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Mathematical Problems in Engineering
Volume 2015, Article ID 379647, 10 pages
http://dx.doi.org/10.1155/2015/379647
Research Article

Quasi-Sliding Networked Control of Systems Subject to Unbounded Disturbance with Limited Rate of Change

Institute of Automatic Control, Technical University of Łódź, 18/22 Stefanowskiego Street, 90-924 Łódź, Poland

Received 24 July 2014; Revised 29 October 2014; Accepted 30 October 2014

Academic Editor: Rongni Yang

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.

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

Continuous-time 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 [48]. In mid-1980s, the theory of variable structure control has been extended to discrete-time systems [9, 10], which in turn inspired many significant works related to discrete-time sliding mode control [1133]. In particular, discrete-time sliding mode control has great significance in digital environments [34, 35], where no continuous states are available.

Since the introduction of relatively low-cost control networks [3641], the problem of effectively controlling discrete-time systems with time delay [4246] 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 discrete-time 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 dead-beat 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 network-induced 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 dead-beat controller for the delay-free 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 quasi-sliding 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 quasi-sliding 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.

Figure 1: Sliding variable .
Figure 2: System output .
Figure 3: Control signal .

It can be seen from Figure 1 that the sliding variable arrives inside the quasi-sliding 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.

Table 1: IAE and ISE for .
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 quasi-sliding 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.

Figure 4: Sliding variable .
Figure 5: System output .
Figure 6: Control signal .

It can be seen from Figure 4 that the sliding variable arrives inside the quasi-sliding 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.

Table 2: IAE and ISE for .

5. Conclusions

In this paper, the problem of sliding mode control of a discrete-time 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.

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