Research Article | Open Access

# Adaptive Synchronization Control of Multiple Vessels with Switching Communication Topologies and Time Delay

**Academic Editor:**Mehmet Onder Efe

#### Abstract

Recently, synchronization movement control of multiple vessels has been studied broadly. In most of the studies, the communication network among vessels is considered to be fixed and the time delay is often ignored. However, the communication network among vessels maybe vary because of switching of different tasks, and the time delay is necessary to be considered when the communication network is unreliable. In this paper, the synchronization movement of multiple vessels with switching connected communication topologies is studied, and an adaptive synchronization control algorithm that is based on backstepping sliding mode control is proposed. The control algorithm is achieved by defining cross coupling error which is combination of the trajectory tracking error and velocity tracking error. And an adaptive control term is used to estimate the external disturbances, so that the unknown external disturbances can be compensated. Furthermore, the robustness of the control law to time-varying time delay is also discussed. At last, some simulations are carried out to validate the effectiveness of the proposed synchronization control algorithm.

#### 1. Introduction

Recently, the applications of synchronization movement control of multiple vessels are increasing. For example, in underway replenishment, its need to control supply vessels maintains the same state with the receiving vessel to insure safety. And another example is a group of vessels performing the seabed mapping operations together, which can implement larger area in shorter time compared with a single vessel. Multiple vessels work together not only improving operation performance but also reducing difficulty. And this paper mainly discusses the synchronization movement control of multiple vessels.

In recent years, synchronization control of multiple agents has been extensively studied in different fields, such as robot systems [1, 2], chaotic systems consensus [3, 4], and coordinated formation control of aircraft and spacecraft [5, 6]. As the development of synchronization control of multiple agents, there are several synchronization control approaches which are proposed. For example, distributed cooperative attitude synchronization control approach has been discussed in [7]. And adaptive control is a classical strategy used to address synchronization movement [8, 9]. Besides, using cascaded system theory and graph theory, a distributed attitude cooperative control scheme is studied in [10]. Compared with the earlier work in computational load and required states, Chung and Slotine proposes a simple synchronization framework to achieve cooperative movement of multiple agents [11]. In the presence of uncertainties of underactuated autonomous surface vehicles and ocean disturbances, a robust adaptive dynamic surface control law is proposed in [12]. There are some other synchronization movement control methods, such as backstepping control design [13, 14], artificial potential approach [15], leader-follower network strategy [16, 17], and methods that are based on graph theory [18, 19]. The main traits of the aforementioned articles are that they assume the communication topology is fixed without considering time delay.

Switching communication topologies and time delay are two fundamental realities in the communication among vessels, but these factors are usually ignored in [20–22]. In recent years, switching communication topologies or time delay has been investigated [23–25], and time delay in the communication network is usually assumed to be a constant. However, in multiple-vessel network, because the bandwidth of communication is limited, communication topology among vessels may be varied when the marine task changes. And the time delay cannot be a constant because of the varying relative position among vessels and unknown external statistics. Thus, it is necessary to design a synchronization controller for multiple vessels with switching communication topologies and time delay. Moreover, for the surface vessels, which often encounter external disturbances, and the disturbances are usually difficult to model because of the disturbances varying with the complex ocean circumstance. So the synchronization movement control law should be robust to unknown disturbances and an adaptive control is useful to solve this problem [26–28].

In view of the above reasons, the main innovation of this paper can be drawn as follows: an adaptive synchronization movement control law that is based on backstepping sliding mode control for multiple vessels with switching communication topologies is proposed. Different from the traditional controller design approach that maybe suffers difficulties to determine the Lyapunov function, the backstepping-based design method provides us with an appropriate Lyapunov function simply and ensures the stability of closed-loop system, and the sliding mode method shows robustness to external disturbances and system uncertainty; the combination of backstepping and sliding mode takes both advantages of the two methods, and an adaptive term is introduced to improve the synchronization control algorithm. Moreover, the directed strongly connected communication topology may be not balanced, which means one vessel can receive information from the neighbors and not necessarily share its own information with the neighbors, and compared to the existing studies, the requirement on the communication is relaxed. And cross coupling error using trajectory tracking error and velocity tracking error is defined, and the cross coupling error is introduced into the switched function. Furthermore, in the presence of time delay, the designed synchronization control strategy is improved, and the synchronization control method is robust to time delay.

The arrangement of this paper is as follows. In Section 2, the basic knowledge for graph theory and the mathematical model of vessels are given. The adaptive synchronization control approach with switching communication topologies and time delay is discussed in Section 3. In Section 4, some simulations are carried out to validate the proposed control algorithm. At last, conclusions and constructive prospects are drawn in Section 5.

#### 2. Preliminaries

##### 2.1. Graph Theory

For the multiple vessels, the communication topology and information exchange among vessels can be described by a graph. Let describe the information exchanges among vessels, which consists of nodes and edges , and composes edges where joint nodes come from . A node in the graph represents a vessel. The edges represent the information exchange links among the vessels. Node is a neighbor of node , if . Let the set of the neighbors of node denoted by . Assume matrix is the adjacency matrix of graph , and is defined as , if , otherwise, , if . Define ; then the Laplacian matrix of the weighted graph can be noted . For all , if , then the graph is called undirected. Otherwise, it is directed.

Define a set of communication topology graphs . And the nodes of graph are the same, but the edge sets are different. Therefore, the Laplacian matrix of graph is denoted by . It is necessary to satisfy that all the communication topologies are connected and is a positive semidefinite matrix.

If the communication topology is connected, then define is a column stack vector, and satisfies .

##### 2.2. Mathematical Model of Vessel

The 3-DOF surface motion model of the vessel can be described as where is the position and heading in the earth-fixed reference frame and is the velocity with regard to the body-fixed reference frame. is a rotation matrix, which can be written as where is a positive definite inertia mass matrix and . represents the hydrodynamic Coriolis and centripetal matrix. denotes damping matrix. illustrates the control forces and torques input. are the external disturbances.

The motion mathematical model of the vessel in the earth-fixed reference frame is where the relation of the conversion yields

The vessel model (3) holds the following properties.

*Property 1. *The inertia matrix is symmetric, positive definite, and bounded as

*Property 2. *The damping matrix satisfies

#### 3. Adaptive Synchronization Control Design

##### 3.1. Adaptive Synchronization Control with Switching Communication Topologies

The objective of this section is to develop a control method for achieving state synchronization of multiple vessels while tracking time-varying trajectories , and , in the presence of switching communication topologies, and a new backstepping sliding mode synchronization controller is designed.

The model of vessel in a compact form yields

Define the trajectory tracking error of vessel as

Take the derivative of as

Define the stabilizing function for vessel as where is a diagonal positive definite matrix.

Define the velocity tracking error of vessel as

The switched function of vessel is chosen as where is a diagonal positive definite matrix.

Define a new parameter as

The vessels are said to synchronize if

With vessel model (7), we can get

In order to prove stability of switching communication topologies, define a common Lyapunov function for all communication topologies.

Define the first Lyapunov function as

The time derivative of (16) yields

Choose the second Lyapunov function as

Therefore, the time derivative of (18) yields

Let the coupling control law for vessel be

Choose the synchronization control input as where is the estimate of external disturbances and is the element of weighted adjacency matrix .

Define the third Lyapunov function as where and is a positive number.

Assume the disturbances are unknown in advance and vary slowly, which means .

The time derivative of (22) yields where

Then, we adopt an adaptive term to estimate the disturbances. Define the adaptive control law of as where is a positive constant.

With (25) and (23), we can obtain where represents the th communication topology and .

Consider the communication topologies are connected; then (26) is a negative definite function, and in view of the Lyapunov function (22) is a strict common function for the switched system. Therefore, we can get , , and as with the arbitrary switching among the communication topologies; with (8) and (12), we can get , , and as ; the velocity tracking error and trajectory tracking error approached zero asymptotically.

With (12) and (13), yields where denotes the synchronization position error between vessel and vessel and is the desired relative position between vessel and vessel . And note . From (27), we can know that (27) represents a linear exponentially stable system with the input , as and is bounded. Then, it can be obtained that , which means , . Therefore, the synchronization position error and synchronization velocity error approach zero asymptotically; that is, the vessels achieve state synchronization.

Theorem 1. *Consider the model of vessel described by (7) with the synchronization control law (21) and adaptive control law (25). If the communication topologies are connected, then the synchronization errors and the tracking errors are uniformly ultimately bounded in spite of switching communication topologies and unknown disturbances.*

##### 3.2. Adaptive Synchronization Control with Time Delay

In view of unreliable communication networks among multiple vessels, time-varying time delay is introduced to describe the condition. In this section, denotes the time delay from vessel to vessel , and it is assumed that time delay is bounded and continuously differentiable, which means , , .

In the presence of time delay, the vessels are considered to be delayed synchronization if

To achieve delayed synchronization, define a positive constant gain as

Similar to the front section, the procedure yields the following.

Define the first Lyapunov function as

The time derivative of (30) yields

Choose the second Lyapunov function as

Therefore, the time derivative of (32) yields

Choose the delayed synchronization control input as where is the estimate of the disturbances.

Define the third Lyapunov function as where , and is a positive number.

The time derivative of (35) yields

Choose the adaptive control law of is

Then, (36) yields

Hence, are bounded, and , , , as . The delayed synchronization can be further rewritten as where ; similar to the preceding section, we can approve that the trajectory tracking error, velocity tracking error, and synchronization error are asymptotically stable.

Theorem 2. *Consider a group of vessels described by (7) with communication delay in the communication network, using the delayed synchronization control law (34) and disturbances adaptive control law (37); the trajectory tracking error, velocity tracking error, and synchronization error are asymptotically stable, and the multiple vessels can realize synchronization movement.*

#### 4. Simulation Results

To verify the effectiveness of the proposed synchronization control algorithm, some simulations are carried out. Assume there are four vessels; the mode parameters are illustrated in [29]. The switching topologies are illustrated in Figure 1.

**(a)**

**(b)**

**(c)**

Therefore, the adjacency matrix and Laplacian matrix of the communication graphs are

Assume the initial position of vessels as follows:

And the desired trajectory of each vessel is

The external disturbances are chosen as

The parameters of the controller are

The simulation results with switching topologies are shown in Figures 2–6. In the first 1000 s, communication topology (a) is used, and from 1000 s to 2000 s, the communication topology (b) is employed. In the last 1000 s, the communication topology (c) is employed.

The synchronization movement curves of vessels are shown in Figure 2. From the curves, it can be seen that the proposed control law can achieve synchronization movement for multiple vessels in the presence of switching communication topologies. The heading yaw curves of four vessels are shown in Figure 3. And in Figures 4, 5, and 6, the surge velocity, sway velocity, and yaw velocity in the synchronization process are shown.

In the next section simulation, we discuss when there is time-varying time delay in the communication topology (a), and simulations are carried out. Assume the time delay as follows: is assumed to be equal for the sake of simplicity which is selected as . The simulation results are shown in Figures 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, and 18.

From Figures 7–18, it can be seen that comparing to the case without time delay in the communication network, the adjusting time may be longer. But the synchronization position error and velocity error among vessels asymptotically converge to zeros in the presence of time delay; that is, the vessels can achieve synchronization movement, and the designed control law is robust to time-varying time delay.

#### 5. Conclusion

For the synchronization control of multiple vessels in the presence of switching communication topologies, an adaptive synchronization control algorithm is proposed. With the directed connected graph describing the communication topology among vessels, a new cross coupling error that includes trajectory tracking error and velocity tracking error is added into the backstepping sliding mode controller, and an adaptive term is introduced to estimate the unknown external disturbances. Furthermore, by introducing a new parameter, the improved synchronization control law is robust to time-varying time delay. Finally, the effectiveness of the synchronization control strategy is supported through several simulations. In the future work, it is desired to consider the mode uncertainty, and it would be useful considering that the velocities of vessels are unknown.

#### Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

#### Acknowledgments

The work is supported by the National Natural Science Foundation of China (no. 51209056) and the Fundamental Research Funds for the Central Universities (no. HEUCF041401).

#### References

- S.-J. Chung and J.-J. E. Slotine, “Cooperative robot control and concurrent synchronization of lagrangian systems,”
*IEEE Transactions on Robotics*, vol. 25, no. 3, pp. 686–700, 2009. View at: Publisher Site | Google Scholar - Y. Bouteraa, J. Ghommam, N. Derbel, and G. Poisson, “Nonlinear control and synchronization with time delays of multiagent robotic systems,”
*Journal of Control Science and Engineering*, vol. 2011, Article ID 632374, 10 pages, 2011. View at: Publisher Site | Google Scholar - G. A. Leonov, A. I. Bunin, and N. Koksch, “Attraktorlokalisierung des Lorenz-Systems,”
*Zeitschrift für Angewandte Mathematik und Mechanik*, vol. 67, no. 12, pp. 649–656, 1987. View at: Publisher Site | Google Scholar | MathSciNet - T. Zhou, J. Lü, G. Chen, and Y. Tang, “Synchronization stability of three chaotic systems with linear coupling,”
*Physics Letters A*, vol. 301, no. 3-4, pp. 231–240, 2002. View at: Publisher Site | Google Scholar | MathSciNet - A.-M. Zou, K. D. Kumar, and Z.-G. Hou, “Attitude coordination control for a group of spacecraft without velocity measurements,”
*IEEE Transactions on Control Systems Technology*, vol. 20, no. 5, pp. 1160–1174, 2012. View at: Publisher Site | Google Scholar - R. W. Beard, J. Lawton, and F. Y. Hadaegh, “A coordination architecture for spacecraft formation control,”
*IEEE Transactions on Control Systems Technology*, vol. 9, no. 6, pp. 777–790, 2001. View at: Publisher Site | Google Scholar - W. Ren, “Distributed cooperative attitude synchronization and tracking for multiple rigid bodies,”
*IEEE Transactions on Control Systems Technology*, vol. 18, no. 2, pp. 383–392, 2010. View at: Publisher Site | Google Scholar - H. Wang, “Passivity based synchronization for networked robotic systems with uncertain kinematics and dynamics,”
*Automatica*, vol. 49, no. 3, pp. 755–761, 2013. View at: Publisher Site | Google Scholar | MathSciNet - D. Sun, “Position synchronization of multiple motion axes with adaptive coupling control,”
*Automatica*, vol. 39, no. 6, pp. 997–1005, 2003. View at: Publisher Site | Google Scholar | MathSciNet - H. Du and S. Li, “Attitude synchronization control for a group of flexible spacecraft,”
*Automatica*, vol. 50, no. 2, pp. 646–651, 2014. View at: Publisher Site | Google Scholar | MathSciNet - S.-J. Chung and J.-J. E. Slotine, “Cooperative robot control and synchronization of lagrangian systems,” in
*Proceedings of the 46th IEEE Conference on Decision and Control (CDC '07)*, pp. 2504–2509, New Orleans, La, USA, December 2007. View at: Publisher Site | Google Scholar - Z. Peng, D. Wang, Z. Chen, X. Hu, and W. Lan, “Adaptive dynamic surface control for formations of autonomous surface vehicles with uncertain dynamics,”
*IEEE Transactions on Control Systems Technology*, vol. 21, no. 2, pp. 513–520, 2013. View at: Publisher Site | Google Scholar - D. Zhao, T. Zou, S. Li, and Q. Zhu, “Adaptive backstepping sliding mode control for leader-follower multi-agent systems,”
*IET Control Theory & Applications*, vol. 6, no. 8, pp. 1109–1117, 2012. View at: Publisher Site | Google Scholar | MathSciNet - B. A. Idowu, U. E. Vincent, and A. N. Njah, “Generalized adaptive backstepping synchronization for non-identical parametrically excited systems,”
*Nonlinear Analysis. Modelling and Control*, vol. 14, no. 2, pp. 165–176, 2009. View at: Google Scholar | MathSciNet - J. Ghommam, M. Saad, and F. Mnif, “Robust adaptive formation control of fully actuated marine vessels using local potential functions,” in
*Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '10)*, pp. 3001–3007, May 2010. View at: Publisher Site | Google Scholar - J. Hu and Y. Hong, “Leader-following coordination of multi-agent systems with coupling time delays,”
*Physica A: Statistical Mechanics and Its Applications*, vol. 374, no. 2, pp. 853–863, 2007. View at: Publisher Site | Google Scholar - E. Peymani and T. I. Fossen, “Leader-follower formation of marine craft using constraint forces and lagrange multipliers,” in
*Proceedings of the 51st IEEE Conference on Decision and Control*, pp. 2447–2452, December 2012. View at: Publisher Site | Google Scholar - A. Jadbabaie, J. Lin, and A. S. Morse, “Coordination of groups of mobile autonomous agents using nearest neighbor rules,”
*IEEE Transactions on Automatic Control*, vol. 48, no. 6, pp. 988–1001, 2003. View at: Publisher Site | Google Scholar | MathSciNet - W. Ren and R. W. Beard, “Consensus seeking in multiagent systems under dynamically changing interaction topologies,”
*IEEE Transactions on Automatic Control*, vol. 50, no. 5, pp. 655–661, 2005. View at: Publisher Site | Google Scholar | MathSciNet - Y. Bouteraa, J. Ghommam, N. Derbel, and G. Poisson, “Non-linear adaptive synchronisation control of multi-agent robotic systems,”
*International Journal of Systems, Control and Communications*, vol. 4, no. 1-2, pp. 55–71, 2012. View at: Publisher Site | Google Scholar - H. Zhang, K. Pothuvila, Q. Hui, R. Yang, and J. M. Berg, “Control of synchronization for multi-agent systems in acceleration motion with additional analysis of formation control,” in
*Proceedings of the American Control Conference (ACC '11)*, pp. 509–514, San Francisco, Calif, USA, July 2011. View at: Publisher Site | Google Scholar - A. Das and F. L. Lewis, “Cooperative adaptive control for synchronization of second-order systems with unknown nonlinearities,”
*International Journal of Robust and Nonlinear Control*, vol. 21, no. 13, pp. 1509–1524, 2011. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - A. R. Mehrabian, S. Tafazoli, and K. Khorasani, “Cooperative tracking control of euler-lagrange systems with switching communication network topologies,” in
*Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics*, pp. 756–762, 2010. View at: Google Scholar - H. Shi, L. Wang, and T. Chu, “Coordinated control of multiple interactive dynamical agents with asymmetric coupling pattern and switching topology,” in
*Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '06)*, pp. 3209–3214, Beijing, China, October 2006. View at: Publisher Site | Google Scholar - Y.-C. Liu and N. Chopra, “Robust controlled synchronization of interconnected robotic systems,” in
*Proceedings of the American Control Conference (ACC '10)*, pp. 1434–1439, Baltimore, Md, USA, July 2010. View at: Google Scholar - S. Yin, H. Luo, and S. X. Ding, “Real-time implementation of fault-tolerant control systems with performance optimization,”
*IEEE Transactions on Industrial Electronics*, vol. 61, no. 5, pp. 2402–2411, 2014. View at: Publisher Site | Google Scholar - I. A. Gravagne, J. M. Davis, and J. J. Dacunha, “A unified approach to high-gain adaptive controllers,”
*Abstract and Applied Analysis*, vol. 2009, Article ID 198353, 8 pages, 2009. View at: Publisher Site | Google Scholar - W. Zhang, Z. Wang, and Y. Guo, “Adaptive backstepping-based synchronization of uncertain networked Lagrangian systems,” in
*Proceedings of the American Control Conference (ACC '11)*, pp. 1057–1062, San Francisco, Calif, USA, July 2011. View at: Publisher Site | Google Scholar - E. Kyrkjebø and K. Y. Pettersen,
*Motion coordination of mechanical systems [Ph.D. thesis]*, Department of Engineering Cybernetics, Norwegian University of Science and Technology, 2007.

#### Copyright

Copyright © 2015 Fuguang Ding 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.