Recently, stochastic models appropriate for long-range dependent phenomena have attracted a great deal of interest and numerous theoretical results and successful applications have been already reported. In particular, several contributions in the literature have been devoted to the extension of the classical theory of continuoustime stochastic systems driven by Brownian motions to analogues in...

This paper presents the optimal regulator for a linear system with multiple time delays in control input and a quadratic criterion. The optimal regulator equations are obtained using the duality principle, which is applied to the optimal filter for linear systems with multiple time delays in observations. Performance of the obtained optimal regulator is verified in the illustrative example agai...

This paper presents the optimal regulator for a linear system with time delay in control input and a quadratic criterion. The optimal regulator equations are obtained using the duality principle, which is applied to the optimal filter for linear systems with time delay in observations. Performance of the obtained optimal regulator is verified in the illustrative example against the best linear ...

The main goal of this work is to investigate the approximate controllability for a class non-autonomous delayed integrodifferential inclusions with unbounded delay. Our technique starts search optimal control linear quadratic regulator problem. existence such an aids establish sufficient conditions insuring our inclusion problem's controllability. findings we acquired represent generalization a...

We compare emissions taxes and quotas when a (strategic) regulator and (non-strategic) firms have asymmetric information about abatement costs, and all agents use Markov perfect decision rules. Firms make investment decisions that affect their future abatement costs. For general functional forms, firms’ investment policy is information-constrained efficient when the regulator uses a quota, but ...

Stochastic optimal control usually requires an explicit dynamical model with probability distributions, which are difficult to obtain in practice. In this work, we consider the linear quadratic regulator (LQR) problem of unknown systems and adopt a Wasserstein penalty address distribution uncertainty additive stochastic disturbances. By constructing equivalent deterministic game penalized LQR p...

Model-free reinforcement learning attempts to find an optimal control action for unknown dynamical system by directly searching over the parameter space of controllers. The convergence behavior and statistical properties these approaches are often poorly understood because nonconvex nature underlying optimization problems lack exact gradient computation. In this article, we take a step toward d...

A quadcopter control system is a fundamentally difficult and challenging problem because its dynamics modelling highly nonlinear, especially after accounting for the complicated aerodynamic effects. Plus, variables are interdependent coupled in nature. There six controllers studied analysed this work which (1) Proportional–Integral–Derivative (PID), (2) Proportional-Derivative (PD), (3) Linear ...