State estimators, including observers and Bayesian filters, are a class of model-based
algorithms for estimating variables in a dynamical system given the sensor measurements of …

In the real world, many systems can be written as multiple-input multiple-output (MIMO)
systems, such as unmanned aerial vehicles (UAVs), unmanned vehicles, etc. Therefore, this …

Exponentially weighted moving average (EWMA) controllers have been extensively studied
for run-to-run (RtR) control in semiconductor manufacturing processes. However, the EWMA …

Deep reinforcement learning (DRL) has been preliminarily applied to run-to-run (RtR)
control. However, the existing works have mainly conducted on shift and drift disturbances in …

Mixed-product production is common practice in semiconductor manufacturing and has
attracted considerable academic attention recently. The main purpose of the mixed-product …

This study investigates a deep reinforcement learning (DRL)-assisted double exponentially
weighted moving average (dEWMA) controller for run-to-run (RtR) control in the …

A nonlinear passive observer (NPO) for estimating the time-varying velocity vector of a multi-
axis high-precision motion control stage is presented. The proposed nonlinear estimation …

This study aims to develop a weight-adjustment scheme for a double exponentially weighted
moving average (dEWMA) controller using deep reinforcement learning (DRL) techniques …

Phase shift control is one of the most common strategies used for the control of a triple active
bridge (TAB) converter, including single-phase shift (SPS) control, extended-phase shift …

In this paper, a discrete active disturbance rejection control (DADRC) scheme is proposed to
manipulate linear input–output systems with zero dynamics. The DADRC is formulated …