New Developments in Sliding Mode Control and Its Applications 2014
View this Special IssueResearch Article  Open Access
Andrzej Bartoszewicz, Paweł Latosiński, "QuasiSliding Networked Control of Systems Subject to Unbounded Disturbance with Limited Rate of Change", Mathematical Problems in Engineering, vol. 2015, Article ID 379647, 10 pages, 2015. https://doi.org/10.1155/2015/379647
QuasiSliding Networked Control of Systems Subject to Unbounded Disturbance with Limited Rate of Change
Abstract
This paper concerns network based sliding mode control of linear plants with state measurement delay. The considered plants are subject to unbounded disturbance, but it is assumed that the change of disturbance value between each two subsequent sampling instants is limited. In order to combat the unpredictable disturbance in the environment with state measurement delay, a novel sliding mode controller has been introduced. It utilizes two nominal models of the plant to drive the system state along a desired trajectory and counteract the predicted effect of the past disturbance on the system. It has been proven that applying the new control strategy to the plant confines the system state to a defined band around the sliding hyperplane.
1. Introduction
Continuoustime variable structure control (VSC) has gained a considerable popularity in control engineering community since its introduction in the early 1960s [1, 2]. Its main advantages are computational efficiency and insensitivity with respect to matched disturbance [3]. These attractive properties resulted in a considerable amount of research dedicated to sliding mode control, which is the most widely used method of VSC implementation [4–8]. In mid1980s, the theory of variable structure control has been extended to discretetime systems [9, 10], which in turn inspired many significant works related to discretetime sliding mode control [11–33]. In particular, discretetime sliding mode control has great significance in digital environments [34, 35], where no continuous states are available.
Since the introduction of relatively lowcost control networks [36–41], the problem of effectively controlling discretetime systems with time delay [42–46] gained importance, as the time it takes for the sensor data and control signal to travel through the network is often nonnegligible. With this in mind, we will design an effective sliding mode control strategy for discretetime systems with time delay. As opposed to the control method proposed in our previous paper [47], the new approach will realize trajectory tracking in a networked environment as well as ensuring upper bounded convergence rate to the vicinity of the switching hyperplane. To that end, we will introduce two nominal models of the controlled plant. One of them will be compared to the original plant in order to extract delayed information about disturbance affecting the system. Then, the current effect of the disturbance on the system will be estimated and reduced to zero with a deadbeat controller. The other model will be utilized to drive the system from its initial state to a desired one in finite time and realize trajectory tracking in subsequent time instants.
Typically, in the design process of a sliding mode controller, an upper bound of the disturbance affecting the controlled plant is assumed to be known [14, 16, 21, 47]. However, in this paper we will abandon that assumption in favor of the one proposed in [15]. In other words, we assume that the change of the disturbance value between every two subsequent time instants is bounded by a certain constant , while the value of the disturbance itself does not have to be bounded. Under this assumption, we will design a sliding mode controller that will effectively combat the effect of the disturbance on the system in a networked environment.
The remainder of this paper is organized in the following way. Section 2 states the considered problem, after which the new control strategy is introduced in Section 3. The control law for the considered system is established and the properties of the controlled system are formally proven in the same section. In Section 4, two simulation examples that show the effectiveness of the proposed method are presented and concluding remarks are given in Section 5.
2. Problem Statement
Let us consider a single input single output, linear, time invariant plant: where is the state matrix such that , and are vectors of appropriate dimensions that represent input distribution and system state, respectively, and and are scalars that represent the control signal and disturbance, respectively. We assume that the plant operates in a networked environment, which means that it is subject to networkinduced delay . Therefore, the control signal in each time instant is calculated based on the system states up to the moment . We do not assume that the disturbance affecting the system is bounded, but that the change of the disturbance value between every two subsequent time instants is limited by . In other words, for any .
3. Proposed Control Strategy
We begin by introducing two models of the considered system (1). The first onewill be used to extract information about disturbance from the system. The matrix and vector in the model are the same as in the original system and . The model is controlled with the same signal as plant (1). Moreover, let us define another modelcontrolled with a different signal . Again, and are the same as in (1) and . The second model will be used solely to drive the system along the desired trajectory . We take into account the sliding hyperplane defined aswhere and is such a vector thatIt can be seen that vector would allow us to design a deadbeat controller for the delayfree system. Although the existing delay prevents us from applying such a controller to the system (1), vector is chosen in such a way that allows us to compensate for the effect of past disturbances on the system as well as partially reducing the ones that have not yet been estimated due to the delay. We first define the following sequence of dimensional vectors:that will be used to extract the information about past disturbances from the system. We assume that for any , which gives for . For any , Therefore, at any time instant , we haveWe will now design a control law that combats the effect of the disturbance obtained from (8) on the system and partially reduces the effect of subsequent disturbances that have not yet been explicitly obtained due to the delay . This is possible since the disturbance rate of change is limited. Let us define vectors in the following way:whereIt can be seen from (9) that each vector can be expressed asVectors possess an important property described by Lemma 1.
Lemma 1. For any , the vector . Furthermore, for , the product .
Proof. The proof of this property is given in our previous work [47].
The control laws (10) will be used to reduce each individual disturbance value affecting the system (1) to zero in finite time. This property will be demonstrated in Theorem 2 later in this paper. We will now define the control law that will drive the system from its initial position to a desired trajectory . Let and let us consider the reaching law [15]:where is a natural number chosen by the designer. It can be seen that the reaching law designed in such a way guarantees that the reference sliding variable will be reduced to 0 at the moment and will retain that value in every subsequent step. The control law for model (3) obtained from reaching law (12) can be expressed asWe propose the following control law for system (1):where is defined by (13), are defined by (10), and is defined by (8). Control law designed in such a way will drive the system from its initial position to a desired state in a finite amount of steps, combat the effect of disturbances up to the moment on the sliding variable, and partially reduce the effects of disturbances that occurred after . Additionally, the element will partially counteract the disturbance that occurs at the moment of applying the control law. As a result, the system state will be confined to a certain band around the sliding hyperplane. The width of the band is specified by Theorem 2 formulated below.
Theorem 2. If the control signal for system (1) is defined by (14) and the system is subject to disturbance , which satisfies for any , then there exists a natural number such that for every the system state is confined to a quasisliding mode band defined as
Proof. Let . First, we express asWe rewrite each disturbance as , where . We obtainWe now rearrange the elements in (17) in the following way. For every , elements containing multiplied by a different power of are grouped together. Then, in each group, the highest power of is factored out. In this way we getLet us introduce the following notation:We substitute from (9) and from (19) into (18) and obtainThen, we substitute from (14) into (20) and getRelation (8) gives for any . Therefore, from (9), we know that for all and consequently for any . From (10) we know that is a function of , which gives for . Taking that into consideration, we rewrite (21) asIt can be seen from (3) thatMoreover, from (8), we know thatTaking into consideration (23) and (24), we rewrite (22) asWe rearrange the elements of (25) as follows:Using (11), we simplify (26) in the following way:From Lemma 1, we know that for each vector , which gives usThe product can therefore be expressed as Lemma 1 states that, for each and , the product , which results inFrom (12), we getfor , which gives usand consequently Therefore, the bound of the absolute value of can be expressed asWe conclude that (15) holds for any .
We have shown that, upon applying control strategy (14) to the plant, the system state is driven into a certain band around the sliding hyperplane in finite time and contained within it in all subsequent time instants. As specified by Theorem 2, the width of the band can be expressed by (15).
4. Simulation Example
We will now show the effectiveness of the proposed method by means of a simulation example. The control law proposed in this paper will be applied to the following linear plant:Our objective is to drive the system output along a desired trajectory described as . It can be seen from (35) that the desired state vector can be expressed asThe system is subject to state measurement delay . Vector for the control law (14) is chosen according to (5) and . We select the parameter to ensure that the sliding variable rate of descent is not excessively big.
4.1. System Subject to Sinusoidal Disturbance
In the first example, we assume that system (35) is subject to matched disturbanceThen, the maximum rate of change of the disturbance between any two subsequent time instants equals . Figure 1 illustrates the evolution of the sliding variable upon applying the control law (14). According to (15), the bounds of the quasisliding mode band equal ±4.375, and it can actually be seen from the figure that the bounds are not exceeded. Figure 2 shows system output . The black dashed line in this figure illustrates the desired value of the output . Figure 3 illustrates the control signal.
It can be seen from Figure 1 that the sliding variable arrives inside the quasisliding mode band specified in Theorem 2 in a finite amount of steps not greater than and is contained within it for all subsequent time instants. Moreover, as seen from Figure 2, the proposed control strategy effectively realizes tracking of the system output along the desired trajectory despite the detrimental effects of disturbance and measurement delay.
In order to assess the control quality obtained in this simulation example, we demonstrate the deviation of sliding variable from 0 and system output from using two control quality criteria: integral absolute error (IAE) and integral squared error (ISE). The criteria are shown in Table 1.

4.2. System Subject to Unbounded Disturbance
The first simulation example has shown that stability of the system as well as tracking of the system desired output is successfully obtained in the presence of slowly changing disturbance. However, disturbance (37) is not only characterized by a limited rate of change, but its magnitude is also limited. Therefore, in the second simulation example, the considered system will be subject to unbounded disturbance with limited rate of change and it will be demonstrated that the advantageous properties shown in the previous example are preserved. The disturbance affecting the plant in the second example can be expressed asIt can be seen that the magnitude of disturbance (38) is unbounded and its rate of change is limited by . Therefore, the bounds of the quasisliding mode band again equal ±4.375. Figure 4 shows the evolution of the sliding variable, Figure 5 illustrates the system output (once again, the desired trajectory is illustrated by a black dashed line), and Figure 6 shows the control signal.
It can be seen from Figure 4 that the sliding variable arrives inside the quasisliding mode band in finite time and remains within it in all subsequent time instants. Figure 5 shows that tracking of the demand system output is realized in the presence of unbounded disturbance. Like in the previous example, we demonstrate the deviation of the sliding variable from 0 and system output from using IAE and ISE. The criteria are given in Table 2.

5. Conclusions
In this paper, the problem of sliding mode control of a discretetime system with state measurement delay has been explored. In order to ensure that the unpredictable disturbance is effectively counteracted in an environment with time delay, we have designed a novel sliding mode controller for such systems. The proposed control method utilizes two models of the considered plant. The first one is responsible for retrieving information about disturbance affecting the plant and reducing its influence on the system state to zero in finite time. The second model drives the system state along a desired trajectory . The result is obtained under the assumption that the disturbance affecting the plant has an upper bounded rate of change, but the boundedness of the disturbance itself is not required. It has been proven that the new controller confines the system state to a given band around the sliding hyperplane and the effectiveness of the proposed method has been verified with simulation examples.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
This work has been performed in the framework of the project “Optimal Sliding Mode Control of Time Delay Systems” financed by the National Science Centre of Poland—decision no. DEC 2011/01/B/ST7/02582. Kind support provided by the Foundation for Polish Science under “Mistrz” Grant is also acknowledged.
References
 S. V. Emelyanov, Variable Structure Control Systems, Nauka, Moscow, Russia, 1967, (Russian).
 V. I. Utkin, “Variable structure systems with sliding modes,” IEEE Transactions on Automatic Control, vol. 22, no. 2, pp. 212–222, 1977. View at: Google Scholar  MathSciNet
 B. Draženović, “The invariance conditions in variable structure systems,” Automatica, vol. 5, pp. 287–295, 1969. View at: Google Scholar  MathSciNet
 A. Bartoszewicz and A. NowackaLeverton, TimeVarying Sliding Modes for Second and Third Order Systems, vol. 382 of Lecture Notes in Control and Information Sciences, Springer, Berlin Heidelberg, Germany, 2009. View at: MathSciNet
 R. A. DeCarlo, S. H. Zak, and G. P. Matthews, “Variable structure control of nonlinear multivariable systems: a tutorial,” Proceedings of the IEEE, vol. 76, no. 3, pp. 212–232, 1988. View at: Publisher Site  Google Scholar
 C. Edwards and S. Spurgeon, Sliding Mode Control: Theory and Applications, Taylor & Francis, London, UK, 1998.
 W. Gao and J. C. Hung, “Variable structure control of nonlinear systems: a new approach,” IEEE Transactions on Industrial Electronics, vol. 40, no. 1, pp. 45–55, 1993. View at: Publisher Site  Google Scholar
 V. Utkin, J. Guldner, and J. Shi, Sliding Mode Control in ElectroMechanical Systems, Taylor & Francis, 2nd edition, 2009.
 C. Milosavljevic, “General conditions for the existence of a quasisliding mode on the switching hyperplane in discrete variable structure systems,” Automation and Remote Control, vol. 46, no. 3, pp. 307–314, 1985. View at: Google Scholar
 V. Utkin and S. V. Drakunov, “On discretetime sliding mode control,” in Proceedings of the IFAC Conference on Nonlinear Control, pp. 484–489, 1989. View at: Google Scholar
 B. Bandyopadhyay and D. Fulwani, “Highperformance tracking controller for discrete plant using nonlinear sliding surface,” IEEE Transactions on Industrial Electronics, vol. 56, no. 9, pp. 3628–3637, 2009. View at: Publisher Site  Google Scholar
 B. Bandyopadhyay and S. Janardhanan, DiscreteTime Sliding Mode Control. A Multirate Output Feedback Approach, Springer, Berlin, Germany, 2006. View at: MathSciNet
 G. Bartolini, A. Ferrara, and V. I. Utkin, “Adaptive sliding mode control in discretetime systems,” Automatica, vol. 31, no. 5, pp. 769–773, 1995. View at: Publisher Site  Google Scholar  MathSciNet
 A. Bartoszewicz, “Remarks on “discretetime variable structure control systems”,” IEEE Transactions on Industrial Electronics, vol. 43, no. 1, pp. 235–238, 1996. View at: Google Scholar
 A. Bartoszewicz, “Discretetime quasislidingmode control strategies,” IEEE Transactions on Industrial Electronics, vol. 45, no. 4, pp. 633–637, 1998. View at: Publisher Site  Google Scholar
 A. Bartoszewicz and J. Żuk, “Discrete time sliding mode flow controller for multisource singlebottleneck connectionoriented communication networks,” Journal of Vibration and Control, vol. 15, no. 11, pp. 1745–1760, 2009. View at: Publisher Site  Google Scholar  MathSciNet
 S. Chakrabarty and B. Bandyopadhyay, “Quasi sliding mode control with quantization in state measurement,” in Proceedings of the 37th Annual Conference of the IEEE Industrial Electronics Society, pp. 3971–3976, November 2011. View at: Publisher Site  Google Scholar
 M. L. Corradini and G. Orlando, “Variable structure control of discretized continuoustime systems,” IEEE Transactions on Automatic Control, vol. 43, no. 9, pp. 1329–1334, 1998. View at: Publisher Site  Google Scholar  MathSciNet
 M. L. Corradini, V. Fossi, A. Giantomassi, G. Ippoliti, S. Longhi, and G. Orlando, “Discrete time sliding mode control of robotic manipulators: development and experimental validation,” Control Engineering Practice, vol. 20, no. 8, pp. 816–822, 2012. View at: Publisher Site  Google Scholar
 K. Furuta, “Sliding mode control of a discrete system,” Systems and Control Letters, vol. 14, no. 2, pp. 145–152, 1990. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 W. Gao, Y. Wang, and A. Homaifa, “Discretetime variable structure control systems,” IEEE Transactions on Industrial Electronics, vol. 42, no. 2, pp. 117–122, 1995. View at: Publisher Site  Google Scholar
 G. Golo and C. Milosavljević, “Robust discretetime chattering free sliding mode control,” Systems and Control Letters, vol. 41, no. 1, pp. 19–28, 2000. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 S. Janardhanan and B. Bandyopadhyay, “Output feedback slidingmode control for uncertain systems using fast output sampling technique,” IEEE Transactions on Industrial Electronics, vol. 53, no. 5, pp. 1677–1682, 2006. View at: Publisher Site  Google Scholar
 S. Janardhanan and B. Bandyopadhyay, “Multirate output feedback based robust quasisliding mode control of discretetime systems,” IEEE Transactions on Automatic Control, vol. 52, no. 3, pp. 499–503, 2007. View at: Publisher Site  Google Scholar  MathSciNet
 S. Janardhanan and V. Kariwala, “Multirateoutputfeedbackbased LQoptimal discretetime sliding mode control,” IEEE Transactions on Automatic Control, vol. 53, no. 1, pp. 367–373, 2008. View at: Publisher Site  Google Scholar  MathSciNet
 S. Kurode, B. Bandyopadhyaya, and P. S. Gandhi, “Discrete sliding mode control for a class of underactuated systems,” in Proceedings of the 37th Annual Conference of the IEEE Industrial Electronics Society, pp. 3936–3941, November 2011. View at: Publisher Site  Google Scholar
 A. Mehta and B. Bandyopadhyay, “Frequencyshaped sliding mode control using output sampled measurements,” IEEE Transactions on Industrial Electronics, vol. 56, no. 1, pp. 28–35, 2009. View at: Google Scholar
 A. J. Mehta and B. Bandyopadhyay, “The design and implementation of output feedback based frequency shaped sliding mode controller for the smart structure,” in Proceedings of the IEEE International Symposium on Industrial Electronics (ISIE '10), pp. 353–358, Bari, Italy, July 2010. View at: Publisher Site  Google Scholar
 S. J. Mija and S. Thomas, “Reaching law based sliding mode control for discrete MIMO systems,” in Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV '10), pp. 1291–1296, December 2010. View at: Publisher Site  Google Scholar
 Č. Milosavljević, B. PeruničićDraženović, B. Veselić, and D. Mitić, “Sampled data quasisliding mode control strategies,” in Proceedings of the IEEE International Conference on Industrial Technology, pp. 2640–2645, December 2006. View at: Publisher Site  Google Scholar
 Y. Niu, D. W. C. Ho, and Z. Wang, “Improved sliding mode control for discretetime systems via reaching law,” IET Control Theory and Applications, vol. 4, no. 11, pp. 2245–2251, 2010. View at: Publisher Site  Google Scholar
 Y. Pan and K. Furuta, “Variable structure control with sliding sector based on hybrid switching law,” International Journal of Adaptive Control and Signal Processing, vol. 21, no. 89, pp. 764–778, 2007. View at: Publisher Site  Google Scholar  MathSciNet
 X. Yu, B. Wang, and X. Li, “Computercontrolled variable structure systems: the stateoftheart,” IEEE Transactions on Industrial Informatics, vol. 8, no. 2, pp. 197–205, 2012. View at: Publisher Site  Google Scholar
 K. J. Astrom and B. Wittenmark, ComputerControlled Systems: Theory and Design, Prentice Hall, Upper Saddle River, NJ, USA, 1997.
 G. F. Franklin, J. D. Powell, and M. Workman, Digital Control of Dynamic Systems, AddisonWesley, Longman Publishing, Reading, Mass, USA, 1997.
 W. Zhang, M. S. Branicky, and S. M. Phillips, “Stability of networked control systems,” IEEE Control Systems Magazine, vol. 21, no. 1, pp. 84–97, 2001. View at: Publisher Site  Google Scholar
 W. P. Heemels, A. R. Teel, N. van de Wouw, and D. Nesic, “Networked control systems with communication constraints: tradeoffs between transmission intervals, delays and performance,” IEEE Transactions on Automatic Control, vol. 55, no. 8, pp. 1781–1796, 2010. View at: Publisher Site  Google Scholar  MathSciNet
 X. 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. View at: Publisher Site  Google Scholar
 R. S. Raji, “Smart networks for control,” IEEE Spectrum, vol. 31, no. 6, pp. 49–55, 1994. View at: Publisher Site  Google Scholar
 Y. Tipsuwan and M.Y. Chow, “Control methodologies in networked control systems,” Control Engineering Practice, vol. 11, no. 10, pp. 1099–1111, 2003. View at: Publisher Site  Google Scholar
 G. C. Walsh, H. Ye, and L. Bushnell, “Stability analysis of networked control systems,” IEEE Transactions on Control Systems Technology, vol. 10, no. 3, pp. 438–446, 2002. View at: Publisher Site  Google Scholar
 L. Dugard and E. I. Verriest, Stability and Control of TimeDelay Systems, Springer, Heidelberg, Germany, 1998. View at: Publisher Site  MathSciNet
 S. Janardhanan and B. Bandyopadhyay, “Output feedback discretetime sliding mode control for time delay systems,” IEE Proceedings on Control Theory and Applications, vol. 153, no. 4, pp. 387–396, 2006. View at: Publisher Site  Google Scholar  MathSciNet
 A. J. Koshkouei and A. S. I. Zinober, “Sliding mode timedelay systems,” in Proceedings of the IEEE International Workshop on Variable Structure Systems (VSS '96), pp. 97–101, December 1996. View at: Google Scholar
 X. Q. Liu, W. Q. Ge, and Y. T. Chui, “Variable structure predictor controller with quasi sliding mode for systems with delay,” in Proceedings of the IEEE International Symposium on Industrial Electronics, pp. 211–214, 1992. View at: Google Scholar
 Y. Xia, J. Han, and Y. Jia, “A sliding mode control for linear systems with input and state delays,” in Proceedings of the 41st IEEE Conference on Decision and Control, pp. 3332–3337, Phoenix, Ariz, USA, December 2002. View at: Publisher Site  Google Scholar
 A. Bartoszewicz and P. Latosinski, “Quasisliding mode networked controller for linear discrete time systems subject to disturbance,” in Proceedings of the 22nd Mediterranean Conference on Control and Automation, pp. 328–333, 2014. View at: Google Scholar
Copyright
Copyright © 2015 Andrzej Bartoszewicz and Paweł Latosiński. 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.