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Design of an Intelligent Monitoring System for Operating States in a Coal Mine Drilling Process 41

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Design of an Intelligent Monitoring System for Operating Sta...

第41届中国控制会议

作者： Xiao Yang Luefeng Chen Sheng Du Chengda Lu Wangnian Li Min Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Xi'an Research Institute Co.Ltd. China Coal Technology and Engineering Group

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development trend of intelligent drilling in coal mines,an intelligent monitoring system for operating states in a drilling process is developed to solve the problems of incomplete display of drilling parameters and ineffective *** system realizes the remote monitoring of drilling *** mainly adopts the Fuzzy C-Means clustering algorithm to cluster the drilling *** it combines expert empirical knowledge to classify coal seams into soft,hard and alternating soft and hard coal *** boundary values are calculated according to the boundary ***,three drilling operating states,rated,conservative and over-excited,are judged to provide guidance for the drilling process control.

关键词： Drilling process Intelligent monitoring Fuzzy C-Means clustering Operating states

Dynamic Collision Avoidance Using Velocity Obstacle-Based control Barrier Functions

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arXiv 2025年

作者： Huang, Jihao Zeng, Jun Chi, Xuemin Sreenath, Koushil Liu, Zhitao Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Institute of Intelligence Science and Engineering Shenzhen Polytechnic University Shenzhen China Hybrid Robotics Group The Department of Mechanical Engineering UC Berkeley United States

Designing safety-critical controllers for acceleration-controlled unicycle robots is challenging, as control inputs may not appear in the constraints of control Lyapunov functions (CLFs) and control barrier functions (CBFs), leading to invalid controllers. Existing methods often rely on state-feedback-based CLFs and high-order CBFs (HOCBFs), which are computationally expensive to construct and fail to maintain effectiveness in dynamic environments with fast-moving, nearby obstacles. To address these challenges, we propose constructing velocity obstacle-based CBFs (VOCBFs) in the velocity space to enhance dynamic collision avoidance capabilities, instead of relying on distance-based CBFs that require the introduction of HOCBFs. Additionally, by extending VOCBFs using variants of VO, we enable reactive collision avoidance between robots. We formulate a safety-critical controller for acceleration-controlled unicycle robots as a mixed-integer quadratic programming (MIQP), integrating state-feedback-based CLFs for navigation and VOCBFs for collision avoidance. To enhance the efficiency of solving the MIQP, we split the MIQP into multiple sub-optimization problems and employ a decision network to reduce computational costs. Numerical simulations demonstrate that our approach effectively guides the robot to its target while avoiding collisions. Compared to HOCBFs, VOCBFs exhibit significantly improved dynamic obstacle avoidance performance, especially when obstacles are fast-moving and close to the robot. Furthermore, we extend our method to distributed multi-robot systems. Copyright © 2025, The Authors. All rights reserved.

关键词： State feedback

GLOBALLY CONVERGENT HOMOTOPIES FOR DISCRETE-TIME OPTIMAL control

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arXiv 2023年

作者： Esterhuizen, Willem Flaβkamp, Kathrin Hoffmann, Matthias Worthmann, Karl Systems Modeling and Simulation Systems Engineering Universität des Saarlandes Germany Optimization-based Control Group Institute of Mathematics Technische Universität Ilmenau Germany

Homotopy methods are attractive due to their capability of solving difficult optimisation and optimal control problems. The underlying idea is to construct a homotopy, which may be considered as a continuous (zero) curve between the difficult original problem and a related, comparatively easy one. Then, the solution of the easier one is continuously perturbed along the zero curve towards the sought-after solution of the original problem. We propose a methodology for the systematic construction of such zero curves for discrete-time optimal control problems drawing upon the theory of globally convergent homotopies for nonlinear programs. The proposed framework ensures that for almost every initial guess at a solution there exists a suitable homotopy path that is, in addition, numerically convenient to track. We demonstrate the results by solving optimal path planning problems for a linear system and the nonlinear nonholonomic car (Dubins’ vehicle).MSC Codes 49K15, 49M99, 90C30, 93B40, 93C55 © 2023, CC BY-NC-ND.

关键词： Discrete time control systems

Stochastic Online Optimization for Cyber-Physical and Robotic systems

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arXiv 2024年

作者： Ma, Hao Zeilinger, Melanie Muehlebach, Michael Learning and Dynamical Systems Group Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen Germany Department of Mechanical and Process Engineering Institute for Dynamic Systems and Control Leonhardstrasse 21 Zürich8092 Switzerland

We propose a novel gradient-based online optimization framework for solving stochastic programming problems that frequently arise in the context of cyber-physical and robotic systems. Our problem formulation accommodates constraints that model the evolution of a cyber-physical system, which has, in general, a continuous state and action space, is nonlinear, and where the state is only partially observed. We also incorporate an approximate model of the dynamics as prior knowledge into the learning process and show that even rough estimates of the dynamics can significantly improve the convergence of our algorithms. Our online optimization framework encompasses both gradient descent and quasi-Newton methods, and we provide a unified convergence analysis of our algorithms in a non-convex setting. We also characterize the impact of modeling errors in the system dynamics on the convergence rate of the algorithms. Finally, we evaluate our algorithms in simulations of a flexible beam, a four-legged walking robot, and in real-world experiments with a ping-pong playing robot. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

Model-based Optimization for Laboratory-Scale Upstream Processing in Monoclonal Antibody Production

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IFAC-PapersOnLine 2025年第1期59卷 403-408页

作者： Guilherme A. Pimentel Camilo Garcia-Tenorio Radhouane Fekih-Salem Cédric Toussaint Ahmed Kanfoud Thomas Cornet Thibault Helleputte Adrien Boes Riccardo Marega Alain Vande Wouwer Laurent Dewasme Systems EstimationControl and Optimization Group University of Mons Belgium DNAlytics Ottignies-Louvain-la-Neuve Belgium Immunobiology Laboratory CER Groupe Belgium University of Tunis El Manar National Engineering School of Tunis LAMSIN Tunisia

The production of monoclonal antibodies (mAbs) is a complex pharmacological process involving upstream and downstream chains, focusing on mAbs production and purification, respectively. This paper presents a process optimization based on an end-to-end mechanistic model for the upstream process chain to enhance the production of mAbs. Upstream processing considers cell expansion, cell production, and primary harvesting steps by filtration. The optimization results offer users the optimal initial concentrations of the medium in the bioreactors, the duration of the cultures, the feed flow rate profiles, and the permeate flow profile in the filtration operation. Simulations incorporating interconnected reactors and process constraints led to optimized initial media concentrations and feed flow profiles, resulting in a 25% increase in mAb production compared to baseline conditions.

关键词： process optimization monoclonal antibodies upstream processing mechanistic modeling

REINFORCEMENT LEARNING-BASED MODEL PREDICTIVE control FOR GREENHOUSE CLIMATE control

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arXiv 2024年

作者： Mallick, Samuel Airaldi, Filippo Dabiri, Azita Sun, Congcong De Schutter, Bart Delft Center for Systems and Control Delft University of Technology Delft2628 CD Netherlands Agricultural Biosystems Engineering Group Wageningen University Wageningen6700 AA Netherlands

Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inputs, while enforcing physical constraints. However, prediction models for greenhouse systems are inherently inaccurate due to the complexity of the real system and the uncertainty in predicted weather profiles. For model-based control approaches such as MPC, this can degrade performance and lead to constraint violations. Existing approaches address uncertainty in the prediction model with robust or stochastic MPC methodology;however, these necessarily reduce crop yield due to conservatism and often bear higher computational loads. In contrast, learning-based control approaches, such as reinforcement learning (RL), can handle uncertainty naturally by leveraging data to improve performance. This work proposes an MPC-based RL control framework to optimize the climate control performance in the presence of prediction uncertainty. The approach employs a parametrized MPC scheme that learns directly from data, in an online fashion, the parametrization of the constraints, prediction model, and optimization cost that minimizes constraint violations and maximizes climate control performance. Simulations show that the approach can learn an MPC controller that significantly outperforms the current state-of-the-art in terms of constraint violations and efficient crop growth. © 2024, CC BY.

关键词： Predictive control systems

Ground Testing of Spacecraft Attitude Dynamics during In-Space Assembly and Manufacturing

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Ground Testing of Spacecraft Attitude Dynamics during In-Spa...

AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Bhundiya, Harsh G. Cordero, Zachary C. Marshall, Michael A. Sternberg, David Lo, Kevin Mohan, Swati Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA United States Guidance and Control Analyst Space Exploration Sector The Johns Hopkins University Applied Physics Laboratory LaurelMD United States Payload/Instrument Pointing Control Systems Engineering Group Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States GNC Hardware and Testbed Development Group Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The on-orbit construction of structures, i.e., in-space assembly and manufacturing (ISAM), is a promising approach for increasing mission flexibility and achieving structures optimized for the space environment. Although many construction processes have been proposed, a critical aspect that needs further investigation is the spacecraft attitude dynamics during ISAM given the large changes in mass properties of the spacecraft and the long timescales of fabrication. This work investigates the ground testing of spacecraft attitude dynamics during Bend-Forming, a candidate process for the on-orbit construction of truss structures via plastic deformation of metallic feedstock. Specifically, we conduct experiments on a planar air bearing platform and develop rigid body dynamics models to understand the platform motion during Bend-Forming. The experimental results reveal 1) the effect of metal spring back on the attitude dynamics, and 2) the possibility of control-structure interaction during fabrication with closed-loop, thruster-based position and attitude control. Overall, this study highlights how artifacts of the manufacturing process like spring back affect attitude dynamics and results in important guidelines for the ground testing of spacecraft attitude dynamics during ISAM. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Trusses

Computationally Efficient Predictive control Based on ANN State-Space Models

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Computationally Efficient Predictive Control Based on ANN St...

IEEE Conference on Decision and control

作者： Jan H. Hoekstra Bence Cseppento Gerben I. Beintema Maarten Schoukens Zsolt Kollár Roland Tóth Dept. of Electrical Engineering at the Eindhoven Control Systems group University of Technology The Netherlands Dept. of Measurement and Information Systems Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Budapest Hungary

Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for the identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), the resulting nonlinear optimization problem is often overly complex due to the size of the network, requires the use of high-order observers to track the states of the ANN model, and the overall control scheme does not exploit the available autograd tools for these models. In this paper, we propose an efficient approach to auto-convert ANN statespace models to linear parameter-varying (LPV) form and solve predictive control problems by successive solutions of linear model predictive problems, corresponding to quadratic programs (QPs). Furthermore, we show how existing deep-learning methods, such as SUBNET that uses a state encoder, enable efficient implementation of MPCs on identified ANN models. Performance of the proposed approach is demonstrated by a simulation study on an unbalanced disc system.

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Beyond Common Randomness: Quantum Resources in Decentralized control

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Beyond Common Randomness: Quantum Resources in Decentralized...

IEEE Conference on Decision and control

作者： Shashank A. Deshpande Ankur A. Kulkarni Department of Aeronautics and Astronautics DINaMo group Massachusetts Institute of Technology Department of Systems and Control Engineering Indian Institute of Technology Bombay Mumbai

Ananthram and Borkar [1] showed that there exist strategies that are consistent with the requirements of a decentralized information structure but are unattainable through the use of common randomness. This opens the question of discovering physically realisable mechanisms that provide access to this region of the strategic space. In our previous work we introduced a class of quantum strategies that allow such access in a two-agent setting. In this paper, we consider the problem of optimal allocation of a $k$ -partite quantum resource amongst $n$ agents, $k < n$ . We study the problem of decentralized estimation of a binary source by agents that are informed through independent binary symmetric channels, and face a cost that is homogeneous in their actions. We show a $k$ -partite quantum resource produces the maximum advantage over classical strategies when allocated to the agents with the $k$ most reliable channels.

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