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Mathematical Problems in Engineering

Volume 2013, Article ID 382934, 11 pages

http://dx.doi.org/10.1155/2013/382934
Research Article

Performance of Networked Control Systems

1State Key Laboratory of Synthetical Automation of Process Industry, Northeastern University, Shenyang, Liaoning 110004, China

2School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China

Received 6 February 2013; Accepted 16 April 2013

Academic Editor: Yang Yi

Copyright © 2013 Yingwei Zhang et al. 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.

Abstract

Data packet dropout is a special kind of time delay problem. In this paper, predictive controllers for networked control systems (NCSs) with dual-network are designed by model predictive control method. The contributions are as follows. (1) The predictive control problem of the dual-network is considered. (2) The predictive performance of the dual-network is evaluated. (3) Compared to the popular networked control systems, the optimal controller of the new NCSs with data packets dropout is designed, which can minimize infinite performance index at each sampling time and guarantee the closed-loop system stability. Finally, the simulation results show the feasibility and effectiveness of the controllers designed.

1. Introduction

With the rapid development of computer networks technology, the control system based on NCSs has become one of the hot research tasks in the current international control field. As stability analysis of NCSs subjected to packet dropping has received much attention in [13], various approaches for the delay issue in NCSs have been presented in [48], and so on. Compared to the traditional control systems, the main advantages of NCSs are lower cost, simpler installation, and higher reliability [911]. Because of these attractive advantages, typical application of these systems ranges from a wide field, such as automotive [12], mobile [13], and advanced aircraft [14]. However, the introduction of communication networks in the control loops makes the analysis and design of NCSs complex. For example, network-induced delays and data packet dropout problem may be inevitable during transmitting communication.

Data packet dropout is a special kind of time delay problems. Data packet dropout which is a kind of uncertainty that may happen due to node failures or network congestion is a common problem in networked systems. This loss will deteriorate the performance and may even cause the system to be unstable. Recently, the effect of data packet dropout on the stability and performance of NCSs has received great attention. In [15], the stability of a linear networked control system in the presence of dropped packets was studied. A stability analysis of model-based NCSs can be found in [2, 1619], where an additional model was used for estimating the plant state between transmission times and generating a control signal. In [20], though turning the model of NCSs into an asynchronous dynamic system, hybrid system technology has been used to handle the system with time delay and data dropouts. A stability condition was obtained for the Try-Once-Discard networked protocol in [3]. In [21], Hadjicostis and Touri analyzed the performance when lost data were replaced by zeros. In [22, 23], Ling and Lemmon posed the problem of optimal compensator design for the case when data loss was independent and identically distributed. Reference [24] addresses the random time delays and packet losses issues of NCSs within the framework of jump linear systems with mode-dependent time delays. Jump linear systems with Markov chains [25, 26] also were used to analyze the effect of dropouts on system stability and performance. In [27], a delay-dependent stability condition was presented for discrete-time jump time delay system where the time delay was dependent on the system mode. In [28], the problem of stability analysis and controller design has been proposed based on a new model with packet dropouts.

Model predictive control (MPC) can now be found in a wide variety of application areas including chemicals, food processing, and so on. MPC is also an effective method to incorporate the input and output constraints into online optimization, which increases the possibility of its application in the synthesis and analysis of NCSs [29]. In [30], the MPC strategy for multivariable plants was presented. Wu et al. [31] introduced MPC into NCSs with time delay and designed an optimal control rule. In [4], the networked predictive control with modified MPC was proposed.

In this paper, the contributions are as follows. (1) The predictive control problem of the dual-network is considered. (2) The predictive performance of the dual-network is evaluated. (3) Compared to the popular networked control systems, the optimal controller of the new NCSs with data packets dropout is designed, which can minimize infinite performance index at each sampling time and guarantee the closed-loop system stability.

The remainder of this paper is organized as follows. The problem descriptions and model of NCSs with packet dropouts are given in Section 2. The optimization method of the new closed-loop systems is proposed in Section 3. The stability analysis is given in Section 4. A numerical example and an industrial example show the effectiveness of the proposed method in Section 5, and some conclusions are given in Section 6.

2. Problem Descriptions and Modeling of NCSs with Packet Dropouts

Consider that NCSs model which is shown in Figure 1, sensors, controllers, and actuators are connected by networks, and we suppose that the communication link between primary sensor and controller ( ) is ideal. Based on such a structure, the problem of packet dropouts in network transmission mainly exists in the actuator and secondary sensor and controller ( and ).

382934.fig.001
Figure 1: Networked control system model.

In Figure 1, , , and denote the sensor, controller, and plant of primary and secondary, respectively, and is actuator. and are outputs of primary and secondary controllers at sampling time . is input of actuator at sampling time , is the quantity of packet dropouts between sampling current time and the latest communicate time successfully ( ) on side, and is the quantity of packet dropouts at current time and the latest communicate successfully ( ) on side. The quantity of packet dropouts on and sides is assumed to satisfy where , , , and are constant positive scalars representing the minimum and maximum quantities of packet dropouts on and sides, respectively, where and are two independent Markov chains. Without loss of generality, define

Suppose that under the condition , the probability that the state of packet dropouts at time is ( ) is where . That is also to say that the transition probability of jumping from mode to is (3), and the transition probability of jumping from mode to is where , , , , and , .

For analysis convenience and without loss of generality, we suppose that Markov chains jump no more than one step. Thus, Markov chain transferring probability matrix is satisfied [28]. Consider

And the two equations are used to produce the definition of data packet dropout. denotes the quantity of the packet dropouts at time . denotes the quantity of packet dropouts at time . The system’s conditional probability of data packets dropout is equal to zero when the difference of the quantity of packet dropouts between time and is not equal to 1 or there is no dropout at time .

Some assumptions of NCSs are introduced as follows. (1)The sensors, controllers, and actuators are clock-driven.(2)The buffers are big enough to hold all the data arrived, and rule of the buffer is last-in-first-out.(3)Transmission link of primary control loop is ideal and data packet dropouts only happen in secondary control loop.(4)The transition of NCSs is single-packet transmission and no timing sequence disordered.

The linear time-invariant discrete-time models of primary and secondary plants in Figure 1 are described as follows: where and are the states of primary and secondary plants, respectively, at sampling time , is the input of secondary plant at sampling time , and , , , , , are known constant matrixes with appropriate dimensions.

Due to the existence of data packet dropouts, transmission link in network cannot normally communicate. The controller and actuator can use the buffer rule, named first-in-last-out, to pick up signal [28]. Take the controller, for example; when a secondary sensor data is false to transmit, the secondary controller gets the latest data from buffer and uses it as to calculate new control input. Otherwise, the new sensor data will be saved to buffer and used by the secondary controller as . Thus,

Similarly

It can easily derive

From the model of NCSs, as shown in Figure 1, is the output of primary controller at sampling time as where is to be designed by MPC method.

is the output of secondary controller as where is to be designed by MPC method.

Substituting (9) and (10) into (11), we have

Therefore, (8) can be rewritten as

In order to simplify the expression of the closed-loop control systems, a function is introduced, which is dependent on whether packet dropped or not, instead of the quantity of packet dropouts. Combining (6) and (13), it can obtain that

Combining (10), (14), (15), and (16) and augmenting the state vectors, the new resulting closed-loop control systems and the augmenting vector (18) formed by predictive controller (10) and (14) designed by MPC method are as follows: where ,

Remark 1. The new closed-loop control systems (17) and the augmenting vector (18) formed by predictive controller (10) and (14) are linear jumping systems, where their communications are described by Markov chain which is the description of the quantity of packet dropouts at sampling time on and sides. The value of depends on whether the designed control signal is successfully transmitted or not. Therefore we can use the results of linear jumping system with delay to analyze this class of NCSs with packets dropped.

3. Optimum Analysis Based on MPC

Assume that predictive horizon , control horizon , and the steps control sequences , , are computed by minimizing the following performance function:

The norm terms in the performance function are defined as

The exact measurement state of NCSs at each sampling time is

By using the control input given in (18), the first control move is

According to (17) and (22), the predicted state at time is obtained as (24) which is predicted based on the exact measurement state . Consider where is model-dependent time invariable delay on side, which is -step ahead prediction based on the measurement time . is the predicted quantity of packet dropouts at time .

And the key of predictive controller rule is to compute the matrixes and in (14). The predicted controller at time based on the first control move is where and are matrixes which contain the control gain matrixes and , respectively.

Next we will introduce a method to solve the optimal control sequence which can minimize the following performance index at each sampling time:

and are symmetric positive definite weight matrixes.

In order to simplify, let , , .

First, consider a Lyapunov function candidate with as follows: where

Matrixes and are positive definite with appropriate dimensions.

Second, suppose that at sampling time , satisfies (29) for any and . Consider

For the infinite performance index to be finite, we must set ; hence, . Summing (29) from to , it can be obtained that

Therefore, the infinite optimization problem at time has transformed into optimizing upper function at each sampling time . As a standard in MPC, only the first control move is implemented, and at the next sampling time , the state is measured and the optimization is repeated to compute matrixes and .

Third, a sufficient condition for existing of a set of control sequence is where is a suitable nonnegative coefficient to be minimized.

Before proceeding, the following lemma needs to be introduced.

Lemma 2 (Schur Complement Lemma). Given constant matrixes , , , where , , then holds if or .

Assumption 3. The values of and are known to the controller.

Theorem 4. Suppose the states of system (17) are measured; then, the control rule in (18) makeing (29) and hold, if existing matrixes , , , , , , and scalar make the following optimization feasible:

subject to

or where , , denotes the quantity of packet dropouts, , , , , , , , , , , , .

In order to express conveniently, let , , , . Thus, (24) becomes .

The proof of the previous theorem is divided into the following two steps: Step  1, we design a Lyapunov function for the systems; Step  2, an optimal controller is designed such that the closed-loop systems are stable and the optimal characteristics are satisfied.

Proof Step  1. Consider a Lyapunov function candidate with , where where , and is the mathematical expectation. Consider

and , ; thus,

Similarly,

Therefore, and where denotes the block determined by symmetry.

Substituting (25) and into (29), we have where .

According to Lemma 2, we obtain

According to (34), then

Hence, (29) holds.

And

That is,

From (30), we have

Step 2. A congruence transformation to (17) and (18) leads to where

Substituting into (34), (35) can be obtained.

The proof of Theorem 4 is completed.

Remark 5. According to the proposed method, the optimal input is unique. is a scalar and . Unique resolution is given finally by using Toolbox of Matlab. Its convergence can be found in Figure 3.

4. Stability Analysis of NCSs with Packet Dropouts

Theorem 6. Suppose that the optimization problem in Theorem 4 is feasible at time ; then, the new resulting closed-loop systems in (17) are asymptotically stable by the optimal control law in (18) which is obtained from Theorem 4.

Proof. First, the Lyapunov function, is discussed and holds only when .

Next, in the following we will consider the situation at time (i.e., ): where , are obtained from the optimal solution at time . Suppose that the optimization problem in Theorem 4 is feasible at time , and according to the introduction in [8], these optimization problems are also feasible for all . Let us note values of , , , , , and , , , obtained from the optimal solution at time and , respectively. Therefore, we have

That is because , are optimal, whereas , are only feasible at time .

From (29), it can be obtained that

Because is symmetric positive definite, we have (the minimal eigenvalue of matrix ), and then

When , (24) is becoming

Because of the measured state,

So we have

Comparing with , it can be obtained that

Therefore, the Lyapunov function is decreasing for the new closed loop, and is concluded. The stochastic stability is obtained.

5. Simulation Examples

In order to show the effectiveness of the proposed method, we will give some cases and simulations.

5.1. A Simple Example

Considering the coefficient matrixes of and

Markov models of the whole closed control systems are consisted. Suppose that and , as shown in Figures 2 and 5, which means that the number of data packet dropouts is at sensor to controller of secondary side and, similarly, is at secondary controller to actuator side at time . In the method, is a scalar and . Its convergence is shown in Figure 3. Unique resolution is given finally by using Toolbox of Matlab. The simulation results under the optimal state feedback controller are shown in Figure 4.

382934.fig.002
Figure 2: The number of data packet dropouts of to and to .
382934.fig.003
Figure 3: The upper of performance index.
fig4
Figure 4: (a) State response of state with designed optimal controller, (b) state response of state with designed optimal controller, (c) state trajectories of state , (d) state trajectories of state , (e) state response of state , and (f) state trajectories of state .
382934.fig.005
Figure 5: The number of data packet dropouts of to side.

From the simulation (1) Figure 3 is the upper of performance index. (2) In Figures 4(a) and 4(b), system state trajectories will eventually be more stable. It is shown that NCSs with data packet dropouts are stable with the optimal controller which is designed by Theorem 4. (3) Through comparing to LQR controller, which is showed in Figures 4(c), 4(d), 4(e), and 4(f), the system overshoots are shortened, and state trajectory paths are more superior. Thus, the optimal controller designed in our paper is effective.

5.2. Industrial Systems

In order to verify the method proposed earlier, a networked control system for main furnace temperature in industrial systems is taken for an example. It is assumed that the transfer functions of the inertial and leading sections are and , respectively. After discretization, the following state space models are available:

Figures 6(a) and 6(b) can be obtained, from which the systems in (17) are stabilized by our designed controller. Through comparing to LQR controller, as shown in Figures 6(c), 6(d), 6(e), and 6(f), and the system overshoots shortened, state trajectories are more superior and performance of the control systems is further improved. Thus, the proposed optimal controller designed is effective and feasible.

fig6
Figure 6: (a) State response of state with designed optimal controller, (b) state response of state with designed optimal controller, (c) state trajectories of state , (d) state trajectories of state , (e) state response of state , and (f) state trajectories of state .

6. Conclusions

In this paper, the modeling, optimal, and control problems for a class of NCSs under data packet dropout effect have been studied. The data packet dropouts are described by Markov chains. The Markov chains describe that the quantity of packet dropouts between current time and its latest communicates successfully instead of whether the data packets dropped or not. Though augmenting the state vectors, the resulting closed-loop control system is transformed into a jump system with time delays. Model predictive control method is applied to study optimization and stability problems of the resulting closed-loop NCSs. Sufficient conditions are proposed for the optimization and stability of the resulting closed-loop NCSs. Some simulations are given in the last section and it can be seen that the designed controllers are feasible and effective. In this work, the full state feedback controllers are designed. However, it is difficult to obtain the full state. Output feedback controllers should be considered. Dynamic output feedback can be investigated in the future work.

References

  1. W. Zhang, M. S. Branicky, and S. M. Phillips, “Stability of networked control systems,” IEEE Control Systems Magazine, vol. 21, no. 1, pp. 84–99, 2001. View at Publisher · View at Google Scholar · View at Scopus
  2. L. A. Montestruque and P. Antsaklis, “Stability of model-based networked control systems with time-varying transmission times,” IEEE Transactions on Automatic Control, vol. 49, no. 9, pp. 1562–1572, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  3. G. C. Walsh, H. Ye, and L. Bushnell, “Stability analysis of networked control systems,” in Proceedings of the American Control Conference, vol. 4, pp. 2876–2880, June 1999. View at Scopus
  4. G. P. Liu, J. X. Mu, D. Rees, and S. C. Chai, “Design and stability analysis of networked control systems with random communication time delay using the modified MPC,” International Journal of Control, vol. 79, no. 4, pp. 288–297, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  5. H. Lin and P. J. Antsaklis, “Stability and persistent disturbance attenuation properties for a class of networked control systems: switched system approach,” International Journal of Control, vol. 78, no. 18, pp. 1447–1458, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  6. S. S. Hu and Q. X. Zhu, “Stochastic optimal control and analysis of stability of networked control systems with long delay,” Automatica, vol. 39, no. 11, pp. 1877–1884, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  7. L. Q. Zhang, Y. Shi, T. W. Chen, and B. Huang, “A new method for stabilization of networked control systems with random delays,” IEEE Transactions on Automatic Control, vol. 50, no. 8, pp. 1177–1181, 2005. View at Publisher · View at Google Scholar · View at MathSciNet
  8. F. W. Yang, Z. D. Wang, Y. S. Hung, and M. Gani, “H control for networked systems with random communication delays,” IEEE Transactions on Automatic Control, vol. 51, no. 3, pp. 511–518, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. F. L. Lian, Analysis, design, modeling and control of networked control systems [Ph.D. dissertation], University of Michigan, Ann Arbor, Mich, USA, 2001.
  10. L. Zhang, Access scheduling and controller design in networked control systems [Ph.D. dissertation], The University of Maryland, College Park, 2005.
  11. T. C. Yang, “Networked control systems: a brief survey,” IEE Proceedings on Control Theory and Applications, vol. 153, no. 4, pp. 403–412, 2006. View at Publisher · View at Google Scholar
  12. G. Leen and D. Heffernan, “Expanding automotive electronic systems,” Computer, vol. 35, no. 1, pp. 88–93, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. F. Goktas, J. M. Smith, and R. Bajcsy, “Telerobotics over communication networks,” in Proceedings of the 36th IEEE Conference on Decision and Control, vol. 3, pp. 2399–2404, San Diego, Calif, USA, December 1997. View at Scopus
  14. P. Seiler and R. Sengupta, “Analysis of communication losses in vehicle control problems,” in Proceedings of the American Control Conference, vol. 2, pp. 1491–1496, Arlington, Va, USA, June 2001. View at Scopus
  15. P. Seiler and R. Sengupta, “Analysis of communication losses in vehicle control problems,” in Proceedings of the American Control Conference, pp. 1491–1496, June 2001. View at Scopus
  16. L. Wu and W. X. Zheng, “Weighted H model reduction for linear switched systems with time-varying delay,” Automatica, vol. 45, no. 1, pp. 186–193, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  17. L. Wu and W. X. Zheng, “Passivity-based sliding mode control of uncertain singular time-delay systems,” Automatica, vol. 45, no. 9, pp. 2120–2127, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  18. L. Wu, J. Lam, X. Yao, and J. Xiong, “Robust guaranteed cost control of discrete-time networked control systems,” Optimal Control Applications & Methods, vol. 32, no. 1, pp. 95–112, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  19. L. Wu, X. Yao, and W. X. Zheng, “Generalized H2 fault detection for two-dimensional Markovian jump systems,” Automatica, vol. 48, no. 8, pp. 1741–1750, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  20. W. Zhang, Stability Analysis of Network Control Systems [Ph.D. thesis], Electrical Engineering and Computer science, Case Western Reserve University, 2001.
  21. C. N. Hadjicostis and R. Touri, “Feedback control utilizing packet dropping network links,” in Proceedings of the 41st IEEE Conference on Decision and Control, pp. 1205–1210, December 2002. View at Scopus
  22. Q. Ling and M. D. Lemmon, “Power spectral analysis of networked control systems with data dropouts,” IEEE Transactions on Automatic Control, vol. 49, no. 6, pp. 955–960, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  23. Q. Ling and M. D. Lemmon, “Optimal dropout compensation in networked control systems,” in Proceedings of the 42nd IEEE Conference on Decision and Control, pp. 670–675, December 2003. View at Scopus
  24. S. Li and Y. Sun, “New approach to stochastic stability and controller design for networked control systems,” Journal of Control Theory and Applications, vol. 3, no. 3, pp. 241–246, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  25. J. Nilsson, Real-time control systems with delays [Ph.D. dissertation], Department of Automatic Control, Lund Institute of Technology, Lund, Sweden, 1998.
  26. L. Xiao, A. Hassibi, and J. P. How, “Control with random communication delays via a discrete-time jump system approach,” in Proceedings of the American Control Conference, pp. 2199–2204, June 2000. View at Scopus
  27. E. K. Boukas and Z. K. Liu, “Robust H control of discrete-time Markovian jump linear systems with mode-dependent time-delays,” IEEE Transactions on Automatic Control, vol. 46, no. 12, pp. 1918–1924, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  28. J. Wu and T. Chen, “Design of networked control systems with packet dropouts,” IEEE Transactions on Automatic Control, vol. 52, no. 7, pp. 1314–1319, 2007. View at Publisher · View at Google Scholar · View at MathSciNet
  29. S. J. Qin and T. A. Badgwell, “A survey of industrial model predictive control technology,” Control Engineering Practice, vol. 11, no. 7, pp. 733–764, 2003. View at Publisher · View at Google Scholar · View at Scopus
  30. O. C. Imer, S. Yüksel, and T. Başar, “Optimal control of LTI systems over unreliable communication links,” Automatica, vol. 42, no. 9, pp. 1429–1439, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  31. J. Wu, L. Zhang, and T. Chen, “Model predictive control for networked control systems,” International Journal of Robust and Nonlinear Control, vol. 19, no. 9, pp. 1016–1035, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet