Search Results for author:
Found 9 papers, 1 papers with code
Iterative distributed moving horizon estimation of linear systems with penalties on both system disturbances and noise
In this paper, partition-based distributed state estimation of general linear systems is considered.
Data-driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large-scale nonlinear processes
The local estimators are integrated via information exchange to form a distributed estimation scheme, which provides estimates of the unmeasured/unmeasurable state variables of the original nonlinear process in a linear manner.
Optimizing the switching operation in monoclonal antibody production: Economic MPC and reinforcement learning
Most of the processes for industrial mAb production rely on batch operations, which result in significant downtime.
Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustness
This work proposes a novel approach to RL training, called control invariant set (CIS) enhanced RL, which leverages the advantages of utilizing the explicit form of CIS to improve stability guarantees and sampling efficiency.
Control invariant set enhanced reinforcement learning for process control: improved sampling efficiency and guaranteed stability
This work proposes a novel approach to RL training, called control invariant set (CIS) enhanced RL, which leverages the benefits of CIS to improve stability guarantees and sampling efficiency.
SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments
Traditional biological and pharmaceutical manufacturing plants are controlled by human workers or pre-defined thresholds.
Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise
To this end, a sensitivity-based performance assessment approach is proposed to pre-evaluate the dynamic economic and tracking performance of them in this work.
Soil moisture map construction using microwave remote sensors and sequential data assimilation
Secondly, measurements obtained from the microwave sensors are assimilated into the field model using the extended Kalman filter to form an information fusion system, which will provide frequent soil moisture estimates and predictions in the form of moisture content maps.
Neural Collaborative Ranking
In this paper, we examine an alternative approach which does not assume that the non-interacted items are necessarily negative, just that they are less preferred than interacted items.
