# Optimal Leader–Follower Control for the Fractional Opinion Formation Model

- 140 Downloads

## Abstract

This paper deals with an opinion formation model, that obeys a nonlinear system of fractional-order differential equations. We introduce a virtual leader in order to attain a consensus. Sufficient conditions are established to ensure that the opinions of all agents globally asymptotically approach the opinion of the leader. We also address the problem of designing optimal control strategies for the leader so that the followers tend to consensus in the most efficient way. A variational integrator scheme is applied to solve the leader–follower optimal control problem. Finally, in order to verify the theoretical analysis, several particular examples are presented.

## Keywords

Opinion formation models Consensus problem Fractional derivatives Optimal control Variational integrators## Mathematics Subject Classification

49K99 49M25 26A33 39A99## 1 Introduction

The process of opinion formation in a social network (a network of social actors connected by social ties) is rapidly attracting the attention of scholars in many disciplines, ranging from sociology to mathematics [1, 2, 3, 4, 5, 6]. There are two different approaches in mathematical modeling of social dynamics. The first is macroscopic and applies tools from continuum mechanics and partial differential equations. The other is microscopic and treats social actors (agents, individuals) in the group as separate objects interacting with each other. In this work, we use the second approach and consider an agent-based model with real-valued opinions. Namely, our model consists in a nonlinear system of fractional-order differential equations. Fractional derivatives are non-local operators [7] and therefore are proper for modeling systems with long range interactions in space and/or time (memory), and processes with many scales of space and/or time involved [8, 9, 10]. We thus argue that fractional-order systems can better describe memory and hereditary properties of the process of opinion formation than integer-order ones.

One of the first agent-based models of opinion formation was the French–DeGroot model, proposed in 1974 [11]. Since then, the most important characteristic of such models has been the emergence of a consensus, where a group of agents agree upon certain quantities of interest such as position, price, etc. Roughly speaking, the consensus problem of agent-based models can be treated as a special case of the asymptotic stability problem of dynamical systems. Although consensus is a behavior, that we would expect in opinion formation models, there are situations when the opinions do not reach consensus and we observe polarization of opinions or chaos [12, 13, 14]. In such situations, one possible way to steer all agents to reach a consensus consists in introducing a (virtual) leader to the system and possibly controlling the leader [15, 16, 17, 18]. The (virtual) leader is a special agent, whose opinion is independent of all the other agents’ opinions. This approach has roots in real-world phenomena such as the relations between a sheepdog and sheep [19], or the influence of mass media on opinions of members of society. In addition, controlling the system through the leader is justified in practice, e.g., crowd evacuation in case of panic situations [20], or designing reference trajectories for a master robot to guide slave robots [21].

In this paper, we study a nonlinear fractional leader–follower model of opinion formation. The contribution of the paper is twofold. First, some sufficient conditions are established to ensure that the opinions of all agents globally asymptotically approach the opinion of the leader. Second, we address the problem of designing optimal control strategies for the leader so that the followers tend to consensus in the most efficient way. In other words, external control is applied to the leader in a manner that minimizes disagreements among all agents and the amount of interventions.

The rest of the paper is organized as follows. In Sect. 2, some preliminaries about fractional operators are given. The fractional opinion formation model with a leader is discussed in Sect. 3. Section 4 presents the leader–follower optimal control problem. The necessary and sufficient optimality conditions for this problem are given. In Sect. 4.1, we discuss a variational integrator scheme for the Hamiltonian system. This numerical method guarantees preservation of the variational structure of the underlying system at the discrete level [22, 23, 24, 25, 26, 27]. Numerical examples, presented in Sect. 5, demonstrate the effectiveness of the proposed control strategy. Finally, conclusions are given in Sect. 6.

## 2 Preliminaries

*f*are given by

*f*, they are singular: \(\lim \nolimits _{t\rightarrow a}D^{\alpha }_{a+}[f](t)=\infty \), unless \(f(a)=0\), and \(\lim \nolimits _{t\rightarrow b}D^{\alpha }_{b-}[f](t)=-\infty \), unless \(f(b)=0\). These facts motivated the introduction of Caputo fractional derivatives. The left and right Caputo fractional derivatives (CFD) are defined as

*f*, of order \(\alpha \), are given by

*a*,

*b*] with \(M\ge 2\) and \(h=(b-a)/M\). Then, we have

## 3 Fractional Opinion Formation Model

*j*is able to influence the opinion of agent

*i*and \(a_{ij}=0\) otherwise, for \(i,j,=1,\dots ,N\); \(\alpha \in ]0,1[\) denotes the significance of the memory in the interaction mechanism. Moreover, we assume that functions \(f_j\), \(j=1,\dots ,N,\) are continuous and satisfy the Lipschitz condition on \(\mathbb {R}\) with Lipschitz constants \(l_j>0\), i.e., \(|f_j(x)-f_j(y)|\le l_j|x-y|\) for every \(x,y\in \mathbb {R}\) and \(j=1,\dots ,N\). The crucial question regarding model (5) is whether the opinions converge to the same unique opinion, which means consensus.

### Definition 3.1

We call consensus a configuration in which the opinions of all agents are equal, i.e., \(x^* \in \mathbb {R}^N\) such that \(x^*_1=x^*_2=\ldots =x^*_N\). We say that a solution \(x(t)=(x_{1}(t),\ldots ,x_{N}(t))^T\) of system (5) tends to consensus if there exists a consensus configuration \(x^* \in \mathbb {R}^N\) such that \(\lim \nolimits _{t\rightarrow \infty }x_i(t)=x^*_i\) for every \(i=1,\dots ,N\).

*i*th agent’s opinion is influenced by the leader and \(c_{i}=0\) otherwise. Equivalently, system (6) can be written as

### Theorem 3.1

Suppose that \(f_i(x_i)=x_i\) and \(c_{i}>0\) for every \(i=1,\dots ,N\). Then, a solution of system (8) tends to consensus \(x^*=(\xi _0,\ldots ,\xi _0)^T\in \mathbb {R}^N\).

### Proof

*N*disks: \(\bigcup _{i=1}^{N}\{z\in \mathbb {R}^2:~~|z+\sum \nolimits _{i\ne j}a_{ij}+c_i|\le \sum \nolimits _{i\ne j}a_{ij}\}.\) Since \(c_{i}>0\) for every \(i=1,\dots ,N\), matrix \(B-C\) has all eigenvalues with negative real parts. Therefore, by Theorem 1 in [30], system (9) is asymptotically stable, which means that \(\lim \nolimits _{t\rightarrow \infty }\Vert y(t)\Vert =0\) for a solution to (9) with any initial conditions. It follows that \(\lim \nolimits _{t\rightarrow \infty }x_i(t)=\xi _0\) for every \(i=1,\dots ,N\). \(\square \)

### Theorem 3.2

Suppose that \(f_i(\xi _0)=0\), \(c_i>0\) and \(c_i-\sum \nolimits _{j=1}^N|b_{ji}|l_i>0\) for every \(i=1,\dots ,N\). Then, a solution of system (8) tends to consensus \(x^*=(\xi _0,\ldots ,\xi _0)^T\in \mathbb {R}^N\).

### Proof

## 4 Optimal Leader–Follower Control

*t*. Precisely, functions \(f_j\), \(j=1,\dots ,N\) are of class \(C^1\) with respect to \(x_j\) and satisfy the Lipschitz condition: for every \(x,y\in \mathbb {R}\) and every \(t\in [0,T]\), \( |f_j(t,x)-f_j(t,y)|\le l_j|x-y|.\) In order to use the least amount of intervention, we seek to minimize the following cost functional:

### Remark 4.1

### Theorem 4.1

*T*] and associated with a control \(u^*\in L^\infty ([0,T];\mathbb {R})\), is a solution to problem (11)–(12), then there exists a function \(\lambda \in H^{\alpha }([0,T];\mathbb {R}^{N+1})\) such that:

- (i)\(x^*\) and \(\lambda \) are weak solutions to the Hamiltonian system$$\begin{aligned}&{}^{\mathrm{c}} D^{\alpha }_{0+}[x](t)=\displaystyle \frac{\partial H}{\partial \lambda }(t,x(t),u^*(t),\lambda (t)),\nonumber \\&{}^{\mathrm{c}} D^{\alpha }_{T-}[\lambda ](t)=\displaystyle \frac{\partial H}{\partial x}(t,x(t),u^*(t),\lambda (t)), \quad t\in [0,T]; \end{aligned}$$(13)
- (ii)
\(\lambda (T)=0\);

- (iii)
the stationary condition \(\lambda _0(t)=-\nu u^*(t)\) holds for almost every \(t\in [0,T]\).

Under additional assumptions, we can prove sufficient optimality conditions for problem (11)–(12).

### Theorem 4.2

Assume that functions \(f_j\) are convex in \(x_j\) for all \(j=1,\ldots ,N\) and \(\lambda (t)\ge 0\) for all \(t\in [0,T]\) or \(f_j\) are linear in \(x_j\) for all \(j=1,\ldots ,N\). If \(x^*\), \(u^*\) and \(\lambda \) satisfy conditions (i)–(iii) of Theorem 4.1, then \(x^*\) associated with a control \(u^*\) is a solution to problem (11)–(12).

### Proof

It follows easily from Theorem 10.3 in [34]. \(\square \)

### 4.1 Variational Integrator for the Leader–Follower Control Problem

*T*], with \(M\ge 2\) and \(h=\frac{T}{M}\) , such that \(2h^\alpha NL<1\), where

*L*is defined as in Remark 4.1. We consider the discrete analogue of problem (11)–(12) given by the system

### Theorem 4.3

- (i)\(x^*\) and \(\lambda \) are solutions to the Hamiltonian system$$\begin{aligned}&{^{c}}\varDelta ^{\alpha }_{0+}[x](t_k)=\displaystyle \frac{\partial H^d}{\partial \lambda }(t_k,x(t_k),u^*(t_k),\lambda (t_{k-1})),~~k=1,\dots ,M,\nonumber \\&\varDelta ^{\alpha }_{T-}[\lambda ](t_k)=\displaystyle \frac{\partial H^d}{\partial x}(t_{k+1},x(t_{k+1}),u^*(t_{k+1}),\lambda (t_{k})),~~k=0,\dots ,M-1;\nonumber \\ \end{aligned}$$(16)
- (ii)
\(\lambda (T)=0\);

- (iii)
the stationary condition \( \lambda _0(t_{k-1})=-\nu u^*(t_k), \quad k=1,\dots ,M,\) holds.

We emphasize that system (16) is not a direct discretization of system (13) by the method explained in Sect. 2 (see [22, 23, 24, 25] for a more in depth discussion about this issue).

## 5 Illustrative Examples

On the basis of the numerical scheme developed in Sect. 4.1, a Maple code has been written and some fractional systems are now analyzed. In all examples, the computations are performed by assuming that \(\alpha =\frac{1}{2}\). First, we consider systems without a leader. Then, we add the leader but with the assumption that \(u=0\), that is, systems are uncontrolled. Finally, the results obtained with the optimal leader–follower control problem are presented.

### Example 5.1

### Example 5.2

### Example 5.3

## 6 Conclusions

In this work, the agent-based model of opinion formation given by the system of nonlinear fractional differential equations was investigated. We emphasize that, by taking the fractional derivative on the left-hand side of the nonlinear system, the long memory effect was included in the considered model. In order to ensure convergence to consensus, we introduced a virtual leader. Moreover, we proposed optimal control strategies for the leader so that the opinions of other agents approach its opinion in the most efficient way. We have used the fractional derivative defined in the sense of Caputo; however, it would also be interesting to consider systems with other types of fractional derivatives, such as the Hadamard or the Erdélyi–Kober type. Clearly, in practice, the choice of a fractional operator should depend on the particular phenomenon that the model is supposed to describe.

## Notes

### Acknowledgements

R. Almeida was supported by Portuguese funds through the CIDMA—Center for Research and Development in Mathematics and Applications, and the Portuguese Foundation for Science and Technology (FCT-Fundação para a Ciência e a Tecnologia), within project UID/MAT/04106/2013; A. B. Malinowska and T. Odzijewicz were supported by Polish founds of the National Science Center, granted on the basis of decision DEC-2014/15/B/ST7/05270.

## References

- 1.Afshar, M., Asadpour, M.: Opinion formation by informed agents. J. Artif. Soc. Soc. Simul.
**13**, 5 (2010)CrossRefGoogle Scholar - 2.Blondel, V.D., Hendrickx, J.M., Tsitsiklis, J.N.: Continuous-time average-preserving opinion dynamics with opinion-dependent communications. SIAM J. Control Optim.
**18**, 5214–5240 (2010)MathSciNetCrossRefzbMATHGoogle Scholar - 3.Cucker, F., Smale, S.: On the mathematics of emergence. Jpn. J. Math.
**2**, 197–227 (2007)MathSciNetCrossRefzbMATHGoogle Scholar - 4.Hegselmann, R., Krause, U.: Opinion dynamics and bounded confidence, models, analysis and simulation. J. Artif. Soc. Soc. Simul.
**81**, 591–646 (2009)Google Scholar - 5.Sznajd-Weron, K., Sznajd, J.: Opinion evolution in closed community. Int. J. Mod. Phys. C
**11**, 1157–1165 (2000)CrossRefzbMATHGoogle Scholar - 6.Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature
**393**, 440–442 (1998)CrossRefzbMATHGoogle Scholar - 7.Podlubny, I.: Fractional Differential Equations. Mathematics in Science and Engineering. Academic Press, San Diego (1999)zbMATHGoogle Scholar
- 8.Ahmeda, E., Elgazzar, A.: On fractional order differential equations model for nonlocal epidemics. Physica A
**379**, 607–614 (2007)CrossRefGoogle Scholar - 9.Cottone, G., Paola, M., Santoro, R.: A novel exact representation of stationary colored Gaussian processes (fractional differential approach). J. Phys. A
**43**, 085002 (2010)MathSciNetCrossRefzbMATHGoogle Scholar - 10.Vazquez, L.: A fruitful interplay: from nonlocality to fractional calculus. Nonlinear waves: classical and quantum aspects. NATO Sci. Ser. II Math. Phys. Chem
**153**, 129–133 (2005)CrossRefGoogle Scholar - 11.DeGroot, M.H.: Reaching a consensus. J. Am. Stat. Assoc.
**69**, 118–121 (1974)CrossRefzbMATHGoogle Scholar - 12.Girejko, E., Machado, L., Malinowska, A.B., Martins, N.: On consensus in the Cucker–Smale type model on isolated times scales. Discrete Contin. Dyn. Syst. Ser. S
**11**, 77–89 (2018)MathSciNetCrossRefzbMATHGoogle Scholar - 13.Hegselmann, R., Krause, U.: Opinion dynamics under the influence of radical groups, charismatic leaders and other constant signals: a simple unifying model. Netw. Heterog. Media
**10**, 477–509 (2015)MathSciNetCrossRefzbMATHGoogle Scholar - 14.Mozyrska, D., Wyrwas, M.: Fractional discrete-time of Hegselmann–Krause’s type consensus model with numerical simulations. Neurocomputing
**2016**, 381–392 (2016)CrossRefGoogle Scholar - 15.Jadbabaie, A., Lin, J., Morse, A.S.: Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans. Autom. Control
**48**, 988–1001 (2003)MathSciNetCrossRefzbMATHGoogle Scholar - 16.Olfati-Saber, R.: Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans. Autom. Control
**51**, 401–420 (2006)MathSciNetCrossRefzbMATHGoogle Scholar - 17.Ren, G., Yu, Y., Zhang, S.: Leader-following consensus of fractional nonlinear multiagent systems. Math. Probl. Eng.
**2015**, 919757 (2015)MathSciNetGoogle Scholar - 18.Wongkaew, S., Caponigro, M., Borzí, A.: On the control through leadership of the Hegselmann–Krause opinion formation model. Math. Methods Appl. Sci.
**3**, 565–585 (2015)MathSciNetCrossRefzbMATHGoogle Scholar - 19.Vaughan, R., Sumpter, N., Henderson, J., Frost, A., Cameron, S.: Experiments in automatic flock control. Robot. Auton. Syst.
**30**, 109–117 (2000)CrossRefGoogle Scholar - 20.Yang, Y., Dimarogonas, D. V., Hu, X.: Optimal leader-follower control for crowd evacuation. In: Proceedings of the 52nd IEEE Conference on Decision Control (CDC), pp. 2769–2774 (2013)Google Scholar
- 21.Cheng, L., Wang, Y., Ren, W., Hou, Z.-G., Tan, M.: Containment control of multiagent systems with dynamic leaders based on a PI n-type approach. IEEE Trans. Cybern.
**46**, 3004–3017 (2016)CrossRefGoogle Scholar - 22.Bourdin, L., Cresson, J., Greff, I., Inizan, P.: Variational integrator for fractional Euler–Lagrange equations. Appl. Numer. Math.
**71**, 14–23 (2013)MathSciNetCrossRefzbMATHGoogle Scholar - 23.Bourdin, L.: A class of fractional optimal control problems and fractional Pontryagin’s systems. Existence of a fractional Noether’s theorem. arXiv:1203.1422
- 24.Bourdin, L.: Variational integrator for fractional Pontryagin’s systems. Existence of a discrete fractional Noether’s theorem. arXiv:1203.1707
- 25.Bourdin, L.: Contributions au Calcul des Variations et au Principe du Maximum de Pontryagin en Calculs Time Scale et Fractionnaire. Ph.D. Thesis, Universitè de Pau et des Pays de l’Adour (2013)Google Scholar
- 26.Hairer, E., Lubich, C., Wanner, G.: Geometric Numerical Integration. Springer Series in Computational Mathematics, vol. 31, 2nd edn. Springer, Berlin (2006)zbMATHGoogle Scholar
- 27.Marsden, J.E., West, M.: Discrete mechanics and variational integrators. Acta Numer.
**10**, 357–514 (2001)MathSciNetCrossRefzbMATHGoogle Scholar - 28.Kilbas, A.A., Srivastava, H.M., Trujillo, J.J.: Theory and Applications of Fractional Differential Equations. North-Holland Mathematics Studies. Elsevier Science B.V., Amsterdam (2006)Google Scholar
- 29.Horn, R., Johnson, C.: Matrix Analysis. Cambridge University Press, New York (1985)CrossRefzbMATHGoogle Scholar
- 30.Qian, D., Li, C., Agarwal, R.P., Wong, P.J.Y.: Stability analysis of fractional differential system with Riemann–Liouville derivative. Math. Comput. Model.
**52**, 862–874 (2010)MathSciNetCrossRefzbMATHGoogle Scholar - 31.Zhang, S., Yu, Y., Wang, H.: Mittag–Leffter stability of fractional-order Hopfield neutral networks. Nonlinear Anal. Hybrid Syst.
**16**, 104–121 (2015)MathSciNetCrossRefGoogle Scholar - 32.Kamocki, R., Majewski, M.: Fractional linear control systems with Caputo derivative and their optimization. Optimal Control Appl. Methods
**36**, 953–967 (2015)MathSciNetCrossRefzbMATHGoogle Scholar - 33.Agrawal, O.P.: A general formulation and solution scheme for fractional optimal control problems. Nonlinear Dyn.
**38**, 323–337 (2004)MathSciNetCrossRefzbMATHGoogle Scholar - 34.Almeida, R., Pooseh, S., Torres, D.F.M.: Computational Methods in the Fractional Calculus of Variations. Imperial College Press, Singapore (2015)CrossRefzbMATHGoogle Scholar

## Copyright information

**Open Access**This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.