Skip to main content
Log in

Game-Theory-Based Consensus Learning of Double-Integrator Agents in the Presence of Worst-Case Adversaries

  • Published:
Journal of Optimization Theory and Applications Aims and scope Submit manuscript

Abstract

This work proposes a game-theory-based technique for guaranteeing consensus in unreliable networks by satisfying local objectives. This multi-agent problem is addressed under a distributed framework, in which every agent has to find the best controller against a worst-case adversary so that agreement is reached among the agents in the networked team. The construction of such controllers requires the solution of a system of coupled partial differential equations, which is typically not feasible. The algorithm proposed uses instead three approximators for each agent: one to approximate the value function, one to approximate the control law, and a third one to approximate a worst-case adversary. The tuning laws for every controller and adversary are driven by their neighboring controllers and adversaries, respectively, and neither the controller nor the adversary knows each other’s policies. A Lyapunov stability proof ensures that all the signals remain bounded and consensus is asymptotically reached. Simulation results are provided to demonstrate the efficacy of the proposed approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Teixeira, A., Sandberg, H., Johansson, K.H.: Networked control systems under cyber attacks with applications to power networks. In: American Control Conference (ACC), 2010, pp. 3690–3696. IEEE (2010)

  2. Vamvoudakis, K.G., Hespanha, J.P.: Online optimal operation of parallel voltage-source inverters using partial information. IEEE Trans. Ind. Electron. 64(5), 4296–4305 (2017)

    Article  Google Scholar 

  3. Beard, R.W., McLain, T.W., Nelson, D.B., Kingston, D., Johanson, D.: Decentralized cooperative aerial surveillance using fixed-wing miniature UAVs. Proc. IEEE 94(7), 1306–1324 (2006)

    Article  Google Scholar 

  4. Kunwar, F., Benhabib, B.: Rendezvous-guidance trajectory planning for robotic dynamic obstacle avoidance and interception. IEEE Trans. Syst. Man Cybern. Part B Cybern. 36(6), 1432–1441 (2006)

    Article  Google Scholar 

  5. Lee, D., Spong, M.W.: Stable flocking of multiple inertial agents on balanced graphs. IEEE Trans. Autom. Control 52(8), 1469–1475 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  6. Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proc. IEEE 95(1), 215–233 (2007)

    Article  MATH  Google Scholar 

  7. Jadbabaie, A., Lin, J., Morse, A.S.: Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans. Autom. Control 48(6), 988–1001 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  8. Ren, W., Beard, R.W., Atkins, E.M.: A survey of consensus problems in multi-agent coordination. In: American Control Conference, 2005. Proceedings of the 2005, pp. 1859–1864. IEEE (2005)

  9. Tsitsiklis, J.N.: Problems in decentralized decision making and computation. Tech. rep., MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR INFORMATION AND DECISION SYSTEMS (1984)

  10. Abbeel, P., Coates, A., Ng, A.Y.: Autonomous helicopter aerobatics through apprenticeship learning. Int. J. Robot. Res. 29(13), 1608–1639 (2010)

    Article  Google Scholar 

  11. Shoham, Y., Leyton-Brown, K.: Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  12. Vamvoudakis, K.G., Antsaklis, P.J., Dixon, W.E., Hespanha, J.P., Lewis, F.L., Modares, H., Kiumarsi, B.: Autonomy and machine intelligence in complex systems: a tutorial. In: American Control Conference (ACC), 2015, pp. 5062–5079. IEEE (2015)

  13. Vamvoudakis, K.G., Modares, H., Kiumarsi, B., Lewis, F.L.: Game theory-based control system algorithms with real-time reinforcement learning: how to solve multiplayer games online. IEEE Control Syst. 37(1), 33–52 (2017)

    Article  MathSciNet  Google Scholar 

  14. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, vol. 1. MIT Press, Cambridge (1998)

    Google Scholar 

  15. Vrabie, D., Vamvoudakis, K.G., Lewis, F.L.: Optimal adaptive control and differential games by reinforcement learning principles, vol. 2. IET (2013)

  16. Werbos, P.J.: Approximate dynamic programming for real-time control and neural modeling. In: White, D.A., Sofge, D.A. (eds.) Handbook of Intelligent Control. Van Nostrand Reinhold, New York (1992)

    Google Scholar 

  17. Bertsekas, D.P., Tsitsiklis, J.N.: Neuro-dynamic programming: an overview. In: Proceedings of the 34th IEEE Conference on Decision and Control, vol. 1, pp. 560–564. IEEE (1995)

  18. Cardenas, A., Amin, S., Sinopoli, B., Giani, A., Perrig, A., Sastry, S., et al.: Challenges for securing cyber physical systems. In: Workshop on Future Directions in Cyber-Physical Systems Security, vol. 5 (2009)

  19. Cardenas, A.A., Amin, S., Sastry, S.: Secure control: towards survivable cyber-physical systems. In: 28th International Conference on Distributed Computing Systems Workshops, 2008. ICDCS’08, pp. 495–500. IEEE (2008)

  20. Pasqualetti, F., Bicchi, A., Bullo, F.: Consensus computation in unreliable networks: a system theoretic approach. IEEE Trans. Autom. Control 57(1), 90–104 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  21. Alpcan, T., Başar, T.: Network Security: A Decision and Game-Theoretic Approach. Cambridge University Press, Cambridge (2010)

    Book  MATH  Google Scholar 

  22. Basar, T., Olsder, G.J.: Dynamic noncooperative game theory, vol. 23. Siam (1999)

  23. Vamvoudakis, K.G., Hespanha, J.P., Sinopoli, B., Mo, Y.: Detection in adversarial environments. IEEE Trans. Autom. Control 59(12), 3209–3223 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  24. Holmgren, A.J., Jenelius, E., Westin, J.: Evaluating strategies for defending electric power networks against antagonistic attacks. IEEE Trans. Power Syst. 22(1), 76–84 (2007)

    Article  Google Scholar 

  25. Wang, J., Elia, N.: Distributed averaging algorithms resilient to communication noise and dropouts. IEEE Trans. Signal Process. 61(9), 2231–2242 (2013)

    Article  Google Scholar 

  26. Zhu, M., Martínez, S.: Attack-resilient distributed formation control via online adaptation. In: 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), pp. 6624–6629. IEEE (2011)

  27. Chung, S.J., Slotine, J.J.E.: Cooperative robot control and concurrent synchronization of lagrangian systems. IEEE Trans. Rob. 25(3), 686–700 (2009)

    Article  Google Scholar 

  28. LeBlanc, H.J., Koutsoukos, X.D.: Low complexity resilient consensus in networked multi-agent systems with adversaries. In: Proceedings of the 15th ACM International Conference on Hybrid Systems: Computation and Control, pp. 5–14. ACM (2012)

  29. Semsar, E., Khorasani, K.: Optimal control and game theoretic approaches to cooperative control of a team of multi-vehicle unmanned systems. In: 2007 IEEE International Conference on Networking, Sensing and Control, pp. 628–633. IEEE (2007)

  30. Semsar-Kazerooni, E., Khorasani, K.: An lmi approach to optimal consensus seeking in multi-agent systems. In: American Control Conference, 2009. ACC’09., pp. 4519–4524. IEEE (2009)

  31. Khanafer, A., Touri, B., Başar, T.: Consensus in the presence of an adversary. IFAC Proc. Vol. 45(26), 276–281 (2012)

    Article  Google Scholar 

  32. Bauso, D., Giarre, L., Pesenti, R.: Mechanism design for optimal consensus problems. In: 2006 45th IEEE Conference on Decision and Control, pp. 3381–3386. IEEE (2006)

  33. Chung, S.J., Bandyopadhyay, S., Chang, I., Hadaegh, F.Y.: Phase synchronization control of complex networks of lagrangian systems on adaptive digraphs. Automatica 49(5), 1148–1161 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  34. Carli, R., Zampieri, S.: Networked clock synchronization based on second order linear consensus algorithms. In: 2010 49th IEEE Conference on Decision and Control (CDC), pp. 7259–7264. IEEE (2010)

  35. Yucelen, T., Egerstedt, M.: Control of multiagent systems under persistent disturbances. In: American Control Conference (ACC), 2012, pp. 5264–5269. IEEE (2012)

  36. Vamvoudakis, K.G., Lewis, F.L., Hudas, G.R.: Multi-agent differential graphical games: Online adaptive learning solution for synchronization with optimality. Automatica 48(8), 1598–1611 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  37. Sundaram, S., Hadjicostis, C.N.: Distributed function calculation via linear iterative strategies in the presence of malicious agents. IEEE Trans. Autom. Control 56(7), 1495–1508 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  38. Zhu, Q., Bushnell, L., Başar, T.: Resilient distributed control of multi-agent cyber-physical systems. In: Control of Cyber-Physical Systems, pp. 301–316. Springer (2013)

  39. Chen, L., Roy, S., Saberi, A.: On the information flow required for tracking control in networks of mobile sensing agents. IEEE Trans. Mob. Comput. 10(4), 519–531 (2011)

    Article  Google Scholar 

  40. Peymani, E., Grip, H.F., Saberi, A., Wang, X., Fossen, T.I.: H-\(\infty \) almost output synchronization for heterogeneous networks of introspective agents under external disturbances. Automatica 50(4), 1026–1036 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  41. Bardi, M., Capuzzo-Dolcetta, I.: Optimal control and viscosity solutions of Hamilton–Jacobi–Bellman equations. Springer, Berlin (2008)

    MATH  Google Scholar 

  42. Crandall, M.G., Lions, P.L.: Viscosity solutions of Hamilton–Jacobi equations. Trans. Am. Math. Soc. 277(1), 1–42 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  43. Van Der Schaft, A.J.: L/sub 2/-gain analysis of nonlinear systems and nonlinear state-feedback h/sub infinity/control. IEEE Trans. Autom. Control 37(6), 770–784 (1992)

    Article  MATH  Google Scholar 

  44. Rao, V.G., Bernstein, D.S.: Naive control of the double integrator. IEEE Control Syst. 21(5), 86–97 (2001)

    Article  Google Scholar 

  45. Kearns, M., Littman, M.L., Singh, S.: Graphical models for game theory. In: Proceedings of the Seventeenth conference on Uncertainty in Artificial Intelligence, pp. 253–260. Morgan Kaufmann Publishers Inc. (2001)

  46. Beard, R.W., Saridis, G.N., Wen, J.T.: Approximate solutions to the time-invariant Hamilton–Jacobi–Bellman equation. J. Optim. Theory Appl. 96(3), 589–626 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  47. Bryson, A., Ho, Y.C.: Applied Optimal Control. Hemisphere, New York (1975)

    Google Scholar 

  48. Khalil, H.K.: Nonlinear Systems, vol. 3. Prentice Hall, Upper Saddle River (2002)

    MATH  Google Scholar 

  49. Hornik, K., Stinchcombe, M., White, H.: Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Netw. 3(5), 551–560 (1990)

    Article  Google Scholar 

  50. Ioannou, P., Fidan, B.: Adaptive control tutorial. Society for Industrial and Applied Mathematics (2006)

  51. Lewis, F., Jagannathan, S., Yesildirak, A.: Neural Network Control of Robot Manipulators and Non-Linear Systems. CRC Press, Boca Raton (1998)

    Google Scholar 

  52. Cao, C., Hovakimyan, N.: Novel \(l_1\) neural network adaptive control architecture with guaranteed transient performance. IEEE Trans. Neural Netw. 18(4), 1160–1171 (2007)

    Article  Google Scholar 

  53. Krstic, M., Kanellakopoulos, I., Kokotovic, P.V.: Nonlinear and Adaptive Control Design. Wiley, New York (1995)

    MATH  Google Scholar 

  54. Lavretsky, E., Wise, K.A.: Robust and adaptive control with aerospace applications. In: Advanced Textbooks in Control and Signal Processing. Springer-Verlag, London (2013)

  55. Pomet, J.B., Praly, L.: Adaptive nonlinear regulation: estimation from the Lyapunov equation. IEEE Trans. Autom. Control 37(6), 729–740 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  56. Anderson, B.: Exponential stability of linear equations arising in adaptive identification. IEEE Trans. Autom. Control 22(1), 83–88 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  57. Ioannou, P.A., Tao, G.: Dominant richness and improvement of performance of robust adaptive control. Automatica 25(2), 287–291 (1989)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This material is based upon work supported in part by NATO under Grant No. SPS G5176, by ONR Minerva under Grant No. N00014-18-1-2160, by an NSF CAREER, by NAWCAD under Grant No. N00421-16-2-0001, and by an US Office of Naval Research MURI Grant No. N00014-16-1-2710.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kyriakos G. Vamvoudakis.

Appendix

Appendix

Proof of Lemma 3.1

The error in Eq. (23) after subtracting zero becomes

$$\begin{aligned} e_i&=\hat{W}_\mathrm {c_i}^T {\frac{\partial \phi _i}{\partial {s}_i}}\bigg (\begin{bmatrix}\mathbf {0}&\mathbf {I} \\ \mathbf {0}&\mathbf {0}\end{bmatrix}s_i+ d_i \begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(\hat{u}_i+\hat{v}_i) -\sum _{j\in {\mathcal {N}}_i} \begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(\hat{u}_j+\hat{v}_j)\bigg )\nonumber \\&\quad -\,W_i^T{\frac{\partial \phi _i}{\partial {s}_i}}\bigg (\begin{bmatrix}\mathbf {0}&\mathbf {I} \\ \mathbf {0}&\mathbf {0}\end{bmatrix}s_i+ d_i \begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(u_i^*+v_i^*) -\sum _{j\in {\mathcal {N}}_i} \begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(u_j^*+v_j^*)\bigg )\nonumber \\&\quad +\,\frac{1}{2} \bigg (\Vert \hat{u}_i\Vert ^2 +\sum _{j\in {\mathcal {N}}_i}\Vert \hat{u}_j\Vert ^2-\gamma _\mathrm {ii}^2\Vert v_i\Vert ^2-\sum _{j\in {\mathcal {N}}_i}\gamma _\mathrm {ij}^2\Vert \hat{v}_j\Vert ^2\bigg )\nonumber \\&\quad -\,\frac{1}{2} \bigg (\Vert u_i^*\Vert ^2 +\sum _{j\in {\mathcal {N}}_i}\Vert u_j^*\Vert ^2-\gamma _{ii}^2\Vert v_i^*\Vert ^2-\sum _{j\in {\mathcal {N}}_i}\gamma _{ij}^2\Vert v_j^*\Vert ^2\bigg )\nonumber \\&\quad -\,\frac{\partial \epsilon _\mathrm {c_i}}{\partial {s}_i}^T\bigg (\begin{bmatrix}\mathbf {0}&\mathbf {I} \\ \mathbf {0}&\mathbf {0}\end{bmatrix}s_i+ d_i \begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(u_i^*+v_i^*) \\ {}&\quad -\,\sum _{j\in {\mathcal {N}}_i} \begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(u_j^*+v_j^*)\bigg ), \ \forall i \in N. \end{aligned}$$

Completing the squares we have

$$\begin{aligned} e_i&=-\,\tilde{W}_\mathrm {c_i}^T\omega _i-W_i^T{\frac{\partial \phi _i}{\partial {s}_i}}\bigg (d_i\begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix} (\tilde{u}_i+\tilde{v}_i)-\sum _{j\in {\mathcal {N}}_i}\begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(\tilde{u}_j+\tilde{v}_j)\bigg )\nonumber \\&\quad +\,\frac{1}{2}\Vert \tilde{u}_i\Vert ^2-\frac{1}{2}\gamma _\mathrm {ii}^2\Vert \tilde{v}_j\Vert ^2-\tilde{u}_i^T u_i^*+\gamma _\mathrm {ii}^2\tilde{v}_i^T v_i^*\nonumber \\&\quad +\,\frac{1}{2}\sum _{j\in {\mathcal {N}}_i}\bigg (\Vert \tilde{u}_j\Vert ^2-\gamma _\mathrm {ij}^2\Vert \tilde{v}_j\Vert ^2-2\tilde{u}_j^T u_j^*+2\gamma _\mathrm {ij}^2 \tilde{v}_j^T v_j^*\bigg )\nonumber \\&\quad -\,\frac{\partial \epsilon _\mathrm {c_i}}{\partial {s}_i}^T\bigg (\begin{bmatrix}\mathbf {0}&\mathbf {I} \\ \mathbf {0}&\mathbf {0}\end{bmatrix}s_i+ d_i \begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(u_i^*+v_i^*)\nonumber \\&\quad -\,\sum _{j\in {\mathcal {N}}_i} \begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(u_j^*+v_j^*)\bigg ), \ \forall i \in N. \end{aligned}$$
(39)

The dynamics of the critic estimation error \(\tilde{W}_{c_i}\) can be found by substituting (39) in (25) as

$$\begin{aligned} \dot{\tilde{W}}_\mathrm {c_i}&=-\,\alpha _i \bar{\omega }_{i}\bar{\omega }_{i}^T \tilde{W}_{c_i} \nonumber \\&\quad +\,\alpha _i\frac{ \omega _i}{(\omega _i^T\omega _i+1)^2}\bigg [-W_i^T{\frac{\partial \phi _i}{\partial {s}_i}}\bigg (d_i\begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix} (\tilde{u}_i+\tilde{v}_i)\nonumber \\&\quad -\,\sum _{j\in {\mathcal {N}}_i}\begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(\tilde{u}_j+\tilde{v}_j)\bigg ) +\,\frac{1}{2}\Vert \tilde{u}_i\Vert ^2-\frac{1}{2}\gamma _\mathrm {ii}^2\Vert \tilde{v}_j\Vert ^2-\tilde{u}_i^T u_i^*+\gamma _\mathrm {ii}^2\tilde{v}_i^T v_i^*\nonumber \\&\quad +\,\frac{1}{2}\sum _{j\in {\mathcal {N}}_i}\bigg (\Vert \tilde{u}_j\Vert ^2-2\gamma _\mathrm {ij}^2\Vert \tilde{v}_j\Vert ^2-2\tilde{u}_j^T u_j^*+2\gamma _\mathrm {ij}^2 \tilde{v}_j^T v_j^*\bigg )\nonumber \\&\quad -\,\frac{\partial \epsilon _\mathrm {c_i}}{\partial {s}_i}^T\bigg (\left[ \begin{array}{cc} \mathbf {0} &{} \mathbf {I} \\ \mathbf {0} &{} \mathbf {0} \end{array}\right] s_i+ d_i \begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(u_i^*+v_i^*)\nonumber \\&\quad -\,\sum _{j\in {\mathcal {N}}_i} \begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(u_j^*+v_j^*)\bigg )\bigg ], \ \forall i \in {\mathcal {N}}, \end{aligned}$$
(40)

from which the result follows. \(\square \)

The following fact is a consequence of Assumption 3.1 and the properties of the projection operator \({\mathcal {P}}r[.]\).

Fact A

There exist constants \(b_\mathrm {i1}, b_\mathrm {i2}, b_\mathrm {i3}, b_\mathrm {i4}, b_\mathrm {i5}\in \mathbb {R}_+\) for which the following bounds hold, for every agent \(\forall i\in \mathcal {N}\) and time \(\forall t\geqslant 0\):

$$\begin{aligned}&\left\| {\frac{\partial \phi _i}{\partial {s}_i}}\begin{bmatrix}\mathbf {0}&\mathbf {0}\\ \mathbf {0}&\mathbf {I}\end{bmatrix}{\frac{\partial \phi _i}{\partial {s}_i}}^T\right\| \leqslant b_\mathrm {i1},\ \ \Vert \tilde{W}_\mathrm {u_i}\Vert \leqslant b_\mathrm {i2}, \ \ \Vert \tilde{W}_\mathrm {v_i}\Vert \leqslant b_\mathrm {i3},\nonumber \\&\left\| -W_i^T{\frac{\partial \phi _i}{\partial {s}_i}}\bigg (d_i\begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix} (\tilde{u}_i+\tilde{v}_i)-\sum _{j\in {\mathcal {N}}_i}\begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(\tilde{u}_j+\tilde{v}_j)\bigg ) \right. \nonumber \\&\quad \left. +\,\frac{1}{2}\Vert \tilde{u}_i\Vert ^2-\frac{1}{2}\gamma _\mathrm {ii}^2\Vert \tilde{v}_i\Vert ^2-\tilde{u}_i^T u_i^*+\gamma _\mathrm {ii}^2\tilde{v}_i^T v_i^*\right. \nonumber \\&\quad \left. +\,\frac{1}{2}\sum _{j\in {\mathcal {N}}_i}\bigg (\Vert \tilde{u}_j\Vert ^2-\gamma _\mathrm {ij}^2\Vert \tilde{v}_j\Vert ^2-2\tilde{u}_j^T u_j^*+2\gamma _\mathrm {ij}^2 \tilde{v}_j^T v_j^*\bigg ) -\frac{\partial \epsilon _\mathrm {c_i}}{\partial {s}_i}^T\right. \nonumber \\&\left. \bigg (\begin{bmatrix}\mathbf {0}&\mathbf {I} \\ \mathbf {0}&\mathbf {0}\end{bmatrix}s_i+ d_i \begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(u_i^*+v_i^*) -\sum _{j\in {\mathcal {N}}_i} \begin{bmatrix}\mathbf {0} \\ \mathbf {I}\end{bmatrix}(u_j^*+v_j^*)\bigg )\right\| \leqslant b_\mathrm {i4} \end{aligned}$$
(41)
$$\begin{aligned}&\left\| {u_i^*}^T\tilde{u}_i-{v_i^*}^T\tilde{v}_i-\frac{{u_i^*}^T}{d_i}\sum _{j\in {\mathcal {N}}_i}\tilde{u}_j+\frac{\gamma _\mathrm {ii}^2}{d_i}{v_i^*}^T\sum _{j\in {\mathcal {N}}_i}\tilde{v}_j\right. \nonumber \\&\quad \left. -\,\frac{1}{2}\bigg (\Vert u_i^*\Vert ^2 +\sum _{j\in {\mathcal {N}}_i}\Vert u_j^*\Vert ^2 -\gamma _\mathrm {ii}^2\Vert v_i^*\Vert ^2-\sum _{j\in {\mathcal {N}}_i}\gamma _\mathrm {ij}^2\Vert v_j^*\Vert ^2\bigg )\right. \nonumber \\&\quad \left. -\,\tilde{W}_\mathrm {u_i}^T\bigg (d_i^2\frac{\partial \phi _i}{\partial {s}_i}\begin{bmatrix}\mathbf {0}&\mathbf {0}\\ \mathbf {0}&\mathbf {I}\end{bmatrix}\frac{\partial \phi _i}{\partial {s}_i}^T W_i\frac{\bar{\omega }_i^T W_i}{{(\omega _i^T\omega _i+1)}} \right. \nonumber \\&\quad \left. -\,d_i^2\frac{\partial \phi _i}{\partial {s}_i}\begin{bmatrix}\mathbf {0}&\mathbf {0}\\ \mathbf {0}&\mathbf {I}\end{bmatrix}\frac{\partial \phi _i}{\partial {s}_i}^T \tilde{W}_\mathrm {u_i}\frac{\bar{\omega }_i^T W_i}{{(\omega _i^T\omega _i+1)}} \right. \nonumber \\&\quad \left. +\,\sum _{j\in {\mathcal {N}}_i}d_j^2\frac{\partial \phi _j}{\partial {s}_j}\begin{bmatrix}\mathbf {0}&\mathbf {0}\\ \mathbf {0}&\mathbf {I}\end{bmatrix}\frac{\partial \phi _j}{\partial {s}_j}^T W_j\frac{\bar{\omega }_i^T W_j}{{(\omega _i^T\omega _i+1)}} \right. \nonumber \\&\quad \left. -\,\sum _{j\in {\mathcal {N}}_i}d_j^2\frac{\partial \phi _j}{\partial {s}_j}\begin{bmatrix}\mathbf {0}&\mathbf {0}\\ \mathbf {0}&\mathbf {I}\end{bmatrix}\frac{\partial \phi _j}{\partial {s}_j}^T\tilde{W}_\mathrm {u_j}\frac{\bar{\omega }_i^T W_j}{{(\omega _i^T\omega _i+1)}} \bigg )\right. \nonumber \\&\quad \left. +\,\tilde{W}_\mathrm {v_i}^T\bigg (\frac{d_i^2}{\gamma _\mathrm {ii}^2}\frac{\partial \phi _i}{\partial {s}_i}\begin{bmatrix}\mathbf {0}&\mathbf {0}\\ \mathbf {0}&\mathbf {I}\end{bmatrix}\frac{\partial \phi _i}{\partial {s}_i}^T W_i\frac{\bar{\omega }_i^T W_i}{{(\omega _i^T\omega _i+1)}} \right. \nonumber \\&\quad \left. -\,\frac{d_i^2}{\gamma _\mathrm {ii}^2}\frac{\partial \phi _i}{\partial {s}_i}\begin{bmatrix}\mathbf {0}&\mathbf {0}\\ \mathbf {0}&\mathbf {I}\end{bmatrix}\frac{\partial \phi _i}{\partial {s}_i}^T \tilde{W}_\mathrm {v_i}\frac{\bar{\omega }_i^T}{{(\omega _i^T\omega _i+1)}} W_i\right. \nonumber \\&\quad \left. +\,\sum _{j\in {\mathcal {N}}_i}\frac{d_j^2 \gamma _\mathrm {ij}^2}{\gamma _\mathrm {jj}^4}\frac{\partial \phi _j}{\partial {s}_j}\begin{bmatrix}\mathbf {0}&\mathbf {0}\\ \mathbf {0}&\mathbf {I}\end{bmatrix}\frac{\partial \phi _j}{\partial {s}_j}^T W_j\frac{\bar{\omega }_i^T W_j}{{(\omega _i^T\omega _i+1)}} \right. \nonumber \\&\quad \left. -\,\sum _{j\in {\mathcal {N}}_i}\frac{d_j^2 \gamma _\mathrm {ij}^2}{\gamma _\mathrm {jj}^4}\frac{\partial \phi _j}{\partial {s}_j}\begin{bmatrix}\mathbf {0}&\mathbf {0}\\ \mathbf {0}&\mathbf {I}\end{bmatrix}\frac{\partial \phi _j}{\partial {s}_j}^T\tilde{W}_\mathrm {v_j}\frac{\bar{\omega }_i^T W_j}{{(\omega _i^T\omega _i+1)}} \bigg ) \right\| \leqslant b_\mathrm {i5}, \end{aligned}$$
(42)

where \(\tilde{u}_i=-d_i \left[ \begin{array}{l} \mathbf {0} \\ \mathbf {I} \end{array} \right] ^T (\frac{\partial \phi _i}{\partial {s}_i}^T \tilde{W}_\mathrm {u_i}+\frac{\partial \epsilon _\mathrm {c_i} }{\partial {s}_i})\), \(\tilde{v}_i=\frac{d_i}{\gamma _\mathrm {ii}^2} \left[ \begin{array}{l} \mathbf {0} \\ \mathbf {I} \end{array} \right] ^T (\frac{\partial \phi _i}{\partial {s}_i}^T \tilde{W}_\mathrm {v_i}+\frac{\partial \epsilon _\mathrm {c_i} }{\partial {s}_i})\), and \(u_i^*,\ v_i^*,\ \hat{u}_i,\ \hat{v}_i\) are given by (19), (20), (21), and (22), respectively. Inequalities (41) and (42) result from the fact that all the quantities that appear in the left-hand side have known definable bounds. Also all the residual errors \(\epsilon _\mathrm {c_i}\) that appear in \({u}_i^*\) [see (19)], \({v}_i^*\) [see (20)], and \(\tilde{u}_i\) and \(\tilde{v}_i\) (as shown above) can be reduced by increasing the number of basis functions. \(\square \)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vamvoudakis, K.G., Hespanha, J.P. Game-Theory-Based Consensus Learning of Double-Integrator Agents in the Presence of Worst-Case Adversaries. J Optim Theory Appl 177, 222–253 (2018). https://doi.org/10.1007/s10957-018-1268-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10957-018-1268-7

Keywords

Navigation