# Distributed Consensus Algorithm for Nonholonomic Wheeled Mobile Robot

Authors: Xianyang Meng; Ping He; Xingzhong Xiong; Haoyang Mi; Zuxin Li; Zhigang Wu
Journal: Security and Communication Networks
 (2021)

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## Abstract

This paper focuses on consensus of the nonholonomic wheeled mobile robotic systems whose geometric center and centroid do not coincide. A consensus control algorithm for mobile robots based on the nonstandard chain systems is proposed. Firstly, coordinate transformation is used to transform the nonholonomic robotic systems into the nonstandard chain model. Then, a distributed cooperative control algorithm is designed, and the Lyapunov stability theorem and LaSalle invariance principle are used to prove that each state of the mobile robot is consensus. Finally, the effectiveness of the algorithm is proved through numerical simulation.


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## Body

## 1. Introduction

With the coming of information times, there has been great interest in cooperative control of nonholonomic systems, including collective behavior and synchronization phenomenon [1]. The consensus problem is one of the significant control problem in cooperative control [2]. In contrast, most of the systems in reality have nonholonomic constraints, such as wheeled mobile robot [3] and tractor trailer systems [4]. Since the Brockett stability condition [5] is not satisfied, such systems cannot become asymptotically stable by the directly continuous differentiable and time-invariant state feedback control law [6]. Therefore, it is very challenging to achieve the consensus of nonholonomic systems.Aiming at the consensus problem of nonholonomic systems, many effective methods are proposed [7–10]. A time-varying cooperative control law was applied to the third-order chain system so that the state of the system converges to the moving point [8]. In addition, by using appropriate coordinate transformation, the homotopy-based method was proposed to solve the problem of matrix inequality [7, 9]. Two kinds of time-invariant continuous state feedback controllers were designed to achieve the consensus of nonholonomic systems [10]. As can be seen from the above, the consensus problem does not require that all agents converge to the same equilibrium point, but to the same variable vector [11]. Moreover, the interconnection graph is used to describe the information flow between agents, and the graph Laplacian is used as the feedback gain. This paper extends the above approach to achieve consensus of nonholonomic systems. Meanwhile, Brockett’s stability condition should not be an obstacle to the realization of the consensus problem [5].Motivated by the consensus of the nonholonomic chain system without pilot [10], this paper designed a decentralized controller, which uses a time-varying and continuous control scheme to achieve consensus of the nonstandard chain system. Then, combined with undirected communication topology, the stability is proved by the Lyapunov stability theorem and LaSalle invariance principle. Finally, Matlab numerical simulation is carried out to prove the effectiveness of the algorithm.The main contributions of this paper are summarized in the following two aspects:(i)
In practice, the geometric center and centroid of most robots do not coincide, which means that this kind of system cannot be transformed into a chain model. Therefore, it is difficult to achieve the consensus of this kind of nonholonomic systems.(ii)
A distributed algorithm is designed for a class of nonholonomic wheeled mobile robot which cannot be convert into the chain system to achieve the consensus.The rest of the paper is organized as follows. Section2 gives the preparatory knowledge, including graph and graph Laplacian, consensus via graph Laplacian and Kronecker product. A distributed cooperative control algorithm is designed for nonstandard chain systems, and the Lyapunov stability theorem and LaSalle invariance principle are used to prove that the state of the mobile robot is consensus in Section 3. In order to verify the effectiveness of the algorithm, the simulation results are obtained through Matlab in Section 4. Conclusions are presented in Section 5.


## 2. Preparatory Knowledge

### 2.1. Graph and Graph Laplacian

Algebraic graph theory is a major tool to study the communication between multiple nonlinear systems, which is widely used in consensus of the multiagent system [9]. Graph G=V,E with the set of node V=1,2,…,N and edges E⊂V×V. The edge i,j∈E means that the information of the jth agent is available for the ith agent. When j,i∈E and i,j∈E, then graph G is called the undirected graph.The adjacency matrixA of graph G is defined as(1)aii=0,aij=1,ifj,i∈E,0,otherwise.Ifaij of adjacency matrix A is the nonnegative real number, then the adjacency matrix A is called as the weighted adjacency matrix, and the corresponding graph is the weighted graph [12]. The Laplacian matrix of graph G is defined as(2)lij=−aij,i≠j,∑k=1,k≠iNaik,i=j.


### 2.2. Consensus via Graph Laplacian

The algorithms based on graph Laplacian are considered, and take the first-order integrator model as an example:(3)x˙i=ui,where xi is the state variable and ui is the control input. The control inputs [13] were proposed as(4)ui=∑j=1Naijxj−xi,i∈N.Then, the matrix form of multiagents withN is(5)X˙=−L⊗INX.The basic properties of the Laplacian matrix are as follows:(1)
Laplacian matrix is a positive semidefinite matrix(2)
The minimum eigenvalue is zero because the sum of every row of the Laplacian matrix is zero


### 2.3. Kronecker Product

Kronecker product is an operation between two matrices of arbitrary size. It is defined as follows.IfA=aijmxn and B=bijpxq, then the following block matrix(6)A⊗B=a11Ba21B⋅⋅⋅am1Ba12Ba22B⋅⋅⋅am2B⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅a1nBa2nB⋅⋅⋅amnB.It is called Kronecker product, which is defined asA⊗B=aijB.The basic properties of Kronecker product are as follows:(1)
IfA is positive semidefinite matrix and B is positive definite matrix, then A⊗B is positive semidefinite matrix(2)
IfA, B, C, and D are four matrices and AC and BD exist, then A⊗BC⊗D=AC⊗BD(3)
IfA=aijmxn and B=bijpxq, then the transpose operation conforms to the distributive law A⊗BT=AT⊗BT


## 2.1. Graph and Graph Laplacian

Algebraic graph theory is a major tool to study the communication between multiple nonlinear systems, which is widely used in consensus of the multiagent system [9]. Graph G=V,E with the set of node V=1,2,…,N and edges E⊂V×V. The edge i,j∈E means that the information of the jth agent is available for the ith agent. When j,i∈E and i,j∈E, then graph G is called the undirected graph.The adjacency matrixA of graph G is defined as(1)aii=0,aij=1,ifj,i∈E,0,otherwise.Ifaij of adjacency matrix A is the nonnegative real number, then the adjacency matrix A is called as the weighted adjacency matrix, and the corresponding graph is the weighted graph [12]. The Laplacian matrix of graph G is defined as(2)lij=−aij,i≠j,∑k=1,k≠iNaik,i=j.


## 2.2. Consensus via Graph Laplacian

The algorithms based on graph Laplacian are considered, and take the first-order integrator model as an example:(3)x˙i=ui,where xi is the state variable and ui is the control input. The control inputs [13] were proposed as(4)ui=∑j=1Naijxj−xi,i∈N.Then, the matrix form of multiagents withN is(5)X˙=−L⊗INX.The basic properties of the Laplacian matrix are as follows:(1)
Laplacian matrix is a positive semidefinite matrix(2)
The minimum eigenvalue is zero because the sum of every row of the Laplacian matrix is zero


## 2.3. Kronecker Product

Kronecker product is an operation between two matrices of arbitrary size. It is defined as follows.IfA=aijmxn and B=bijpxq, then the following block matrix(6)A⊗B=a11Ba21B⋅⋅⋅am1Ba12Ba22B⋅⋅⋅am2B⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅a1nBa2nB⋅⋅⋅amnB.It is called Kronecker product, which is defined asA⊗B=aijB.The basic properties of Kronecker product are as follows:(1)
IfA is positive semidefinite matrix and B is positive definite matrix, then A⊗B is positive semidefinite matrix(2)
IfA, B, C, and D are four matrices and AC and BD exist, then A⊗BC⊗D=AC⊗BD(3)
IfA=aijmxn and B=bijpxq, then the transpose operation conforms to the distributive law A⊗BT=AT⊗BT


## 3. Problem Description

### 3.1. Nonholonomic Wheeled Mobile Robot

Consider a group ofN robots, whose centroid and geometric center do not coincide. The geometric model of mobile robots is shown in Figure 1.Figure 1
Structure diagram of the nonholonomic wheeled mobile robot.In Figure1, C is centroid of two driving wheels, M is the geometric center of the body of the robot, d is the distance point C and M, θi is the angle between the robot’s motion direction and X-axis, and xc,yc is the coordinate of the geometric center C of the mobile robot in the XOY coordinate system.Under the condition of pure rolling without slipping, we get to meet the following nonholonomic constraints of the wheeled mobile robot:(7)x˙csinθ−y˙ccosθ−dθ˙=0.The equation (7) can be rewritten as follows:(8)Aqq˙=0,where Aq=sinθ,−cosθ,−d is the nonholonomic constraint matrix and q˙=x˙c,y˙c,θ˙T.LetSq spans the null space of Aq and a full rank matrix formed by a set of smooth and linearly independent vector fields(9)AqSq=0.In this case from (8), the matrix Sq of (9) is(10)Sq=cosθdsinθsinθ−dcosθ01.Define(11)Mt=vcωc,where vc and ωc are linear velocity and angular velocity of the center of geometric, respectively.The kinematics of theith robot (i∈N) can be obtained:(12)Qi=SqiMi,where(13)Qi=x˙iy˙iθ˙i,Mi=viωi.And,(14)Sqi=cosθidsinθisinθi−dcosθi01,where xi and yi are the coordinates of the ith mobile robot in Cartesian coordinates.vi and ωi are the linear and angular velocity of the ith robot, respectively.It can be obtained thatafter the following two transformations [14],(15)zi1=θi,zi2=xicosθi+yisinθi,zi3=xisinθi−yicosθi.(16)ui1=ωi,ui2=vi−ωizi3.It is easy that the above transformation is regular.Substitute (16) into the derivative of (15), we can obtain(17)z˙i1=θ˙i=ωi=ui1,z˙i2=x˙icosθi−xisinθiθ˙i+y˙isinθi+yicosθiθ˙i=vicosθi+dωisinθicosθi−xisinθiωi+visinθi−dωicosθisinθi+yicosθiωi=ui2,z˙i3=x˙isinθi+xicosθiθ˙i−y˙icosθi+yisinθiθ˙i=vicosθi+dωisinθisinθi+xicosθiωi−visinθi−dωicosθicosθi+yisinθiωi=zi2ui1+dui1.The following nonstandard chain model is sorted out:(18)z˙i1=ui1,z˙i2=ui2,z˙i3=zi2ui1+dui1,where Zi=zi1,zi2,zi3T is the state of the system. Ui=ui1,ui2T is the control input. Therefore, the above system can be simplified to the following matrix form:(19)Z˙=RU,where(20)Z=Z1T,Z2T,…,ZNTT,R=diagR1,R2,…,RN,U=U1T,U2T,…,UNTT,Ri=10zi2+d010.


### 3.2. Control Objectives

Assumption 1.
It is assumed that the topological structure diagram composed of multirobots is connected and there is no isolated system.Assumption 2.
It is assumed that multirobot networks communicate with each other and communication topology is undirected. Therefore, its Laplacian matrix is positive semidefinite and symmetric.Remark 1.
Assumption1 is the premise of the consensus problem. Assumption 2 is the premise of the controller design in this paper. The consensus problem of all states of the system was considered.
To sum up, each agent can only obtain information from its neighboring agents and itself [15]. The objective of this paper is to design a time-invariant continuous state feedback law Ui=FiZi,Zj,j∈Ni, for(21)limt⟶∞Zi−Zj=0,∀i≠j.


### 3.3. Controller Design

Under the conditions of Assumptions1 and 2, the following control inputs are designed:(22)ui1=−ki1g1∑j=1Naijzi1−zj1+g3zi2+d∑j=1Naijzi2−zj2+∑j=1Naijzi3−zj3,ui2=−ki2g2∑j=1Naijzi2−zj2+g3∑j=1Naijzi2−zj2+∑j=1Naijzi3−zj3.The above equation is equivalent to(23)ui1ui2=−ki1g10−ki1g3zi2+d0−ki2g2−ki2g3∑j=1Naijzi1−zj1∑j=1Naijzi2−zj2∑j=1Naijzi2−zj2+∑j=1Naijzi3−zj3=−ki100ki2g10g3zi2+d0g2g3100010011∑j=1Naijzi1−zj1∑j=1Naijzi2−zj2∑j=1Naijzi3−zj3=−ki100ki210zi2+d011g1000g2000g3100010011∑j=1Naijzi1−zj1∑j=1Naijzi2−zj2∑j=1Naijzi3−zj3=−ki100ki210zi2+d010100011001g1000g2000g3100010011∑j=1Naijzi1−zj1∑j=1Naijzi2−zj2∑j=1Naijzi3−zj3.It is can be simplified to the following matrix form:(24)U=−KRTIN⊗ATGAL⊗INZ=−KRTL⊗ATGAZ,where K=diagk11,k12,k21,…,kN1,kN2, U=diagu11,u12,u21,…,uN1,uN2, G=diagg1,g2,g3, and gi,i=1,2,3, is a positive number, and(25)A=100010011.In addition, we can obtain(26)ATGA=g1000g2g20g2g2+g3,where the value of first-order principal minor of matrix ATGA is g1>0. The value of second order of principal minor is g1g2>0. The determinant of the matrix is g1g2g3>0. So, ATGA is positive definite symmetric matrix and symmetric matrix.Substituting (24) into (19), we can obtain the whole closed-loop system:(27)Z˙=−RKRTL⊗ATGAZ.


## 3.1. Nonholonomic Wheeled Mobile Robot

Consider a group ofN robots, whose centroid and geometric center do not coincide. The geometric model of mobile robots is shown in Figure 1.Figure 1
Structure diagram of the nonholonomic wheeled mobile robot.In Figure1, C is centroid of two driving wheels, M is the geometric center of the body of the robot, d is the distance point C and M, θi is the angle between the robot’s motion direction and X-axis, and xc,yc is the coordinate of the geometric center C of the mobile robot in the XOY coordinate system.Under the condition of pure rolling without slipping, we get to meet the following nonholonomic constraints of the wheeled mobile robot:(7)x˙csinθ−y˙ccosθ−dθ˙=0.The equation (7) can be rewritten as follows:(8)Aqq˙=0,where Aq=sinθ,−cosθ,−d is the nonholonomic constraint matrix and q˙=x˙c,y˙c,θ˙T.LetSq spans the null space of Aq and a full rank matrix formed by a set of smooth and linearly independent vector fields(9)AqSq=0.In this case from (8), the matrix Sq of (9) is(10)Sq=cosθdsinθsinθ−dcosθ01.Define(11)Mt=vcωc,where vc and ωc are linear velocity and angular velocity of the center of geometric, respectively.The kinematics of theith robot (i∈N) can be obtained:(12)Qi=SqiMi,where(13)Qi=x˙iy˙iθ˙i,Mi=viωi.And,(14)Sqi=cosθidsinθisinθi−dcosθi01,where xi and yi are the coordinates of the ith mobile robot in Cartesian coordinates.vi and ωi are the linear and angular velocity of the ith robot, respectively.It can be obtained thatafter the following two transformations [14],(15)zi1=θi,zi2=xicosθi+yisinθi,zi3=xisinθi−yicosθi.(16)ui1=ωi,ui2=vi−ωizi3.It is easy that the above transformation is regular.Substitute (16) into the derivative of (15), we can obtain(17)z˙i1=θ˙i=ωi=ui1,z˙i2=x˙icosθi−xisinθiθ˙i+y˙isinθi+yicosθiθ˙i=vicosθi+dωisinθicosθi−xisinθiωi+visinθi−dωicosθisinθi+yicosθiωi=ui2,z˙i3=x˙isinθi+xicosθiθ˙i−y˙icosθi+yisinθiθ˙i=vicosθi+dωisinθisinθi+xicosθiωi−visinθi−dωicosθicosθi+yisinθiωi=zi2ui1+dui1.The following nonstandard chain model is sorted out:(18)z˙i1=ui1,z˙i2=ui2,z˙i3=zi2ui1+dui1,where Zi=zi1,zi2,zi3T is the state of the system. Ui=ui1,ui2T is the control input. Therefore, the above system can be simplified to the following matrix form:(19)Z˙=RU,where(20)Z=Z1T,Z2T,…,ZNTT,R=diagR1,R2,…,RN,U=U1T,U2T,…,UNTT,Ri=10zi2+d010.


## 3.2. Control Objectives

Assumption 1.
It is assumed that the topological structure diagram composed of multirobots is connected and there is no isolated system.Assumption 2.
It is assumed that multirobot networks communicate with each other and communication topology is undirected. Therefore, its Laplacian matrix is positive semidefinite and symmetric.Remark 1.
Assumption1 is the premise of the consensus problem. Assumption 2 is the premise of the controller design in this paper. The consensus problem of all states of the system was considered.
To sum up, each agent can only obtain information from its neighboring agents and itself [15]. The objective of this paper is to design a time-invariant continuous state feedback law Ui=FiZi,Zj,j∈Ni, for(21)limt⟶∞Zi−Zj=0,∀i≠j.


## 3.3. Controller Design

Under the conditions of Assumptions1 and 2, the following control inputs are designed:(22)ui1=−ki1g1∑j=1Naijzi1−zj1+g3zi2+d∑j=1Naijzi2−zj2+∑j=1Naijzi3−zj3,ui2=−ki2g2∑j=1Naijzi2−zj2+g3∑j=1Naijzi2−zj2+∑j=1Naijzi3−zj3.The above equation is equivalent to(23)ui1ui2=−ki1g10−ki1g3zi2+d0−ki2g2−ki2g3∑j=1Naijzi1−zj1∑j=1Naijzi2−zj2∑j=1Naijzi2−zj2+∑j=1Naijzi3−zj3=−ki100ki2g10g3zi2+d0g2g3100010011∑j=1Naijzi1−zj1∑j=1Naijzi2−zj2∑j=1Naijzi3−zj3=−ki100ki210zi2+d011g1000g2000g3100010011∑j=1Naijzi1−zj1∑j=1Naijzi2−zj2∑j=1Naijzi3−zj3=−ki100ki210zi2+d010100011001g1000g2000g3100010011∑j=1Naijzi1−zj1∑j=1Naijzi2−zj2∑j=1Naijzi3−zj3.It is can be simplified to the following matrix form:(24)U=−KRTIN⊗ATGAL⊗INZ=−KRTL⊗ATGAZ,where K=diagk11,k12,k21,…,kN1,kN2, U=diagu11,u12,u21,…,uN1,uN2, G=diagg1,g2,g3, and gi,i=1,2,3, is a positive number, and(25)A=100010011.In addition, we can obtain(26)ATGA=g1000g2g20g2g2+g3,where the value of first-order principal minor of matrix ATGA is g1>0. The value of second order of principal minor is g1g2>0. The determinant of the matrix is g1g2g3>0. So, ATGA is positive definite symmetric matrix and symmetric matrix.Substituting (24) into (19), we can obtain the whole closed-loop system:(27)Z˙=−RKRTL⊗ATGAZ.


## 4. Proof of Consensus

Theorem 1.
Decentralized algorithm (22) are used to achieve consensus of system (19). In other words, closed-loop system (27) is globally asymptotically stable.Proof:.
To begin with, we choose a candidate Lyapunov function:(28)V=12ZTL⊗ATGAZ,where L is the symmetric matrix from Assumption 2 and ATGA is also the symmetric matrix, so L⊗ATGA is the symmetric matrix. According to basic properties (1) of the Laplacian matrix, we get L≥0. In addition, ATGA>0, so L⊗ATGA≥0 from basic properties (1) of Kronecker product. Therefore, V is the positive matrix.
Substituting (27) into the derivative of (28), we can obtain(29)V˙=12Z˙TL⊗ATGAZ+ZTL⊗ATGAZ˙=−ZTL⊗ATGARKRTL⊗ATGAZ≤0.
Therefore,V˙ is always nonpositive.
SupposeV˙=0 is true at a certain time, and we can get it from (29):(30)RTL⊗ATGAZ=0.
The above equation expands to(31)0=g1∑j=1Naijzi1−zj1+g3zi2+d∑j=1Naijzi2−zj2+∑j=1Naijzi3−zj3,(32)0=g2∑j=1Naijzi2−zj2+g3∑j=1Naijzi2−zj2+∑j=1Naijzi3−zj3.
Combining (31) and (32), we can obtain(33)g2zi2+d∑j=1Naijzi2−zj2=g1∑j=1Naijzi1−zj1.
By accumulating up alli of the two sides of (33) and then according to basic properties (2) of the Laplacian matrix, we can get(34)g2∑i=1Nzi2+d∑j=1Naijzi2−zj2=0,which is equivalent to(35)g2∑j=1Nzj2+d∑i=1Naijzj2−zi2=0.
Adding (34) and (35), then(36)∑i=1N∑j=1Naijzi2−zj22=0.
Thus,z12=z22=⋯=zN2 holds when V˙=0.
Furthermore, it can be seen from the above conclusion that the left side of (33) is zero. Therefore, it can be obtained from the right side of the following equation:(37)∑j=1Naijzi1−zj1=0,which is equivalent to(38)∑i=1Nzi1∑j=1Naijzi1−zj1=0.
Furthermore,(39)∑j=1Nzj1∑i=1Naijzj1−zi1=0.
Adding (38) and (39), then(40)∑i=1N∑j=1Naijzi1−zj12=0.
Therefore, we prove thatz11=z21=⋯=zN1. Similarly, by sorting out (32), we obtain z13=z23=⋯=zN3.
In summary, according to the LaSalle invariance principle, whenV˙=0, the consensus of nonholonomic systems is achieved.


### 4.1. Numerical Simulation

In order to verify the effectiveness of the proposed scheme, the wheeled mobile robot (Figure1) is selected as the controlled object in the simulation model.Coordinate transformation is carried out by using (15) and (16) for the nonholonomic mobile robot, and the nonstandard chain model is obtained. The actual control input linear velocity and angular velocity can be expressed as(41)vi=ui2+ωizi3=−ki2g2∑j=1Naijzi2−zj2+g3∑j=1Naijzi2−zj2+∑j=1Naijzi3−zj3−ki1xisinθi−yicosθi⋅g1∑j=1Naijzi1−zj1+g3zi2+d∑j=1Naijzi2−zj2+∑j=1Naijzi3−zj3.(42)ωi=ui1=−ki1g1∑j=1Naijzi1−zj1+g3zi2+d∑j=1Naijzi2−zj2+∑j=1Naijzi3−zj3.Suppose that the communication topology of three wheeled mobile robots is shown in Figure2.Figure 2
Communication graph.Its Laplacian matrix is(43)L=2−1−1−12−1−1−12.Meanwhile, the initial states of robots are(44)x1,y1,θ1=3,5,−3π5,x2,y2,θ2=−3,−4,5π9,x3,y3,θ3=2,2,−π3.The corresponding initial value of the nonstandard chain model is(45)z11,z12,z13=−1.88,2.83,−5.09,z21,z22,z23=1.75,−3.15,3.91,z31,z32,z33=−1.05,1.96,−2.04.Condition 1.
Parameterd=0.
Whend=0, then (18) becomes a third-order chain system [10]:(46)z˙i1=ui1,z˙i2=ui2,z˙i3=zi2ui1.
The controller parameters areG=diag5,2,6 and ki=35,i∈1,2,3,4,5,6. Three states of the system can be obtained by numerical simulation. Figure 3 shows the trajectory of the first state variable of the chain system. Figure 4 shows the trajectory of the second state variable of the chain system. Figure 5 shows the trajectory of the third state variable of the chain system.
It can be seen from Figures3–5 that the three states of the mobile robot under the third-order chain system can achieve consensus.Figure 3
Trajectory ofzi1 in the chain system.Figure 4
Trajectory ofzi2 in the chain system.Figure 5
Trajectory ofzi3 in the chain system.Condition 2.
Parameterd=10.
Similarly, in order to verify the correctness of the proposed scheme, we choose the larger structure parameter of the robot system. The controller parametersd=10, ki=65,i∈1,2,3,4,5,6, and G=diag5,2,9. Through the numerical simulation of the proposed control strategy, three states of the system can be obtained. Figure 6 shows the trajectory of the first state variable after the transformation of three robots. Figure 7 shows the trajectory of the second state variable after the transformation of three robots. Figure 8 shows the trajectory of the third state variable after the transformation of three robots.
As can be seen from Figures6–8, the first state variable can achieve consensus in about 10 seconds. The second state variable can achieve consensus in about 13 seconds. The third state variable can achieve consensus in about 2 seconds. Although the convergence rates of the three states are different, they all have good convergence performance. The convergence rate of three state variables can also be achieved by adjusting the corresponding control parameters. In conclusion, the control scheme mentioned above is effective.Figure 6
Trajectory ofzi1.Figure 7
Trajectory ofzi2.Figure 8
Trajectory ofzi3.


## 4.1. Numerical Simulation

In order to verify the effectiveness of the proposed scheme, the wheeled mobile robot (Figure1) is selected as the controlled object in the simulation model.Coordinate transformation is carried out by using (15) and (16) for the nonholonomic mobile robot, and the nonstandard chain model is obtained. The actual control input linear velocity and angular velocity can be expressed as(41)vi=ui2+ωizi3=−ki2g2∑j=1Naijzi2−zj2+g3∑j=1Naijzi2−zj2+∑j=1Naijzi3−zj3−ki1xisinθi−yicosθi⋅g1∑j=1Naijzi1−zj1+g3zi2+d∑j=1Naijzi2−zj2+∑j=1Naijzi3−zj3.(42)ωi=ui1=−ki1g1∑j=1Naijzi1−zj1+g3zi2+d∑j=1Naijzi2−zj2+∑j=1Naijzi3−zj3.Suppose that the communication topology of three wheeled mobile robots is shown in Figure2.Figure 2
Communication graph.Its Laplacian matrix is(43)L=2−1−1−12−1−1−12.Meanwhile, the initial states of robots are(44)x1,y1,θ1=3,5,−3π5,x2,y2,θ2=−3,−4,5π9,x3,y3,θ3=2,2,−π3.The corresponding initial value of the nonstandard chain model is(45)z11,z12,z13=−1.88,2.83,−5.09,z21,z22,z23=1.75,−3.15,3.91,z31,z32,z33=−1.05,1.96,−2.04.Condition 1.
Parameterd=0.
Whend=0, then (18) becomes a third-order chain system [10]:(46)z˙i1=ui1,z˙i2=ui2,z˙i3=zi2ui1.
The controller parameters areG=diag5,2,6 and ki=35,i∈1,2,3,4,5,6. Three states of the system can be obtained by numerical simulation. Figure 3 shows the trajectory of the first state variable of the chain system. Figure 4 shows the trajectory of the second state variable of the chain system. Figure 5 shows the trajectory of the third state variable of the chain system.
It can be seen from Figures3–5 that the three states of the mobile robot under the third-order chain system can achieve consensus.Figure 3
Trajectory ofzi1 in the chain system.Figure 4
Trajectory ofzi2 in the chain system.Figure 5
Trajectory ofzi3 in the chain system.Condition 2.
Parameterd=10.
Similarly, in order to verify the correctness of the proposed scheme, we choose the larger structure parameter of the robot system. The controller parametersd=10, ki=65,i∈1,2,3,4,5,6, and G=diag5,2,9. Through the numerical simulation of the proposed control strategy, three states of the system can be obtained. Figure 6 shows the trajectory of the first state variable after the transformation of three robots. Figure 7 shows the trajectory of the second state variable after the transformation of three robots. Figure 8 shows the trajectory of the third state variable after the transformation of three robots.
As can be seen from Figures6–8, the first state variable can achieve consensus in about 10 seconds. The second state variable can achieve consensus in about 13 seconds. The third state variable can achieve consensus in about 2 seconds. Although the convergence rates of the three states are different, they all have good convergence performance. The convergence rate of three state variables can also be achieved by adjusting the corresponding control parameters. In conclusion, the control scheme mentioned above is effective.Figure 6
Trajectory ofzi1.Figure 7
Trajectory ofzi2.Figure 8
Trajectory ofzi3.


## 5. Conclusion

In this paper, the consensus of nonholonomic wheeled mobile robotic systems whose geometric center and centroid do not coincide was studied. The time-invariant continuous state feedback control law is proposed by changing the structure of the nonholonomic systems. Compared with the existing chain form, the nonstandard chain form is more in line with the actual motion state of the robot. At the same time, the controllers can set free parameters to adjust when dealing with more complex control requirement.In the future, the relationship between the consensus value of state and initial states of all agents will be studied. In addition to this, the consensus of nonholonomic systems, such as communication delay and switching topology [16] are further analyzed.



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*Source: 7617819-2021-09-29.xml*