Search Results for author:
Found 18 papers, 4 papers with code
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.
Partition-based distributed extended Kalman filter for large-scale nonlinear processes with application to chemical and wastewater treatment processes
In this paper, we address a partition-based distributed state estimation problem for large-scale general nonlinear processes by proposing a Kalman-based approach.
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.
Performance triggered adaptive model reduction for soil moisture estimation in precision irrigation
Soil moisture estimation based on limited soil moisture sensors is crucial for obtaining comprehensive soil moisture information when dealing with large-scale agricultural fields.
Reduced-order Koopman modeling and predictive control of nonlinear processes
In this paper, we propose an efficient data-driven predictive control approach for general nonlinear processes based on a reduced-order Koopman operator.
Performance Evaluation of Semi-supervised Learning Frameworks for Multi-Class Weed Detection
In this study, we assess the effectiveness of a semi-supervised learning framework for multi-class weed detection, employing two well-known object detection frameworks, namely FCOS and Faster-RCNN.
Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges
These models are trained on a large amount of data from multiple domains and modalities.
Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control
Connected and autonomous vehicles (CAVs) promise next-gen transportation systems with enhanced safety, energy efficiency, and sustainability.
Extended Neighboring Extremal Optimal Control with State and Preview Perturbations
In this work, an extended NE (ENE) framework is developed to systematically adapt the nominal control to both state and preview perturbations.
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.
Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy
By utilizing this representation, a generic subsystem decomposition method is proposed to partition the entire IES vertically based on the dynamic time scale and horizontally based on the closeness of interconnections between the operating units.
State estimation of a carbon capture process through POD model reduction and neural network approximation
Multi-layer perceptron (MLP) neural networks capture the dominant dynamics of the process and train the network parameters with low-dimensional data obtained from open-loop simulations.
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.
Sensor network design for post-combustion CO2 capture plants: economy, complexity and robustness
The sensor selection problem is converted to an optimization problem, and is efficiently solved by a one-by-one removal approach through sensitivity analysis.
A Transferable Multi-stage Model with Cycling Discrepancy Learning for Lithium-ion Battery State of Health Estimation
A transferable multi-stage SOH estimation model is proposed to perform TL across batteries in the same stage, consisting of four steps.
A sensitivity-based approach to optimal sensor selection for process networks
The minimum number of sensors is determined in a way such that the local sensitivity matrix is full column rank.
Economic model predictive control of integrated energy systems: A multi-time-scale framework
Subsequently, the CEMPC, which includes slow economic model predictive control (EMPC), medium EMPC and fast EMPC, is developed.
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.
