandwidth of NCSs is constrained, the packet collision becomes more severe. It is difficult to guarantee the stabilization and acceptable performance of systems only by means of controller design [11, 12]. In this case, the network scheduling becomes particularly important. The network nodes’ unnecessary data traffic could be reduced by introducing appropriate network scheduling strategy; thus, the network-induced delay and packet dropouts can be reduced remarkably [13–17]. In , the problem of integrated design of controller and communication sequences is addressed for a class of multiple inputs multiple outputs NCSs based on switch system theory, but not taking the network-induced delay into account, and the results are extended to NCSs with transmission delay in [14, 15]. In , a coordinate design scheme of the try-once-discard (TOD) dynamic scheduling strategy and robust controller for a class of networked control systems with communication constraints and random time-delay is proposed. In , the wireless NCSs with communication delay and packet dropouts, which are based on the deadband scheduling policy, are modeled as continuous time-delay systems and an controller is designed. Meanwhile, the event-triggering NCSs attract many researchers’ attention [18–20]. Compared with the widely used time-triggering sampling scheme, the event-triggering sampling scheme, whose main property is that the signal is sampled only when some functions of the system state or output measurement exceeds a threshold, can also save network bandwidth for the NCSs.
It is worth pointing that all of the aforementioned results are under the assumption that NCSs work in a single rate mode (either the actuators are event driven or the updating period of actuators is the same as the sampling period). The multirate NCSs, in which the updating rate of actuators is several times the sampling rate, have many advantages: (1) compared with the NCSs with event-driven actuators, the actuator updating moments are fixed in the multirate NCSs; thus, the analysis and synthesis of the multirate NCSs become more easier; (2) compared with the NCSs in which the actuator updating rate is the same as the sampling rate, the control input can be implemented to the plant more sooner in the multirate NCSs; therefore, better system performance can be ensured [21–23]. In , the multirate NCSs are models as the discrete switch systems, and an controller is designed by using the average dwell time method. The model-predictive control problem for multirate NCSs is proposed in . To the best of the authors’ knowledge, control problem of multirate NCSs with deadband scheduling has not been fully investigated to date, which motivates the present study.
In this paper, we focus on the problem of control for multirate NCSs with deadband scheduling and transmission delay. By analyzing the influences of transmission deadband, a discrete-time switch system model with uncertain parameters is proposed to describe the multirate NCSs. The design method of a robust controller that stabilizes this class of networked control systems is proposed. A simulation example is presented to show the advantage of the deadband scheduling policy and the efficiency of the proposed method.
This paper is organized as follows. In Section 2, the problem is stated and some useful definitions and lemmas are given, and then the main results of this paper are given in Section 3. Section 4 provides a simulation example to illustrate the effectiveness of our results. Finally, Section 5 gives some concluding remarks.
Notation. denotes the -dimensional Euclidean space, and is identity matrix. stands for the transpose of the corresponding matrix . The notation means that the matrix is a positive semidefinite (positive definite) matrix. For an arbitrary matrix and two symmetric matrices and , denotes a symmetric matrix, where denotes a block matrix entry implied by symmetry.
2. Problem Formulation and Preliminaries
Consider NCSs whose setup is depicted in Figure 1. The controlled plant, sensor, and controller are connected with a communication network. The transmission deadbands are set at the sensor node and the controller node.
The plant is described by where is the state, is the control input, is the output, and is the exogenous input. , and are known real constant matrices with appropriate dimensions.
The sensors are periodically sampled with the sampling interval . The sensor-to-controller transmission delay is described as , and the controller-to-actuator transmission delay is described as . We make the following assumptions about NCSs.
Assumption 1. .
Assumption 2. The controller is event driven; the sensors and actuators are time driven.
Assumption 3. The updating period of actuators is , , is positive integer, and .
First, the discrete-time model of multirate NCSs without transmission deadband is proposed.
The controller output arrives at the actuator at the instants ; if , then the actuator will update at the instances . Therefore, in the sample interval , system (1) is described as Let , system (1) with sampling period is discretized to Let Then Next, we introduce the deadband scheduling policy. The transmission deadbands located at the sensor node and controller node are based on such a deadband scheduling policy: if the difference between the present signal and the last transmitted signal exceeds the threshold of deadband, the present signal will be transmitted, otherwise it will not be transmitted for the purpose of reducing the network traffic and saving the network bandwidth.
Let , is the component of and sampled separately by each senor at the sampling instant. If enters into the deadband, according to the proposed scheduling policy, will not be transmitted and the controller will hold the previous transmitted one, which is equivalent to transmitting the previous transmitted one again. Therefore, the deadband 1 is described as where is the deadband threshold coefficient, .
According to (7), Define and ; therefore, one can obtain
Similarly, deadband 2 is described as where is the deadband threshold coefficient, .
Define and ; therefore, one can obtain
The following definition and lemmas will be essential for the proofs in the next section.
Definition 1. System (12) with a given matrix is said to be asymptotically stable with an norm bound if the following conditions hold:(1)system (12) is asymptotically stable with ; (2)under the zero initial condition, the controlled output satisfies , where .
Lemma 2 (see  Schur complement). For matrices , the matrix inequality holds, if and only if the following matrix inequalities hold.
Lemma 3 (see ). For some given matrices , , and of appropriate dimensions and with being symmetric, then holds, where satisfying , if and only if there exists a scalar such that
3. Main Results
In this section, we will find a state feedback control matrix such that the system is asymptotically stable with an norm bound .
Theorem 4. For given state feedback control matrix , system (12) is asymptotically stable with an norm bound , if there exist symmetric positive definite matrices and , such that the following condition holds:
Proof. Consider the following Lyapunov candidate functional:
First, we proof that system (12) is asymptotically stable with under (16).
Let along the trajectory of the system (12), we have using Lemma 2 (Schur complement), we have if and only if
By Lemma 2 (Schur complement), we know that (16) implies (20); Therefore, system (12) is asymptotically stable with being proved.
Now, we will consider the performance of system (12).
When , define under zero condition and , we have
Let , we obtain where
By Lemma 2 (Schur complement), (16) is equivalent to , which implies that ; thus, we have . This completes the proof.
Next, the design method of the state feedback controller is proposed.
Theorem 5. For given diagonal matrices , and a scalar , if there exist positive definite matrices , , and , scalars and a matrix , such that the following linear matrix inequalities hold, then the system is asymptotically stable with an norm bound ; the desired controllers are given as follows: where
Proof. Rewrite (16) as
, and are defined above in Theorem 5.
By Lemma 3, is equivalent to where By the definitions of , and , we have using Lemma 2 (Schur complement), (25) is equivalent to . Define , (26) implies that
Combining (33), (34), and (35), we have Therefore, Guarantees (16).
By Lemma 2 (Schur complement), (37) is equivalent to where
Let , we pre- and postmultiply both sides of (38) with we have (27). This completes the proof.
In this section, we will give an example to show the usefulness of the derived results and the effectiveness of the proposed methods.
The controlled plant parameters are given as follows:
Given the sampling period , and the actuator updating period , following (6), we have
The deadband threshold coefficients of deadbands 1 and 2 are given as , , and the disturbance attenuation . According to Theorem 5, using the LMI toolbox of MATLAB, the controller parameters can be calculated as follows:
The exogenous input is chosen as , the states responses of the closed-loop multirate NCSs are shown in Figure 2, which confirms that the closed-loop multirate NCSs are robustly asymptotically stable under the exogenous input.
The packet dropout rates of the S-to-C channel and the C-to-A channel are shown in Figure 3. The packet dropout mentioned here is the data packets which are lost “actively” when the multirate NCSs enter into the transmission deadband; thus, Figure 3 confirms that the proposed deadband scheduling policy could reduce the network traffic remarkably.
In this paper, control problem for multirate networked control systems with deadband scheduling policy and transmission delay is considered. The transmission deadbands are set at the sensor node and controller node to reduce the network traffic. The systems are modeled as the discrete switch system with uncertain parameters and the design method of controller is established by using the Lyapunov theorem. Finally, the simulation example is presented to show the advantage of the deadband scheduling policy and the efficiency of the proposed theoretical results.
This work was supported by the National Natural Science Foundation of China under Grants nos. 60975075 and 61074023 and the innovation project of graduate students’ scientific research of Jiangsu Province no. CXLX11_0256.
- R. A. Gupta and M. Y. Chow, “Networked control system: overview and research trends,” IEEE Transactions on Industrial Electronics, vol. 57, no. 7, pp. 2527–2535, 2010.
- Y. Tipsuwan and M. Y. Chow, “Gain adaptation of networked mobile robot to compensate QoS deterioration,” in Proceedings of the 28th IEEE Annual Conference of Industrial Electronics Society (IECON '02), vol. 4, pp. 3146–3151, November 2002.
- Y. Tang, H. J. Gao, J. Kurths, and J. A. Fang, “Evolutionary pinning control and its application in UAV coordination,” IEEE Transactions on Industrial Informatics, vol. 8, no. 4, pp. 828–838, 2012.
- S. Shirmohammadi and N. H. Woo, “Evaluating decorators for haptic collaboration over internet,” in Proceedings of the 3rd IEEE International Workshop on Haptic, Audio and Visual Environments and their Applications (HAVE '04), pp. 105–109, October 2004.
- P. Leitão, “Agent-based distributed manufacturing control: a state-of-the-art survey,” Engineering Applications of Artificial Intelligence, vol. 22, no. 7, pp. 979–991, 2009.
- E. G. Tian and D. Yue, “Reliable filter design for T-S fuzzy model-based networked control systems with random sensor failure,” International Journal of Robust and Nonlinear Control, vol. 23, no. 1, pp. 15–32, 2013.
- X. L. Luan, P. Shi, and F. Liu, “Stabilization of networked control systems with random delays,” IEEE Transactions on Industrial Electronics, vol. 58, no. 9, pp. 4323–4330, 2011.
- Y. Y. Liu and G. H. Yang, “Sampled-data control for networked control systems with digital control inputs,” International Journal of Systems Science, vol. 43, no. 9, pp. 1728–1740, 2012.
- Y. Shi and B. Yu, “Output feedback stabilization of networked control systems with random delays modeled by Markov chains,” Institute of Electrical and Electronics Engineers, vol. 54, no. 7, pp. 1668–1674, 2009.
- C. Ma and H. Fang, “Research on stochastic control of networked control systems,” Communications in Nonlinear Science and Numerical Simulation, vol. 14, no. 2, pp. 500–507, 2009.
- S. L. Dai, H. Lin, and S. S. Ge, “Scheduling-and-control codesign for a collection of networked control systems with uncertain delays,” IEEE Transactions on Control Systems Technology, vol. 18, no. 1, pp. 66–78, 2010.
- Y. B. Zhao, G. P. Liu, and D. Rees, “Integrated predictive control and scheduling co-design for networked control systems,” IET Control Theory & Applications, vol. 2, no. 1, pp. 7–15, 2008.
- L. Zhang and D. Hritsu-Varsakelis, “Stabilization of networked control systems under feedback-based communication,” in Proceedings of American Control Conference, vol. 4, pp. 2933–2938, 2005.
- G. Guo, “A switching system approach to sensor and actuator assignment for stabilisation via limited multi-packet transmitting channels,” International Journal of Control, vol. 84, no. 1, pp. 78–93, 2011.
- G. Guo and H. Jin, “A switching system approach to actuator assignment with limited channels,” International Journal of Robust and Nonlinear Control, vol. 20, no. 12, pp. 1407–1426, 2010.
- M. L. Du, C. Zhou, Q. W. Chen, and J. Ren, “Coordinate design of dynamic scheduling and H-infinity control for networked control systems with communication constraints,” Control Theory & Applications, vol. 29, no. 9, pp. 1132–1138, 2012.
- Z. N. GAO, R. H. XIE, W. H. Fan, and Q. W. Chen, “ control of wireless networked control systems with signal difference-based deadband scheduling,” Control and Decision, vol. 27, no. 9, pp. 1301–1307, 2012.
- D. Yue, E. G. Tian, and Q. L. Han, “A delay system method to design of event-triggered control of networked control systems,” in Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC '11), pp. 1668–1673, 2011.
- S. L. Hu and D. Yue, “Event-based filtering for networked system with communication delay,” Signal Processing, vol. 92, no. 9, pp. 2029–2039, 2012.
- X. Wang and M. D. Lemmon, “Event-triggering in distributed networked control systems,” Institute of Electrical and Electronics Engineers, vol. 56, no. 3, pp. 586–601, 2011.
- W. A. Zhang, L. Yu, and S. Yin, “A switched system approach to control of networked control systems with time-varying delays,” Journal of the Franklin Institute, vol. 348, no. 2, pp. 165–178, 2011.
- Y. Zou, T. Chen, and S. Li, “Network-based predictive control of multirate systems,” IET Control Theory & Applications, vol. 4, no. 7, pp. 1145–1156, 2010.
- L. L. Yang and S. H. Yang, “Multirate control in Internet-based control systems,” IEEE Transactions on Systems, Man, and Cybernetics C, vol. 37, no. 2, pp. 185–192, 2007.
- F. Z. Zhang, The Schur Complement and its Applications, vol. 4 of Numerical Methods and Algorithms, Springer, New York, NY, USA, 2005, Edited by Fuzhen Zhang.
- L. Xie, “Output feedback control of systems with parameter uncertainty,” International Journal of Control, vol. 63, no. 4, pp. 741–750, 1996.