Analysis and Control of Nonlinear Stochastic Systems

We are working on the analysis and synthesis of stochastic systems. Because it is often challenging to obtain precise models of dynamical systems, stochastic systems are one of the suitable approaches to deal with uncertain systems. Stochastic systems can also be used to model large or complex systems, which include non-engineering systems, such as biological, social, and economic systems. We pursue control of nonlinear stochastic systems, which include systems described by stochastic differential equations, and develop new methodologies to deal with both of the stochasticity and nonlinearity. In addition, focusing on stochastic perspectives, we also pursue the data-driven modeling of dynamical systems and study stochastic dynamical problems appearing in machine learning problems.



Figure: Illustration of Stochastic Control Systems


Figure: Application of Stochastic Control to Drone

■Keywords

Stochastic systems, Nonlinear control systems, Stochastic control, Learning theory

References

  • K. Hoshino, Y. Nishimura, and Y. Yamashita. Convergence Rates of Stochastic Homogeneous Systems. Systems & Control Letters, Vol. 124, February 2019, pp. 33-39 (2019) DOI:10.1016/j.sysconle.2018.11.013
  • K. Hoshino and Y. Nishimura. Strong Solutions of Stochastic Differential Equations in Finite-Time Stabilization. IFAC-PapersOnLine, Vol. 51, Issue 13, pp. 266-271 (2018) 10.1016/j.ifacol.2018.07.289
  • K. Hoshino, Y. Nishimura, Y. Yamashita, and D. Tsubakino. Global Asymptotic Stabilization of Nonlinear Deterministic Systems Using Wiener Processes. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol. 61, No. 8, pp. 2318-2323 (2016) DOI:10.1109/TAC.2015.2495622
  • K. Hoshino, Y. Nishimura, Y. Yamashita, and D. Tsubakino. Homogeneous Stabilization of Driftless Input-Affine Systems Using Wiener Processes. Proceedings of the 53rd Conference on Decision and Control , pp. 3167-3172 (2014) DOI:10.1109/CDC.2014.7039878