About this Journal Submit a Manuscript Table of Contents
Advances in Mechanical Engineering
Volume 2013 (2013), Article ID 604393, 16 pages
Research Article

Dynamic Control and Disturbance Estimation of 3D Path Following for the Observation Class Underwater Remotely Operated Vehicle

1National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin 150001, China
2University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany

Received 25 August 2013; Accepted 23 October 2013

Academic Editor: Xiaoting Rui

Copyright © 2013 Hai Huang 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.


This study addresses the question of 3D path following for the observation class underwater remotely operated vehicle. The dynamic model of the investigated remote operated vehicle is taken as a coupled multibody system composing of a flexible body and a rigid body. For precise control, the tether cable disturbance has been investigated as well via a dynamic model. Each element of the tethered cable even has been taken as an elastic body, and the waves and current disturbances have been taken into consideration. Based on the multibody system model, an adaptive backstepping sliding mode controller has been designed. To improve the controller’s systematic robustness against disturbances, the sliding mode surface and adaptive control rule have been designed, too. Experiments have been performed in a tank, including the 3D path following controls of depth, heading, advance, sideway, polygon line, and spiral line. With current and wave disturbances having been taken into consideration, the tether effect has been analyzed, the efficacy and superiority of adaptive backstepping sliding mode control have been verified. It is further confirmed from the comparisons that the investigated method outperforms those S surface based controllers.

1. Introduction

Observation class underwater remotely operated vehicles (ROVs) have been used widely in many areas such as hull and harbor inspection, underwater salvage, and pollution surveillance. High accuracy positioning and path following are demanded for underwater inspections and manipulations. The 3D path following is extremely important for accomplishing missions executed by subsea remotely operated vehicles. Observation class ROV is not so big; thus its motion is easy to be affected by the tethered cable, and also by current, wave, and wind. Therefore, the robust 3D path-following control for observation used ROV has been recognized as one of the most challenging issues [1, 2].

In order to meet the requirements of path following (sometimes refer to trajectory tracking when it is under a temporal law) for underwater vehicles, various types of control approaches have been studied. Kokegei et al. proposed a sliding mode controller for underwater vehicle to realize coupled 6 DoF trajectory control [3]. Bian et al. presented a nonlinear feedback controller based on input state for path following [4]. But those control techniques assume that the unknown dynamic model can be represented by a linear one with unknown parameters and a regression matrix is specified for the ROVs [5]. Fuzzy linguistic rules are more flexible for dealing with nonlinear control problems. Thus, Liu et al. in [6] and Wang et al. in [7] both provided an S surface (plane) controller in combination with fuzzy control for underwater vehicles navigation. A trained neural-fuzzy system can approximate the linear or nonlinear mapping of disturbance, but the objectives for training are usually difficult to be obtained; see in [8, 9]. Marzbanrad et al. modified sliding mode with robust adaptive fuzzy control algorithm for ROVs path following control. In that case, fuzzy algorithm was used for online estimation of external disturbances as well as unknown nonlinear terms of the dynamic model of the ROV. A robust control rule is employed to compensate the estimation errors [2]. Zhang et al. also derived an output feedback controller using nonlinear control theory [10]. They proved that a nonlinear output feedback integral controller provides exponential stability. In consideration of disturbance caused by wave-induced hydrodynamic forces, Patompak and Nilkhamhang provided an adaptive backstepping sliding mode controller for station keeping and path following of underwater vehicle based on its dynamic model [11]. Lapierre and Jouvencel extended a kinematic controller with backstepping and Lyapunov-based techniques so as to cope with parametric uncertainties and external disturbances [12]. The controllers shown in [1315] confirmed that the model-based backstepping controller outperforms the conventional linear controller for the wide range of velocities.

A teleoperated ROV can be taken as a mass-spring system, and periodic waves accompanied with strong current may excite the motion of the vehicle. Tethered cable tensions may cause forces and position nonlinear oscillations on ROV as stated in [16, 17]. Thus a quasistatic or dynamic model reflecting the umbilical cable behavior is very necessary, however, being quite complicated, for precise trajectory control [1820]. Prabhakar and Buckham developed a computational tethered cable model by applying finite element technique with decoupled PD controller [21]. Researchers de Souza and Maruyama estimated tethered cable dynamics from a lumped mass tethered cable model and issued a PI feed forward control strategy for ROV position control [22]. Soylu et al. considered the tethered cable as a series of lumped point masses connected by linear, massless, and visco-elastic springs and proposed a model-based sliding mode controller to improve the ROV performances [23]. Montano et al. modeled the tethered cable dynamics in a quasi-stationary state and presented adaptive control scheme for tethered cable perturbations [24]. Unfortunately, based on our knowledge, there are very few of previous studies that consider the effects from tethered cable and the disturbances from current, wave, and wind all together. However, none of these effects can be neglected during the practical applications. This study exactly focuses on this issue and contributes an adaptive backstepping sliding mode controller which takes into consideration all the mentioned aspects for the high precision 3D path following of one of observation class ROVs.

The rest of this study is organized as follows. Section 2 sets up the dynamic model of the ROV multibody system especially including the considered umbilical cable. A controller which is for the 3D path following is designed in Section 3, while Section 4 presents the experimental results. Conclusions are given in Section 5 to close this paper.

2. Dynamic Model of the ROV System

2.1. Modeling the Whole ROV Multibody System

The ROV is usually operating with the support vessel (mother ship) in the ocean; see Figure 1. The tethered cable not only provides power and communication media but also brings nonlinear drag forces upon ROV. In this study, the considered system is a multibody system including a flexible body, that is, the tethered cable, and a rigid body, that is, the ROV itself.

Figure 1: Multibody system of the considered ROV with tethered cable.

The dynamic equation of the whole ROV multibody system according to [5] can be represented by where is the mass matrix of the ROV, vector is a pose vector of the ROV, is a matrix of centrifugal and Coriolis terms, is the damping matrix, is the vector of gravitational forces and moments, is a disturbance vector, and contains the forces and torques from thrusts, while is the drag effects from the tethered cable. For observation class ROV, the drag force from tethered cable is one of the most important nonlinear disturbances particularly when the current or the wave is strong. It is one of the most important factors, which restricts the path-following accuracy. The following subsection focuses on the investigation of drag forces from the tethered cable.

2.2. Tethered Cable Drag Forces and Boundary Conditions

The tethered cable is a long flexible cylinder which can only sustain tensile loads and its length varies over the time. The tension force at any point of a tethered cable in fluid according to [5] can be described by where is the effective mass per unit length, is the inertial acceleration, is the unstretched Lagrangian coordinate, is the fluid dynamic force per unit length, and is the cable unit length weight which can be written as since its direction is fixed. We defineas the density of the tethered cable, as the cross-sectional area of the tether, and with for the profile diameter of the cable. If we take EA as the cable stiffness and as the stretched Lagrangian coordinate of the tethered cable profile which has then the tension force in (2) exerted atwith its value is calculated by due to Hooke’s law. In Figure 2, is the diving depth of the ROV, and and are the horizontal and vertical drag forces, respectively, at the ship end of the tethered cable, that is, at point .

Figure 2: Model of tethered cable of ROV system in the water.

In order to analyze the motion of the tethered cable and its effect on the vehicle, three coordinate systems have been established which are the fixed frame , the local frame on one of the cable ends at the mother ship side, and the ROV frame . We define as the angle formed by the horizontal and local tangential direction at the considered point, , . Therefore, where is the velocity of any point of the tethered cable.

For the tethered cable in the air, the first item at the left side of (2) is in which is the value of gravitational acceleration. Here only the vertical direction is considered since it is a gravitational force. If we set as the wind velocity relative to the cable, we have as the wind effect on the length tethered cable, where is the air density, the strength of local wind velocity above the sea surface can be expressed by in which is the wind speed at 10 meters above the sea surface, is the cable height out of the water counting from to the water plane, and is only the cable height out of the water counting from the considered point to the water plane; see Figure 2. Here is the drag coefficient in the air. If we focus on the th microunit length of tethered cable in the air; (see Figure 3), one has Here we consider as usual that the wind is horizontal only, and in this case in right horizontal,andare the two tension forces at the two ends of theth microunit length cable,.

Figure 3: The th microunit length of tethered cable in the air and its forces analysis.

Similarly, as shown in Figure 4, the tethered cable is in the water.

Figure 4: The th microunit length of tethered cable in the water and its forces analysis.

Its dynamics in the water hold where, and is the density of water. Hereandare the two tension forces at the two ends of theth microunit length cable under water, and is the force due to the wave and current disturbances which usually only considers for the horizontal direction. Ifis assumed to be the resultant velocity of wave and current velocities, then the relative velocity between the cable and this resultant velocity is Thus, if the drag coefficient in the water is represented by, then In this study we consider that the wave and current forces are in horizontal direction, for example, in Figures 2 and 4, and they are in the right horizontal direction.

Considering the boundary condition at, that is, at point, the cable tension force is

At the point on ROV, the cable tension force can be calculated by assuming that is in right horizontal direction and is in down vertical direction. Here , in which and are the reactive forces in horizontal and vertical directions, respectively, at point on ROV. At the right side of (13), , , , and are the corresponding microunits accelerations. The cable tension force at point is .

In (13), other corresponding terms can be governed by whereis the total length of the cable, that is, from to , and is the length of the cable out of water, that is, from to the water surface.

In the following, we analyse the cable tension force at the water surface. This boundary condition can be considered either from the cable part in the air or the part in the water. Considering the cable in the air, one has whereandare the cable tension forces at horizontal and vertical directions, respectively. The actual directions of these cable tension forces are determined via the positive or negative characteristics of the force values. If it is positive, its actual direction is the same as assumed, otherwise in the opposite of the assumed. Here we assume that is in right horizontal direction andis in down vertical direction. In (15),. If we consider the cable part in the water, one further has whereandare the cable tension forces at horizontal and vertical directions, respectively, on the water surface and considered at the water side. In addition, the following equations should be met which are The moment at is governed by when it is in the equilibrium state. Here is the horizontal length of the cable in the air, is the horizontal length of the total cable.

3. Controller Design for Path Following

The ROV dynamic model described in state space can be expressed as where is the state vector and is the observation variable. Set to be the desired trajectory of ROV and it is twice differentiable. The main steps of the adaptive backstepping sliding mode control can be described as follows.

Step 1. According to the path following objective,is selected as the trajectory tracking error and defined as
Then define virtual control coefficient where . The velocity error of the ROV is defined as The corresponding Lyapunov function is selected as Thus, It is possible to adjust and let it satisfy ; then is satisfied which means the first subsystem of our whole ROV system is stable.

Step 2. To improve the controller’s systematic robustness against the disturbances, the following switching function of the terminal sliding mode surface is defined which is whereis the switching function of the terminal sliding mode,. Then the Lyapunov function is chosen as Therefore,
To satisfy the Lyapunov stability theory and the reaching law of the controller as in [25], the derivative is defined as where and are positive constants. We design the back-stepping sliding mode controller as where is the estimation error of the disturbance ; that is, , in which is the estimation of . Here is the control input.

Step 3. In order to estimate further disturbances and avoid the undetermined upper bounds, we define Lyapunov function: whereis a positive constant. If we assume the disturbance being time invariant in a short period, thus The adaptive control rule can be selected as Further we can design the backstepping sliding mode controller as same as in (29); that is, With (31)–(33), one can obtain If we set it yields By selecting proper values of,, andto satisfy; that is, the transformation matrix is regarded as the positive definite matrix; then where. This means the third subsystem is also stable which is sufficient for our ROV system.

4. Experiments

4.1. Experimental Setup

In order to verify and analysis the investigated tethered cable dynamic model and the backstepping controller, experiments have been performed based on an SY-II remote operated vehicle; see Figure 5. The SY-II ROV is an open frame underwater vehicle, which is purposed for ship hull inspection. Its power supply and communication cable are a neutrally buoyant tether which is attached to the vehicle at the tail. The control commands are sent through network communication between surface computer and PC104 embedded processor. It is equipped with depth gauge, ultrasonic Doppler velocity meter (DVL), magnetic compass as motion sensors, underwater CCD, image sonar, ultrasonic thickness gauge as environmental sensors, and 6 thrusters including 2 main thrusters, 2 side thrusters, and 2 vertical thrusters. Its tethered cable and hydrodynamic and inertial parameters are illustrated in Tables 1, 2, and 3, respectively. The experiments have been performed in a 50 m × 30 m × 10 m tank at the Key Laboratory of Science and Technology on Underwater Vehicle in Harbin Engineering University. Comparisons have been made between the S surface controller investigated in [6] and our adaptive backstepping sliding mode controller. Current and waves are generated from local current generation device (Figure 6(a)) and multidirectional waves generator (Figure 6(b)), respectively, in the experiments to simulate real oceanic conditions.

Table 1: Tethered cable parameters.
Table 2: SY-II hydrodynamic parameters.
Table 3: SY-II inertial parameters.
Figure 5: SY-II open-frame underwater vehicle.
Figure 6: Water tank with wave generator and local current generation device.
4.2. Experiments and Results Analysis

In the depth control experiments of Figures 7 and 8, the tension force from tethered cable upon SY-II ROV changes little mainly because its neutral buoyancy, and the peak values of tethered cable moment are decided by diving speed. The tethered cable makes little effect upon depth control when diving is not very fast. Adaptive backstepping sliding mode controller has improved the stability and speed constringency in depth control in comparing with S surface controller since it provides dynamic compensation during the experiments.

Figure 7: The 6-meter depth control experiments.
Figure 8: The 8-meter depth control experiments.

In the heading control experiments of Figures 9 and 10, the desired heading is and relative to the normal north of earth’s magnetic field. The tension force and moment from tethered cable upon SY-II ROV change a lot at first during revolution process. The tethered cable and ROV’s rectangle shape have made nonlinear effects to the ROV manipulation. As a result, the S surface controller cannot realize precise heading control, while adaptive backstepping sliding mode controller qualifies, since the controller delivers command according to its dynamic manipulation and character.

Figure 9: The heading control experiments.
Figure 10: The heading control experiments.

During the northward advancing process (see Figures 11 and 12), the SY-II ROV that is advancing mainly depends on two main thrusters. Two conditions, that is, and  m/s normal east ( is the currents speed), are compared. Here the current has made great influence upon tethered cable and generates tension force and moment. Nonlinear disturbance from current and tether has produced much greater errors for the S surface controller than the adaptive backstepping sliding mode controller.

Figure 11: Advance toward north with two main thrusters, .
Figure 12: Advance toward north with two main thrusters,  m/s normal eastward.

During the eastward lateral motion process (see Figures 13 and 14), the SY-II ROV that is laterally moving mainly depends on two lateral thrusters. Two conditions, that is, and  m/s normal north, are compared. At first SY-II ROV has been blown a few meters away, and then the control started. The current has generated great tension force and moment effect upon SY-II ROV. Adaptive backstepping sliding mode controller has realized more accuracy result compared to the S surface controller.

Figure 13: Lateral motion toward east with two side thrusters, .
Figure 14: Lateral motion toward east with two side thrusters,  m/s normal northward.

During the 3D path-following experiments in the tank (see Figures 15 and 16), the disturbances are current and waves. The current speed is  m/s, the wave is a sine wave with speed  m/s, and height  m, respectively. Polygon path and spiral path are followed, respectively. As the experiments we made above, environmental disturbances exerted on the tethered cable have contributed nonlinear effect to the ROV. The S surface controller manages to follow the polygon path but have to adjust continuously and heavily to follow the spiral path. Comparatively, adaptive backstepping sliding mode controller can compensate more disturbances and can follow the desired path more precisely. Deduced from dynamic model, adaptive backstepping sliding mode controller is established to calculate and compensate nonlinear disturbances caused by current and tether. Therefore, it outperforms S surface controller in control accuracy and robustness.

Figure 15: 3D path-following experiments, polygon path,  m/s,  m/s normal northward.
Figure 16: 3D path-following experiments, spiral path,  m/s,  m/s, normal northward.

For the experiments to be performed successfully, we have also run some preliminary simulations to get the initial value of some uncertainties. The simulation and experimental results agree with each other well. Details of simulation results can be found in [26, 27].

5. Conclusions

The main contributions of this paper are summarized as follows. At first, the dynamic tether model has been established based on the lumped mass cable model. Each element of the cable is taken as an elastic body and takes into consideration waves and current disturbances. Based on the dynamic model, an adaptive backstepping sliding mode controller has been designed. Secondly, the controller has taken dynamic model into consideration at the first and second steps (backstepping steps) based on Lyapunov functions. To improve the controller’s systematic robustness and against disturbances, the sliding mode surface has been defined. An adaptive control rule has been chosen to further resist environmental disturbance which is the most difficult part for ROV 3D path following.

Experiments have been performed in the tank, including depth control, heading control, advance control, sideway control, polygon line, and spiral line 3D path-following control. With current and waves disturbances being taken into consideration, tethered cable effect has been analyzed, and the efficacy and superiority of our designed controller have been verified. The comparisons to the S surface controller have shown better performances of the investigated adaptive backstepping sliding mode controller.

Conflict of Interests

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


This project is supported by National Science Foundation of China under the Grants of no. 51209050, no. 51179035, no. 51279221, and no. 61100006, the Doctoral Fund of Ministry of Education for Young Scholar with no. 20122304120003, State Key Laboratory of Ocean Engineering of Shanghai Jiao Tong University no. 1102, State Key Laboratory of Robotics and Systems of Harbin Institute of Technology no. SKLRS-2012-ZD-03, and the Fundamental Research Funds for the Central Universities with no. HEUCFR1101.


  1. K. Zhu and L. Gu, “A MIMO nonlinear robust controller for work-class ROVs positioning and trajectory tracking control,” in Proceedings of the Chinese Control and Decision Conference (CCDC '11), pp. 2565–2570, Mianyang, China, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. A. R. Marzbanrad, M. Eghtesad, and R. Kamali, “A robust adaptive fuzzy sliding mode controller for trajectory tracking of ROVs,” in Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, pp. 2863–2870, Orlando, Fla, USA, December 2011.
  3. M. Kokegei, F. He, and K. Sammut, “Fully coupled 6 degree-of-freedom control of an over-actuated autonomous underwater vehicle,” in Autonomous Underwater Vehicles, N. A. Cruz, Ed., pp. 147–170, InTech, Rijeka, Croatia, 2011.
  4. X. Bian, Y. Qu, Z. Yan, and W. Zhang, “Nonlinear feedback control for trajectory tracking of an unmanned underwater vehicle,” in Proceedings of the IEEE International Conference on Information and Automation (ICIA '10), pp. 1387–1392, Harbin, China, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Bagheri, T. Karimi, and N. Amanifard, “Tracking performance control of a cable communicated underwater vehicle using adaptive neural network controllers,” Applied Soft Computing Journal, vol. 10, no. 3, pp. 908–918, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Liu, H. Yu, and Y. Xu, “Improved S plane control algorithm for underwater vehicles,” Journal of Harbin Engineering University, vol. 23, no. 1, pp. 33–36, 2002 (Chinese).
  7. J. Wang, G. Wu, L. Wan, Y. Sun, and L. Wang, “Controller design of underwater robots based on generalized S-plane,” Electric Machines and Control, vol. 13, supplement 1, pp. 144–148, 2009 (Chinese). View at Scopus
  8. P. W. J. van de Ven, C. Flanagan, and D. Toal, “Neural network control of underwater vehicles,” Engineering Applications of Artificial Intelligence, vol. 18, no. 5, pp. 533–547, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. W. Luo and Z. Zou, “Neural network based robust controller for trajectory tracking of underwater vehicles,” China Ocean Engineering, vol. 21, no. 2, pp. 281–292, 2007. View at Scopus
  10. L. Zhang, X. Qi, and Y. Pang, “Adaptive output feedback control based on DRFNN for AUV,” Ocean Engineering, vol. 36, no. 9-10, pp. 716–722, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. P. Patompak and I. Nilkhamhang, “Adaptive backstepping sliding-mode controller with bound estimation for underwater robotics vehicles,” in Proceedings of the 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp. 1–4, Phetchaburi, Thailand, May 2012.
  12. L. Lapierre and B. Jouvencel, “Robust nonlinear path-following control of an AUV,” IEEE Journal of Oceanic Engineering, vol. 33, no. 2, pp. 89–102, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. G. Antonelli, F. Caccavale, S. Chiaverini, and G. Fusco, “A novel adaptive control law for underwater vehicles,” IEEE Transactions on Control Systems Technology, vol. 11, no. 2, pp. 221–232, 2003. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Wang and C. S. G. Lee, “Self-adaptive recurrent neuro-fuzzy control of an autonomous underwater vehicle,” IEEE Transactions on Robotics and Automation, vol. 19, no. 2, pp. 283–295, 2003. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Xiao, “Variable structure control of a catastrophic course in a high-speed underwater vehicle launched out of the water,” Advances in Mechanical Engineering, vol. 2013, Article ID 398707, 8 pages, 2013. View at Publisher · View at Google Scholar
  16. F. Muttin, “Umbilical deployment modeling for tethered UAV detecting oil pollution from ship,” Applied Ocean Research, vol. 33, no. 4, pp. 332–343, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. K. Zhu, H. Zhu, Y. Zhang, and J. Gao, “A multi-body space-coupled motion simulation for a deep-sea tethered remotely operated vehicle,” Journal of Hydrodynamics, vol. 20, no. 2, pp. 210–215, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. Z. Feng and R. Allen, “Evaluation of the effects of the communication cable on the dynamics of an underwater flight vehicle,” Ocean Engineering, vol. 31, no. 8-9, pp. 1019–1035, 2004. View at Publisher · View at Google Scholar · View at Scopus
  19. F. R. Driscoll, R. G. Lueck, and M. Nahon, “Development and validation of a lumped-mass dynamics model of a deep-sea ROV system,” Applied Ocean Research, vol. 22, no. 3, pp. 169–182, 2000. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Wu, J. Ye, C. Yang, Y. Chen, H. Tian, and X. Xiong, “Experimental study on a controllable underwater towed system,” Ocean Engineering, vol. 32, no. 14-15, pp. 1803–1817, 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Prabhakar and B. Buckham, “Dynamics modeling and control of a variable length remotely operated vehicle tether,” in Proceedings of the MTS/IEEE OCEANS, pp. 1255–1262, Washington, DC, USA, September 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. E. C. de Souza and N. Maruyama, “Intelligent UUVs: some issues on ROV dynamic positioning,” IEEE Transactions on Aerospace and Electronic Systems, vol. 43, no. 1, pp. 214–226, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. S. Soylu, B. J. Buckham, and R. P. Podhorodeski, “Dynamics and control of tethered underwater-manipulator systems,” in Proceedings of the MTS/IEEE OCEANS, pp. 1–8, Seattle, Wash, USA, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. A. Montano, M. Restelli, and R. Sacco, “Numerical simulation of tethered buoy dynamics using mixed finite elements,” Computer Methods in Applied Mechanics and Engineering, vol. 196, no. 41–44, pp. 4117–4129, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. N. Chen, F. Song, G. Li, X. Sun, and C. Ai, “An adaptive sliding mode backstepping control for the mobile manipulator with nonholonomic constraints,” Communications in Nonlinear Science and Numerical Simulation, vol. 18, no. 10, pp. 2885–2899, 2013.
  26. H. Huang, L. Wan, Y. Li, and Y. Pang, “The fault tolerable control system structure of SY-II remote operated vehicle,” Advanced Materials Research, vol. 308–310, pp. 1483–1491, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. H. Huang, S. Jiang, L. Wan, and Y. Pang, “Type-2 fuzzy logic neural network control and target following for remote operated vehicles,” in Proceedings of the IEEE International Conference on Mechatronics and Automation, pp. 516–521, Chengdu, China, August 2012.