Distributed Consensus-Based Robust Adaptive Formation Control for Nonholonomic Mobile Robots with Partial Known Dynamics

Peng, Zhaoxia; Yang, Shichun; Wen, Guoguang; Rahmani, Ahmed

doi:https://doi.org/10.1155/2014/670497

Mathematical Problems in Engineering

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Abstract Introduction Background Conclusion Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2014 | Article ID 670497 | https://doi.org/10.1155/2014/670497

Distributed Consensus-Based Robust Adaptive Formation Control for Nonholonomic Mobile Robots with Partial Known Dynamics

Zhaoxia Peng,¹Shichun Yang,¹Guoguang Wen,²and Ahmed Rahmani³

Academic Editor: Quang Phuc Ha

Received17 Dec 2013

Accepted10 Mar 2014

Published04 May 2014

Abstract

This paper investigates the distributed consensus-based robust adaptive formation control for nonholonomic mobile robots with partially known dynamics. Firstly, multirobot formation control problem has been converted into a state consensus problem. Secondly, the practical control strategies, which incorporate the distributed kinematic controllers and the robust adaptive torque controllers, are designed for solving the formation control problem. Thirdly, the specified reference trajectory for the geometric centroid of the formation is assumed as the trajectory of a virtual leader, whose information is available to only a subset of the followers. Finally, numerical results are provided to illustrate the effectiveness of the proposed control approaches.

1. Introduction

In the past decades, cooperative control of multiple mobile robots has been receiving significant attention owing to many potential advantages of such systems over single robot. In fact, multirobot cooperative control means a group of mobile robots working cooperatively that can achieve great benefits including low cost, greater flexibility, adaptability to unknown environments, and robustness [1–4]. In the field of cooperative control, formation control has received a lot of attention from the researchers for its potential applications such as surveillance-and-security, object transportation, object manipulation, search-and-rescue, intelligent transportation systems, and exploration. The formation control means the problem of controlling the relative position and orientation of mobile robots in a group according to some desired pattern for executing a given task.

Various control approaches have been proposed in the literature for mobile robot formations, including leader-follower approach [5–10], behavior-based approach [11–13], virtual-structure approach [14–18], artificial potential approach [19–22], and graph theory [23, 24]. The main idea behind these approaches is to find suitable velocity control inputs to stabilize the closed-loop system. In the literature, formation control for multiple nonholonomic mobile robots, just simply consider the kinematic model by ignoring the robot dynamics. To design the control inputs to guarantee the stability of the closed-loop system, it is assumed that there is “perfect velocity tracking.” Reference [25] proposed an error-based tracking model and designed a stable kinematic tracking controller for the nonholonomic mobile robot. Reference [26] presents a kinematic controller based on the receding-horizon leader-follower (RH-LF) control framework to solve the formation problem of multiple nonholonomic mobile robots. Reference [27] studied the tracking control problem for nonholonomic mobile robots with limited information of a desired of trajectory. Reference [28] proposed a kinematic controller for the distributed consensus-based formation control. However, the perfect velocity tracking assumption does not hold in practice, and the dynamics of robot should not be ignored and practical control strategies accounting for both the kinematic and dynamic affect should be implemented [29–31]. In [32], the decentralized cooperative robust controllers are proposed for the formation control of a group of wheeled mobile robots with dynamics. In [31], an adaptive tracking controller for the dynamic model with unknown parameters was designed for a nonholonomic mobile robot by using an adaptive backstepping approach. Though these works consider the dynamics of the mobile robot, the dynamics of the mobile robot do not have the friction and bounded disturbance. It is well known that friction plays a central, controlling role in a rich variety of physical systems. Therefore, the friction term and bounded disturbance term should not be ignored and practical control strategies accounting for the friction term and bounded disturbance term should be implemented in practice.

Motivated by the above discussions, this paper investigates the distributed consensus-based robust adaptive formation control for nonholonomic mobile robots with partial known dynamics. The contribution of this paper is given as follows. Firstly, a variable transformation is given to convert the formation control problem into a state consensus problem. Then, the distributed consensus-based kinematic controllers are developed to make a group of robots asymptotically converge to a desired geometric pattern. In this paper, the specified reference trajectory for the geometric centroid of the formation is assumed as the trajectory of a virtual leader whose information is available to only a subset of the followers. Also the followers are assumed to have only local interaction with their neighbors. It is well known in practice that the perfect knowledge of dynamic model of the wheeled mobile robot is unattainable, and it is almost impossible to obtain exact values of the parameters of the mobile robot. Therefore, this paper considers that the dynamics of the mobile robot is partial known, in which there exist some unknown factors that will affect the robust trajectory tracking of the system. Then the corresponding robust adaptive torque controllers for mobile robots are developed for guaranteeing the robust velocity tracking, and the corresponding sufficient conditions are obtained for a group of nonholonomic mobile robots asymptotically converge to a desired geometric pattern with its centroid moving along the specified reference trajectory. The rigorous proofs are given by using graph theory, matrix theory, and Lyapunov theory. Finally, simulation examples illustrate the effectiveness of the proposed controllers. Compared with existing works in the literature, the current paper has the following advantages. Firstly, the relative distance and angular for each robot with its leader are not required to be known that is different from the traditional leader-follower approach [5, 6, 8, 9, 33]. Secondly, in contrast to that only kinematic control models considered in [26, 27, 34–36], the controllers designed in this paper are based on both the kinematic and dynamic models of robots. Moreover, the dynamics of wheeled mobile robots with possible uncertainty are considered. Thirdly, in contrast to that complete knowledge of the dynamics needed in [32, 37], only partial knowledge of the dynamics is needed. Fourthly, the control laws proposed in this paper are distributed. It is not necessary to know the global information for each robot. In fact, each robot can obtain information only from its neighbors.

The remainder of this paper is organized as follows. Section 2 introduces some preliminaries and gives the problem formulation. Section 3 and Section 4 present some new results on distributed formation control problem for multiple nonholonomic mobile robots. Simulations results are provided to verify the theoretical analysis in Section 5. Section 6 concludes this article.

2. Background

In this section, the model of nonholonomic wheeled mobile robot is first briefly presented. Then some notations for graph theory and nonsmooth analysis are introduced. Finally, the problem description is given.

2.1. Dynamics of Nonholonomic Wheeled Mobile Robot

Consider a multirobot system consisting of nonholonomic wheeled mobile robots indexed by . The nonholonomic mobile robot is shown in Figure 1. The kinematic model and dynamic model of the mobile robot can be described as follows [38]: where is the coordinates of the mobile robot , , , and are the position and orientation of the mobile robot. and are the linear velocity and angular velocity, respectively, and . is the Jacobian matrix, and . is a symmetric positive definite inertia matrix, is the bounded centripetal and coriolis matrix, denotes surface friction, is the gravitational vector, and denotes bounded unknown disturbances including unstructured unmodeled dynamics. is the input transformation matrix, is the control torque vector, is the matrix associated with the constraints, and is the vector of constraint forces.

The dynamic model (2) has the following properties [38].

Property 1. The inertia matrix is symmetric positive definite and satisfies the following inequality: where , are positive constants, and is the standard Euclidean norm.

Property 2. is skew symmetric; that is to say,

2.2. Graph Theory

The communication topology among robots is presented by a weighted graph with a vertex set , an edges set , and a weighted adjacency matrix . Here, each node in represents a robot , and each edge in a weighted undirected graph represents an information link from robot to robot , which means that the robots and can receive information from each other. The weighted adjacency matrix of a digraph is defined for any ; that is, self-edges are not allowed, if , otherwise, where is the weight of the link . Note that here , , since implies . We can say that is a neighbor vertex of , if . The neighbor set of node is defined as A path in the undirected graph is a sequence of edges in the form , where . We call an undirected graph connected if for any different nodes and in there exists an undirected path.

The Laplacian matrix associated with for graph is defined as for , and , . For an undirected graph, is symmetric positive semidefinite.

Lemma 1 (Chung [39]). Assume that is a weighted undirected graph with Laplacian matrix ; then is connected if and only if the matrix has an eigenvalue zero with multiplicity 1 and corresponding eigenvector 1, and all other eigenvalues are positive.

2.3. Nonsmooth Analysis

In what follows, some elements from nonsmooth analysis will be presented. Consider a vector differential equation with a discontinuous right-hand side as where is measurable and essentially locally bounded. The vector function is called a Filippov solution [40] of (6) if is absolutely continuous and satisfies almost everywhere where where denotes the set of measure zero that contains the set of points where is not differentiable and denotes the convex closure.

Lemma 2 (see [40]). The Filippov set-value map has the following useful properties.(1)Consistency: if is continuous at , then (2)Sum Rule: if function are locally bounded at , then Moreover, if either or is continuous at , then equality holds.

Lyapunov theorems have been extended to nonsmooth systems in [41]. The following chain rule provides a calculus for the time derivative of the energy function in the nonsmooth case.

Definition 3 (see [42]). Let be a locally Lipschitz continuous function. The generalized gradient of is given by where denotes the convex hull, is the set of Lebesgue measure zero, where does not exist, and is an arbitrary set of zero measure.

In this paper, the candidate Lyapunov function we use is smooth and hence regular, while its generalized gradient is a singleton which is equal to its usual gradient everywhere in the state space: .

Definition 4 (see [40]). Consider the vector differential equation (6), a set-valued map , the set-valued Lie derivative of with respect to (6) is defined as

In what follows, we introduce a Lyapunov stability theorem in terms of the set-valued map .

Lemma 5 (see [41]). For (6), let be locally essentially bounded and in a region , where . Also, let be a regular function satisfying in for some and belonging to class . If there exists a class function in such that the set-valued Lie derivative of satisfies then the solution is asymptotically stable.

2.4. Problem Formulation

In this paper, the desired geometric pattern of mobile robots is described by the orthogonal coordinates as follows: where denotes the center of . Without loss of generality, assume that , .

The objective of this paper is to design the control inputs and for the wheeled nonholonomic mobile robot using its states and as well as its neighbors' states and for , such that(a)the group of mobile robots converges to the desired formation ;(b)each robot in group converges to the desired orientation ;(c)the geometric centroid of the formation converges to the desired reference trajectory ;that is to say, where can be considered as the posture of a virtual leader , which does not have to be an actual robot but is specified by Hereafter, the robots in system (1) are called followers.

The connection weight between robot and the virtual leader is described by , in which if robot can obtain information from the virtual leader , otherwise. Note that if the undirected graph is connected, it then follows that the matrix and the matrix are symmetric positive definite.

In this paper, the following assumptions are needed for achieving our control objective.

Assumption 6. The for is bounded, for is persistently exciting, and .

Remark 7. is persistently exciting, which means that does not converge to 0. The assumption is because of the fact that the wheeled mobile robot system is nonholonomic.

Assumption 8. There exists at least one follower which can obtain information from the virtual leader.

Remark 9. Note from Assumption 8 that all follower robots do not need to obtain the information from the virtual leader; that is to say, the desired reference trajectory is not required to available for each robot, which is different from the existing works in [35, 43].

The following notations will be used throughout this paper. Let denote the identity matrix, denote the zero matrix, and ( for short, when there is no confusion). and are the smallest and the largest eigenvalues of the matrix , respectively.

3. Distributed Control Algorithm

To achieve the control objective (16)–(18), the following transformation is defined to convert the formation control problem for multiple nonholonomic mobile robots into a state consensus problem: where and are control inputs, , , and is the signum function. The definitions in (20) yield the following dynamic system as Then the control objective is changed to design and such that the following equations are satisfied:

Lemma 10. If (22)–(25) hold for , then the mobile robots can converge to the formation pattern ; that is, (16)–(18) can be satisfied.

Proof. Due to the fact that it is similar to the proof of Lemma 3.1 in [28], it is therefore omitted.

In practice, it is well known that the dynamics model of the wheel mobile robot may have unknown dynamical parameters and bounded unknown disturbances, which will affect the robust trajectory tracking of the system; that is to say, the “perfect velocity tracking" for robot may not hold. Hence, the following desired control inputs for the mobile robot are proposed in this paper as where , is a positive constant if the virtual leader's position is available to the follower , and otherwise, , is a positive constant, is a nonnegative constant, and is a positive constant and satisfies .

Define the auxiliary velocity tracking error as where and . Then the dynamic system (21) becomes in the following form:

Substitute (26) into the dynamic system (29) and (30). Then the closed-loop system (29) and (30) can be written as where and , , and . Let and . Then where the fact that has been applied. Let , , and . Hence, the error dynamic system (33) can be rewritten in a vector form as where .

4. Adaptive Dynamic Controller Design

4.1. Robot Model and Its Properties

According to (20) and the definition of in Section 2.2, it is easy to obtain that where . Then, it follows from (1) that we have where Hence, the dynamics (2) of the mobile robot can be rewritten as follows: that is, where is a symmetric positive definite inertia matrix. is the centripetal and coriolis matrix, is the gravitation vector, . is the surface friction, denotes the bounded unknown disturbances including unstructured unmodeled dynamics, and is the input vector.

Similar to the Properties 1 and 2 in Section 2.2, (39) has the following properties.

Property 3. The inertia matrix is symmetric positive definite.

Proof. It is easy to verify the result, and it is therefore omitted here.

Property 4. The matrix is skew symmetric.

Proof. The derivative of the inertia matrix and the centripetal and coriolis matrix are given by Since is skew symmetric and is symmetric positive definite, it follows that Hence, the matrix is skew symmetric.

4.2. Controller Design

Taking the derivative of (28) and multiplying by the inertia matrix to both sides of (28) give where is composed of known quantities and the disturbance term is with representing any model uncertainties and unmodeled dynamics and being the unknown bounded disturbance which could represent any inaccurately modeled dynamics.

Lemma 11 (bounds on the disturbance term, [30]). The disturbance term is bounded according to with depending on the terms like the disturbance bound, the changes in the mass of the robot due to payload, and friction coefficients with being a known regression vector.

When the robot dynamics are partially known, the torque control algorithm for the dynamics system (42) is designed to be where is a symmetric positive-definite matrix defined by with being a positive gain constant and being the identity matrix. The nonlinear term is an adaptive robustifying term and is defined as [44] where and are positive design constants, is the adaptive estimate of the known function , is the estimate of , and the parameter turning law for the estimate is defined as with being a symmetric and positive definite matrix. Let be the estimation error of the parameter turning law, and . It then follows that .

Substituting (45) into (42) and writing it in a vector form give where , , and are the block diagonal matrices of , , and , respectively, , with and under (43).

Theorem 12. Suppose that the communication graph is connected, Assumption 8 is satisfied, the velocity controllers for (29) and (30) are, respectively, designed by (26) and (27), and the torque control input for the dynamics system (42) is designed by (45), if the control gains are chosen as , , and , where ; then, for , the errors , , , and are globally asymptotically stable.

Proof. Choose the Lyapunov candidate as where and are chosen as with and being the block diagonal matrices of . Using the properties of , the set-valued Lie derivative of can be obtained as follows: Since is continuous, it follows from Lemma 2 that the equality (51) holds.
According to Definition 4, the set-valued Lie derivative of is given as where is the generalized gradient of at . Because is continuously differentiable with respect to , , which is a singleton. Therefore, it follows that where the fact that has been used. By Lemma 2 and [42], if is continuous, then . Note that the set-valued Lie derivative is a singleton, whose only element is actually . Therefore, it follows that where and is positive. It is easy to verify that is symmetric positive definite.
Since is continuous, it follows that the set-valued Lie derivative of satisfies . Hence, we have Since the matrix is skew symmetric, we have Let , . Substituting the robustifying term (46) and the disturbance (43) into gives Hence, it can be obtained that Substituting (58) into (55) gives the following inequality:
Now, substituting (54) and (59) into the set-valued Lie derivative reveals Therefore, as , , and . It then follows from Lemma 5 that and as ; that is, , , , and as . Therefore, the errors , , , and are globally asymptotically stable. This proof is completed.

Remark 13. From Theorem 12, we have proved that the variables and , respectively, converge to and globally asymptotically under the proposed control laws (26), (27), and (45). In Theorem 14, we will prove that asymptotically converges to under the control laws (26), (27), and (45).

Theorem 14. Suppose that the communication graph is connected, Assumption 8 is satisfied, the velocity controllers for (29) and (30) are, respectively, designed by (26) and (27), and the torque control input for the dynamics system (42) is designed by (45). If and asymptotically converge to and , then also asymptotically converges to .

Proof. Let . Take the derivative of as where . The solution of the differential equation (61) is given as follows:
According to Theorem 12, asymptotically converges to zero, and asymptotically converges to . It then follows from the definition of that also asymptotically converges to zero. Hence, according to the definition of asymptotic stable, for an arbitrary positive value , exists; when the , it has .
From Assumption 6, the is bounded, and , Hence, .
The solution of the differential equation (62) satisfies the inequality Hence, when , . Since is an arbitrary positive value, from the definition of asymptotic stable, the is asymptotic stable at the neighborhood of origin. This proof is completed.

Remark 15. From Theorems 12 and 14, our control objectives (22)–(25) hold under the distributed kinematic controller (26) and the torque controller (45). Therefore, from Lemma 10, the mobile robots converge to the formation pattern ; that is, (16)–(18) are satisfied.

5. Simulation

In this section, some simulations results will be provided to demonstrate the effectiveness of some theoretical results of the previous sections. Consider a multiple mobile robot system with six followers denoted by and one virtual leader denoted by , respectively. The communication graph of the multiple mobile robot system is shown in Figure 2.

For simplicity, in this simulation we suppose that if robot can receive information from robot , otherwise; if the virtual leader's information is available to the follower , and otherwise, where and .

The desired formation geometric pattern is defined by orthogonal coordinates as , , , , , and . The reference trajectory of the virtual leader is chosen as

The control gain parameters are chosen as , , . For , , , , . The parameters for each robot are considered as the mass kg and the moment of inertia kg·m². The unmodeled dynamics are introduced in the form of friction as The disturbance is introduced as .

5.1. Verification of Formation Control Based on Robust Adaptive Techniques

In this simulation, Figure 3 shows the trajectory of virtual leader (black line), the trajectory of the six followers' centroid (blue line), and the formation positions and pattern of the six followers at several moments. We could see from Figure 3 that the six robots converge to the desired geometry pattern under the proposed controllers (27), (26), and (45); that is to say, (16) has been verified.

Figure 4(a) shows the trajectories of (blue line) and the centroid of () (red line), and Figure 4(b) shows the position error between and the centroid of . Figure 5(a) shows the trajectories of (blue line) and the centroid of () (red line), and Figure 5(b) shows the position error between and the centroid of . We could see, from Figures 4 and 5, the trajectory of the formation geometric centroid converges to the trajectory of virtual leader; that is to say, (18) has been verified. Figure 6 shows the tracking error for and for under the torque controller (45). From Figure 6, and , respectively, converge to zeros. The perfect tracking of velocity and angular velocity has been guaranteed. Figure 7, respectively, shows the angular velocity tracking errors and the orientation tracking errors between follower () and virtual leader. It can be seen from Figure 7 that and converge to zero over time; that is, (17) and (25) have been verified.

(a)

(b)

(a)

(b)

(a)

(b)

(a)

(b)

6. Conclusion

In this paper, the distributed consensus-based robust adaptive formation control problem for nonholonomic mobile robots with partial known dynamics has been investigated, in which the dynamics model of the wheeled mobile robot has the friction term and bounded disturbance term in the dynamic model. The partial knowledge of the mobile robot dynamics has been assumed to be available. Then an asymptotically stable torque controller has been proposed by using robust adaptive control techniques to account for unmolded dynamics and bounded disturbances.

As further extensions of this study, there still exist a number of topics for future works. In practice, formations have to avoid obstacles and need to admit changes in the formation speed and strong deformations in formation shape. The obstacles avoidance problem for formation control of nonholonomic mobile robots will be considered. In addition, the formation control is assumed to be noiseless in this paper. However, it is inevitable in reality. Hence, in the future it is necessary to investigate the formation control problem with measurement noise. Finally, it is well known that most operations in mobile robots systems are naturally delayed. Moreover, it has been observed from numerical experiments that formation control algorithms without considering time delays may lead to unexpected instability. Hence, in future, we may consider the formation control with time-varying delays.

Conflict of Interests

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

Acknowledgments

The authors would like to thank the associate editor and the anonymous reviewers for their valuable comments and suggestions that have improved the presentation of this paper. This work was supported by the National High Technology Research and Development Program of China (863 Program) under Grants 2012AA111104, 2012AA111718, and 2012AA111202.

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Copyright © 2014 Zhaoxia Peng 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.

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