This article studies the adaptive optimal stationary control of continuous-time linear stochastic systems with both additive and multiplicative noises, using reinforcement learning techniques. Based on policy iteration, a novel off-policy algorithm, named optimistic least-squares-based is proposed, which able to find iteratively near-optimal policies problem directly from input/state data witho...

The adaptive hybrid function projective synchronization AHFPS of different chaotic systems with unknown time-varying parameters is investigated. Based on the Lyapunov stability theory and adaptive bounding technique, the robust adaptive control law and the parameters update law are derived to make the states of two different chaotic systems asymptotically synchronized. In the control strategy, ...

This article focuses on the fulfillment of persistent excitation (PE) condition for signals which result from transformations by means polynomials. is essential, e.g., convergence adaptive dynamic programming algorithms due to commonly used polynomial function approximators. As theoretical statements are scarce regarding nonlinear transformation PE signals, we propose conditions system state su...

Abstract Reinforcement learning (RL) is a promising tool for solving robust optimal well control problems where the model parameters are highly uncertain and system partially observable in practice. However, RL of policies often relies on performing large number simulations. This could easily become computationally intractable cases with intensive To address this bottleneck, an adaptive multigr...

This paper presents a distributed model reference adaptive control scheme for optimal tracking of an interconnected dynamical system in the presence system/interconnection uncertainties. A selection which achieves nominal is introduced by using linear quadratic regulator theory. Then law developed uncertain system, where it employs specified model. It shown that desired behaviour such output as...