# The Bioinspired Model-Based Hybrid Sliding-Mode Formation Control for Underactuated Unmanned Surface Vehicles.

1. IntroductionThe formation control of USVs is receiving increasing interest in control science and engineering [1-3]. Multiple USVs can accomplish challenging and dangerous tasks, such as mine clearance, patrol, investigation, and the transportation of strategic materials, which can not only decrease personal injury but also achieve tasks that a single USV cannot complete [4]. To achieve a desirable formation pattern, several methods including leader-follower approach [5], virtual structure strategy [6], behavioral-based approach [7], and artificial potential function [8] have been proposed. Among these methods, the leader-follower strategy is commonly used because of simplicity and scalability. To address the formation problem, many control schemes have been implemented. An adaptive backstepping method for a group of underactuated autonomous vehicles was studied in [9, 10], and the course angle error between leader and follower was considered to guarantee the follower's course angle stability. A Linear Quadratic Regulator Proportional Integral (LQR PI) controller was implemented in [11] for centralized heterogeneous leader-follower architecture. With this control scheme, formation geometry can be switched to any shape while flying, and obstacle avoidance can be realized. In [12], a sliding-mode control law for controlling multiple USVs in arbitrary formations was proposed, mesh stability and parameter uncertainty in the dynamic model and wave disturbance were considered in designing the controllers, and the effectiveness of the controller was verified by the computer simulation. Though it has an outstanding characteristic insensitivity to parameter variations, one major drawback of sliding-mode control is the inherent problem of chattering. In addition, many intelligent methods were used to achieve formation control. A bounded neural network control law was constructed with the aid of a saturated function for the USV while over a closed curve in [13]. An adaptive neural network formation controller combined with a dynamic surface technique for USVs was studied in [14], which solved the problem of "explosion of complexity" by introducing the first-order filter. In [15], a robust adaptive formation control law is proposed for multiple autonomous surface vehicles moving in a leader-follower formation. However, while all works mentioned above have common styles, the procedure for designing a controller is complex, and too many parameters needed to be adjusted. Based on graph-theoretic concepts and locally distributed information, a neural fuzzy formation controller was designed with the capability of online learning in [16]. Mu and Wang [17] proposed fuzzy-based path following control for USV to solve the problem of unknown control gain coefficient. The fuzzy rules-based formation control approaches can solve the problem of large initial robot velocities, but formulating the fuzzy rules is not easy because they are usually obtained by trial and error based human knowledge [18]. A neural network needs either online learning or offline training procedures, either of which could be computational complicated.

The main contribution of this work is to solve the problem of the speed jump and actuator control constraints of USV formation. The leader-follower strategy is used since it is easy to implement. When the controller is designed, a virtual USV is introduced to produce a predefined path, and the other USV keeps the desired distance and angle with it to achieve the desired formation pattern. A bioinspired model-based hybrid sliding-mode controller is designed, inspired by [19], and on the basis of the previous literature [4]. It can not only avoid the traditional chattering problem and smooth the input signal but also simplify the control law.

2. Formation Model of USV

2.1. Single USV Model. Consider a class of networked multiagent systems consisting of n-unmanned surface underactuated vehicles. The dynamical model of the i-th USV is given by [20]

[mathematical expression not reproducible] (1)

The signals [x.sub.i], [y.sub.i], and denote position and orientation (yaw angle), respectively, in the earth fixed frame. [u.sub.i], [v.sub.i], and [r.sub.i] represent the surge, sway, and yaw velocities with respect to body frame, respectively. [m.sub.ii] denotes the masses including added masses in the surge, sway and yaw axes. The damping term in the body coordinate is described by [d.sub.ii]. The signals [tau] = [([[tau].sub.ui], 0, [[tau].sub.ri]).sup.T] are the surge force and yaw torque inputs provided by thrusters. W = [[[d.sub.u],[d.sub.v],[d.sub.r]].sup.T denotes the loads induced by waves, wind, and ocean currents along the surge, sway, and yaw axes, respectively [21].

2.2. USV Formation Model. In this work, the formation control problem for USV is addressed by a distributed strategy based on virtual leader strategy. For convenience, only 2 USVs are considered as a group. The mathematical model is formed as in Figure 1: where l is the distance between leader USV and follower USV, [phi] is described as the relative angle between follower and leader, [l.sub.d], [[phi].sub.d] are the desired distance and angle, and [l.sub.x], [l.sub.y] represent the components of l in the earth fixed frame on x- and y-axes. p is the bow position of USV, [x.sub.Fp], [y.sub.Fp], [[psi].sub.Fp] are the position and orientation of p, and d is the distance between p and the mass center of the follower. We can construe from Figure 1 that

[x.sub.Fp] = [x.sub.F] + d cos [[psi].sub.F],

[y.sub.Fp] = [y.sub.F] + d sin [[psi].sub.F] (2)

To achieve the desired formation pattern, the following inequality should be guaranteed:

[mathematical expression not reproducible] (3)

[[zeta].sub.1], [[zeta].sub.2] are positive constants that can be arbitrarily small. [l.sub.d], [[phi].sub.d] are the desired distance and angle yet bounded and there exist a positive constant, that is, [[rho].sub.1], [[rho].sub.2], such that [parallel][[??].sub.d][parallel] [less than or equal to] [[rho].sub.1], [parallel][[??].sub.d][parallel] [less than or equal to] [[rho].sub.2].

If the value of l, [phi] can be confirmed, then the value of [l.sub.x], [l.sub.y] can be confirmed, and then the value of l, [phi] can be transformed to control [l.sub.x], [l.sub.y]. As seen from the picture,

[l.sub.x] = l cos [theta] = [x.sub.L] - [x.sub.F] - d cos [[psi].sub.F],

[l.sub.y] = l sin [theta] = [y.sub.L] - [y.sub.F] - d cos [[psi].sub.F] (4)

Then,

[mathematical expression not reproducible] (5)

The desired distance between 2 USVs is considered as [l.sub.d], and the projection weight in the earth fixed frame is [l.sup.d.sub.x], [l.sup.d.sub.y], so

[l.sup.d.sub.x] = -[l.sub.d] cos ([[phi].sub.d] + [[psi].sub.L]),

[l.sup.d.sub.y] = -[l.sub.d] sin ([[phi].sub.d] + [[psi].sub.L]) (6)

Differentiating (6) yields

[mathematical expression not reproducible] (7)

The errors of the formation model can be defined as

[mathematical expression not reproducible] (8)

From what has been discussed above, the formation mathematics model can be written as follows:

[mathematical expression not reproducible] (9)

where

[mathematical expression not reproducible] (10)

and we can get that [[mu].sub.1], [[mu].sub.2] are bounded since [absolute value of sin([[phi].sub.d] + [e.sub.[psi]])] [less than or equal to] 1, [absolute value of cos([[phi].sub.d] + [e.sub.[psi]])] [less than or equal to] 1, [l.sub.d], [[phi].sub.d] are bounded, and [mathematical expression not reproducible].

Based on [4], we have the following assumptions.

Assumption 1. The surge velocity [u.sub.i] [greater than or equal to] 0.

Assumption 2. Trajectory ([x.sub.l], [y.sub.l]) produced by the virtual leader satisfies that its derivatives with respect to t exit up to second order.

Assumption 3. The terms [d.sub.u], [d.sub.v], [d.sub.r] that satisfy [d.sub.u] [less than or equal to] [absolute value of [D.sub.u]], [d.sub.v] [less than or equal to] [absolute value of [D.sub.v]], [d.sub.r] [less than or equal to] [absolute value of [D.sub.r]], [D.sub.u], [D.sub.v], [D.sub.r] are constants.

Assumption 4. The follower can receive the position and velocity information from the leader by sensors in time.

In summary, the underactuated USV formation control problem can be divided into two parts: kinematics control and dynamics control.

For kinematics control, virtual surge, and sway velocities are designed to make

[mathematical expression not reproducible] (11)

For dynamics control, surge force and yaw torque are designed to make actual velocities approximate to virtual velocities.

3. Hybrid Control Strategy for Unmanned Surface Vehicles

3.1. Virtual Velocity Controller. The virtual velocity controller based on the backstepping approach can be defined as

[mathematical expression not reproducible] (12)

Here, [u.sub.d], [v.sub.d], [r.sub.d] are the virtual surge, sway, and yaw motion speed of the following USVs, [u.sub.L], [v.sub.L], [r.sub.L] are the desired velocity in the body-fixed frame, and [k.sub.1], [k.sub.2], [k.sub.3] are a positive constant, respectively.

3.2. Bioinspired Velocity Controller. With the analysis of (12), the virtual speed is directly related to the state error. The traditional backstepping method will generate a sharp speed jump when a sudden tracking error occurs. This means that large acceleration and forces/moments are required that make exceeding the control constraint practically impossible. To solve the speed jump and control constraint problems, a bioinspired model is introduced to design the virtual speed controller.

The bioinspired neural dynamics model was first put forward by Grossberg [21]. It can describe the online adaptive behavior of individuals. It was originally derived based on the membrane model proposed by Hodgkin and Huxley [22] for a patch of membrane using electrical elements. The dynamics of voltage across the membrane can be described in the membrane model, using state equation technique as

[C.sub.m] d[V.sub.m]/dt = -([E.sub.p] +[V.sub.m]) [g.sub.p] + ([E.sub.Na] - [V.sub.m]) [g.sub.Na]

- ([E.sub.k] + [V.sub.m]) [g.sub.k] (13)

where [V.sub.m] is the neural activity of the j-th neuron in the neural network, the parameters [E.sub.p] + [V.sub.m], [E.sub.Na] - [V.sub.m], [E.sub.k] + [V.sub.m] are nonnegative constants representing the passive decay rate, the upper and lower bounds of the neural activity, respectively, and the variables the excitatory and inhibitory inputs to the neuron.

The bioinspired model can be defined as following form:

[[??].sub.i] = -[AV.sub.i] + (B - [V.sub.i]) f ([e.sub.i]) - (D + [V.sub.i]) g ([e.sub.i]) (14)

where

[mathematical expression not reproducible] (15)

In this work, the tracking errors [e.sub.x], [e.sub.y], [e.sub.p] are chosen as the input of the neural dynamic model, and the outputs [V.sub.i] will substitute the error of [e.sub.x], [e.sub.y], [e.sub.p].

[mathematical expression not reproducible] (16)

[A.sub.i] (i = 1,2,3) are nonnegative constants on behalf of the neurons of attenuation rate. [B.sub.i], [D.sub.i] (i = 1,2,3) are considered nonnegative constants that denote the upper and lower bounds of neurons dynamic, which can restrict the outputs to [-[D.sub.i], [B.sub.i]]

Therefore, the proposed virtual speed controller is as follows:

[mathematical expression not reproducible] (17)

k, [k.sub.3] are the same parameters as (12). Based on the description above, [V.sub.x], [V.sub.y], [V.sub.[psi]] are all bounded and smooth without any sharp jumps when the inputs suddenly change.

3.3. Sliding-Mode Controller. After the virtual speed controller has been designed, a sliding-mode controller is introduced to produce the control force to make the USV arrive at the virtual speed.

Generally, the process of designing the sliding-mode controller is divided into two parts: defining a sliding manifold and designing a control law to move toward the sliding manifold.

The sliding manifold is selected as

[S.sub.1] = [[GAMMA].sub.eu] + [[lambda].sub.1] [[integral].sup.t.sub.0] [[GAMMA].sub.eu] ([tau])d[tau] (18)

where [[GAMMA].sub.eu] = [u.sub.F] - [[GAMMA].sub.u] is the error between the reality surge velocity and the virtual velocity of the leader. From the derivation of (18), we can get

[mathematical expression not reproducible] (19)

let [[??].sub.1] = 0; we can get the equivalent control law

[[tau].sub.uFeq] = [m.sub.11] [[??].sub.u] - [[lambda].sub.1] [m.sub.11] ([u.sub.F] - [[GAMMA].sub.u]) - [m.sub.22] [u.sub.F] [r.sub.F] + [d.sub.11][u.sub.F]

-[d.sub.u] (20)

Then, the switching control law can be selected as

[[tau].sub.uFsw] = -[[eta].sub.1] sgn ([s.sub.1]) (21)

We can obtain the surge force control law

[[tau].sub.uF] = [[tau].sub.uFeq] + [[tau].sub.uFsw] (22)

Next, the yaw control torque will be designed.

The yaw control torque is based on a two-order sliding manifold, and the sliding manifold is defined according to the sway speed error of the underactuated unmanned surface vehicles:

[mathematical expression not reproducible] (23)

where [[GAMMA].sub.ev] = [v.sub.F] - [[GAMMA].sub.v] and [[lambda].sub.2] is a positive constant.

By derivation (23), we can get

[mathematical expression not reproducible] (24)

[mathematical expression not reproducible] (25)

[mathematical expression not reproducible] (26)

Considering the difficulty of computing [[??].sub.ev], a feedback control input of acceleration error is introduced:

[mathematical expression not reproducible] (27)

let [[??].sub.2] = 0; then the equivalent control law can be designed as

[mathematical expression not reproducible] (28)

The switching control law is

[[tau].sub.rFsw] = [m.sub.22][m.sub.33]/[m.sub.11][u.sub.f] ([[eta].sub.2]sgn ([s.sub.2])) (29)

so, the yaw control torque is

[[tau].sub.rF] = [[tau].sub.rFeq] + [[tau].sub.rFsw] (30)

The controller based on the backstepping method can be described as

[mathematical expression not reproducible] (31)

[mathematical expression not reproducible] (32)

where [u.sup.[alpha].sub.j], [r.sup.[alpha].sub.j] are virtual velocity controller.

4. Stability Analysis

We choose a Lyapunov function as

[V.sub.1] = 1/2 ([e.sup.2.sub.x] + [e.sup.2.sub.y] + [e.sup.2.sub.[psi]]) + k/2B ([V.sup.2.sub.x] + [V.sup.2.sub.y]) + [k.sub.3]/2B [V.sup.2.sub.[psi]] [greater than or equal to] 0, (33)

k, B, and [k.sub.3] are positive constants.

Let [V.sub.10] = (1/2)([e.sup.2.sub.x] + [e.sup.2.sub.y]) + (k/2B)([V.sup.2.sub.x] + [V.sup.2.sub.y]) [greater than or equal to] 0, [V.sub.11] = (1/2)[e.sup.2.sub.[psi]] + ([k.sub.3] /2B)[V.sup.2.sub.[psi]].

Then,

[mathematical expression not reproducible] (34)

Substituting (9) and (12) into (32), we can get

[mathematical expression not reproducible] (35)

and if B = D,

[mathematical expression not reproducible] (36)

In the same way,

[mathematical expression not reproducible] (37)

as defined in (14), if [e.sub.i] [greater than or equal to] 0, then f([e.sub.i]) = [e.sub.i], g([e.sub.i]) = 0, A + f([e.sub.i]) + g([e.sub.i]) = A + [e.sub.i] > 0, f([e.sub.i]) - g([e.sub.i]) - [e.sub.i] = [e.sub.i] - [e.sub.i] = 0.

If [e.sub.i] < 0, then f([e.sub.i]) = 0, g([e.sub.i]) = -[e.sub.i],

A + f([e.sub.i]) + g([e.sub.i]) = A - [e.sub.i] > 0,

f([e.sub.i]) - g([e.sub.i]) -[e.sub.i] = [e.sub.i] - [e.sub.i] = 0 (38)

We can determine that A + f([e.sub.i]) + g([e.sub.i]) is a positive constant and f([e.sub.i]) - g([e.sub.i]) -[e.sub.i] equal to 0. So, [V.sub.10] < 0, [V.sub.11] < 0; therefore [[??].sub.1] = [V.sub.10] + [V.sub.11] < 0, so [V.sub.1] is monotonically decreasing and [mathematical expression not reproducible]; we can obtain. Based on Lyapunov stability, the outer loop is stable.

To guarantee the surge speed [u.sub.F] [right arrow] [[GAMMA].sub.u], a candidate Lyapunov function is chosen as follows:

[V.sub.2] = 1/2 [m.sub.11] [s.sup.2.sub.1] (39)

and, then, derivation of (36),

[mathematical expression not reproducible] (40)

where [[eta].sub.1] is a positive constant. This means that [s.sub.1] has asymptotic stability, so the trajectory can reach the origin, [[GAMMA].sub.eu] [right arrow] 0, [u.sub.F] [right arrow] [[GAMMA].sub.u].

Choose another Lyapunov function as

[V.sub.3] = 1/2 [m.sub.22][m.sub.33][s.sup.2.sub.2] (41)

With Assumption 1, we can get

[mathematical expression not reproducible] (42)

Therefore, [s.sub.2] is asymptotic stability, [[GAMMA].sub.ev] [right arrow] 0, [v.sub.F] [right arrow] [[GAMMA].sub.v]. The surge speed and sway speed of unmanned surface vehicles are bounded, which has been proven above, and the yaw angle speed is also bounded, which will be verified below.

Consider the augmented Lyapunov function:

[V.sub.4] = 1/2 [m.sub.33] [r.sup.2.sub.F]; (43)

then,

[mathematical expression not reproducible] (44)

If ([m.sub.11] - [m.sub.22])[u.sub.F][v.sub.F] + [[tau].sub.rF] + [d.sub.r] < [d.sub.33] [r.sub.F] is satisfied, then [[??].sub.4] < 0, [V.sub.4] is a decreasing function. Since [V.sub.4] = (1/2)[m.sub.33][r.sup.2.sub.F] [greater than or equal to] 0, [r.sub.F] is also decreasing and the largest value must exist. [u.sub.F], [v.sub.F], [[tau].sub.rF] is bounded, which has been proven above, and [d.sub.r] is also bounded, as clarified in Assumption 3, so [r.sub.F] must be bounded.

5. Simulation Results and Analysis

5.1. Straight Line Formation Control. To verify the effectiveness of the proposed strategy, the mathematical value of 3 USVs is used for simulation from [23].

[M.sub.11] = 120 x [10.sup.3] kg, [m.sub.22] = 217.9 x [10.sup.3] kg, [m.sub.33] = 636 x [10.sup.5] kg,

[d.sub.11] = 215 x [10.sup.2] kg*[s.sup.-1], [d.sub.22] = 117 x [10.sup.3] kg*[s.sub.-1], [d.sub.33] = 802 x [10.sup.4] kg*[s.sub.-1].

A simple case of straight line formation control is considered first. In the simulation, USV 2 and USV 3 are followers; USV 1 is a virtual leader. The system initial state is as follows: [[eta].sub.2] = [[0.1,2, [pi]/2].sup.T], [[eta].sub.3] = [[2, -5, [pi]/2].sup.T]; the desired position and orientation with respect to USV1 are as follows: l[d.sub.12] = 5m, l[d.sub.13] = 5m, [[phi].sub.d12] = [pi]/2, [[phi].sub.d13] = -[pi]/2. We select [k.sub.1]=[k.sub.2]=k=3, [k.sub.3]=3,[A.sub.1]=[A.sub.2]=[A.sub.3]=4, [B.sub.1]=[D.sub.1]=6, [B.sub.2]=[D.sub.2]=5, [B.sub.3]=[D.sub.3]=5, [[lambda].sub.1] = 1, [[eta].sub.1] = 150, [[lambda].sub.2] = 1.3, [[eta].sub.2] = 300. The disturbance is defined as time-varying disturbance W = 4 x [10.sup.3][[sin(0.2f), 1 + 2sin(0.3t), 2].sup.T].

The motion curve of three USVs under the proposed scheme is shown in Figure 2. As seen from Figures 2-4, at first, the leaders adjust their control input, making them approach the desired distance and orientation. After a few seconds, the leader and the follower have reached the desired index. During this procedure, we can see from Figure 3 that the yaw angle changes slowly; this is attributed to the bioinspired model constraining the control input when the initial error was large and included the coordinate transformation of the control point. Figure 4 shows that the control input of the USV is smooth and different from the traditional method in [24]. As the controller based on the backstepping approach, this controller causes the sharp speed jumps when tracking errors change suddenly at initial time. For example, the surge speed of the backstepping method jumps to more than 10 m/s, but the biological inspired method is just about 5 m/s in Figures 5 and 6. Therefore, the proposed bioinspired method is more practical and effective.

5.2. Circular Formation Control. A typical circular path was studied in this section. The initial state [[eta].sub.1] = [[0.1,0.1, [pi]/4].sup.T], [[eta].sub.2] = [[6,0.1,0.1].sup.T], [[eta].sub.3] = [[0.1,6,[pi]/2].sup.T]. The desired distance and orientation are l[d.sub.12]=8m, l[d.sub.13]=8m, [phi][d.sub.12] = [pi]/2, [phi][d.sub.13] = -[pi]/2. We select [k.sub.1] = [k.sub.2] = [k.sub.3] = k = 1, [A.sub.1] = [A.sub.2] = [A.sub.3] = 8, [B.sub.1] = [D.sub.1] = 10, [B.sub.2] = [D.sub.2] = 6, [B.sub.3] = [D.sub.3] = 6. [[lambda].sub.1] = 1.6, [[eta].sub.1] = 200, [[lambda].sub.2] = 1, [[eta].sub.2] = 200.

The disturbance is defined as time-varying disturbance W = 4 x [10.sup.3] [[sin(0.2f), 1 + 2 sin(0.3f), 2].sup.T]. The simulation results are as shown in Figures 5-7. We can also see that, after the first 20 seconds, the followers USV 2 and USV 3 produce a control force and torque based on the bioinspired hybrid control method, making them move in the error-decreasing direction. Though the distance error and orientation error are large at first, the control input changes slowly in the first few seconds, which leads to the error tending toward zero at a reasonable speed in Figures 8 and 9.

6. Conclusion and Future Work

The development of a bioinspired model-based hybrid sliding-mode formation controller for underactuated unmanned surface vehicles has been presented. The kinematics and dynamic equation of USV formation are first established; then a bioinspired-based, sliding-mode hybrid control strategy, including a virtual speed controller and a sliding-mode controller, is proposed. The stability and effectiveness of the controller have been proven by the Lyapunov theory and verified through MATLAB simulation. The controller addresses the problem of speed jump and controller saturation caused by the large initial error and guarantees yaw angle stability through coordinate transformation. However, collision avoidance and communication between the follower and the leader should be considered in future work. In addition, distributed formation control and constrained control of underactuated marine vehicles are also highly desirable; related research is most concerned with the full actuated surface vehicles so far [25, 26].

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

https://doi.org/10.1155/2018/7563178

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was partially supported by the National Nature Science Foundation of China (Grant no. 51309062).

References

[1] K. D. Do, "Synchronization Motion Tracking Control of Multiple Underactuated Ships with Collision Avoidance," IEEE Transactions on Industrial Electronics, vol. 63, no. 5, pp. 2976-2989, 2016.

[2] X. Wang and S. Li, "Distributed finite-time consensus algorithms for leader-follower second-order multi-agent systems with mismatched disturbances," in Proceedings of the American Control Conference, pp. 2814-2819, IEEE, 2016.

[3] Z.-H. Peng, D. Wang, W.-Y. Lan, and G. Sun, "Robust leader-follower formation tracking control of multiple underactuated surface vessels," China Ocean Engineering, vol. 26, no. 3, pp. 521534, 2012.

[4] M.-Y. Fu, D.-S. Wang, and C.-L. Wang, "Formation Control for Water-Jet USV Based on Bio-Inspired Method," China Ocean Engineering, vol. 32, no. 1, pp. 117-122, 2018.

[5] X. Wang, S. Li, and X. Yu, "Distributed finite-time consensus algorithms for leader-follower second-order multi-agent systems with mismatched disturbances," in Proceedings of the American Control Conference (ACC), pp. 2814-2819, IEEE, Boston, MA, USA, 2016.

[6] 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.

[7] C. Ma and Q. Zeng, "Distributed formation control of 6DOF autonomous underwater vehicles networked by sampled-data information under directed topology," Elsevier Science Publishers, 2015.

[8] N. E. Leonard and E. Fiorelli, "Virtual leaders, artificial potentials and coordinated control of groups," in Proceedings of the 40th IEEE Conference on Decision and Control (CDC), vol. 3, pp. 2968-2973, IEEE, USA, 2001.

[9] J. Ghommam and M. Saad, "Backstepping-based cooperative and adaptive tracking control design for a group of underactuated AUVs in horizontal plan," International Journal of Control, vol. 87, no. 5, pp. 1076-1093, 2014.

[10] L. Ding and G. Guo, "A backstepping method for ship formation control," Control and Decision, vol. 27, no. 2, pp. 299-303, 2012.

[11] Q. Ali and S. Montenegro, "Explicit model following distributed control scheme for formation flying of mini UAVs," IEEE Access, vol. 4, pp. 397-406, 2016.

[12] F. Fahimi, "Sliding-mode formation control for underactuated surface vessels," IEEE Transactions on Robotics, vol. 23, no. 3, pp. 617-622, 2007

[13] L. Liu, D. Wang, Z. Peng, and H. H. T. Liu, "Saturated coordinated control of multiple underactuated unmanned surface vehicles over a closed curve," Science China Information Sciences, vol. 60, no. 7, 2017

[14] 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.

[15] B. Ranjbar-Sahraei, F. Shabaninia, A. Nemati, and S.-D. Stan, "A novel robust decentralized adaptive fuzzy control for swarm formation of multiagent systems," IEEE Transactions on Industrial Electronics, vol. 59, no. 8, pp. 3124-3134,2012.

[16] Y. H. Chang, C. L. Chen, W. S. Chan, H. W. Lin, and C. W. Chang, "Fuzzy Formation Control and Collision Avoidance for Multiagent Systems," Mathematical Problems in Engineering, vol. 2013, no. 8, Article ID 908180, pp. 61-61, 2013.

[17] D.-D. Mu, G.-F. Wang, Y.-S. Fan, Y.-M. Bai, and Y.-S. Zhao, "Fuzzy-Based Optimal Adaptive Line-of-Sight Path Following for Underactuated Unmanned Surface Vehicle with Uncertainties and Time-Varying Disturbances," Mathematical Problems in Engineering, pp. 1-12, 2018.

[18] B. Sun, D. Zhu, and S. X. Yang, "A bio-inspired cascaded approach for three-dimensional tracking control of unmanned underwater vehicles1," International Journal of Robotics and Automation, vol. 29, no. 4, pp. 349-358, 2014.

[19] Z. Peng, D. Wang, and X. Hu, "Robust adaptive formation control of underactuated autonomous surface vehicles with uncertain dynamics," Iet Control Theory & Applications, vol. 5, no. 12, pp. 1378-1387, 2011.

[20] R. Yang and D. Zhu, "Plane trajectory tracking control of autonomous underwater vehicles based on a biologically inspired shunting model," Journal of Shanghai Maritime University, 2011.

[21] T. I. Fossen, Marine Control Systems: Guidance, Navigation, and Control of Ships, Rigs and Underwater Vehicles, 2002.

[22] S. Grossberg, "Nonlinear neural networks: principles, mechanisms, and architectures," Neural Networks, vol. 1, no. 1, pp. 17-61, 1988.

[23] A. L. Hodgkin and A. F. Huxley, "A quantitative description of membrane current and its application to conduction and excitation in nerve," The Journal of Physiology, vol. 117, no. 4, pp. 500-544, 1952.

[24] K. D. Do, Z. P. Jiang, and J. Pan, "Underactuated ship global tracking under relaxed conditions," IEEE Transactions on Automatic Control, vol. 47, no. 9, pp. 1529-1536,2002.

[25] Z. Peng, J. Wang, and D. Wang, "Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation," IEEE Transactions on Control Systems Technology, vol. 99, pp. 1-8, 2017

[26] S.-L. Dai, S. He, H. Lin, and C. Wang, "Platoon Formation Control With Prescribed Performance Guarantees for USVs," IEEE Transactions on Industrial Electronics, vol. 99, pp. 1-1,2017

Mingyu Fu and Duansong Wang

Harbin Engineering University, Harbin, Heilongjiang, China

Correspondence should be addressed to Duansong Wang; wangduansong@hrbeu.edu.cn

Received 2 September 2018; Revised 3 October 2018; Accepted 9 October 2018; Published 2 December 2018

Academic Editor: Sing Kiong Nguang

Caption: FIGURE 1: Leader-follower formation configuration with the control point P.

Caption: FIGURE 2: The USV motion curve of straight line formation.

Caption: FIGURE 3: The position and attitude of the three USV.

Caption: FIGURE 4: The force and torque of USV 2 and USV 3.

Caption: FIGURE 5: The velocity of USV with the proposed method.

Caption: FIGURE 6: The velocity of USV with backstepping method.

Caption: FIGURE 7: The USV motion curve of straight line formation.

Caption: FIGURE 8: The force and torque of USV 2 and USV 3.

Caption: FIGURE 9: The position and attitude of the three USV.

Printer friendly Cite/link Email Feedback | |

Title Annotation: | Research Article |
---|---|

Author: | Fu, Mingyu; Wang, Duansong |

Publication: | Journal of Control Science and Engineering |

Article Type: | Report |

Date: | Jan 1, 2018 |

Words: | 4813 |

Previous Article: | Study on Load Parameters Estimation during AMT Startup. |

Next Article: | Nonfragile [H.sub.[infinity]] Filter Design for Nonlinear Continuous-Time System with Interval Time-Varying Delay. |

Topics: |