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17th IFAC Symposium on Control of Transportation systems (CTS)

作者： Ganeshan, Reyshwanth Darbha, Swaroop Texas A&M Univ J Mike Walker 66 Dept Mech Engn College Stn TX 77843 USA

To safely introduce any level of autonomy to trucks, the health of their brake systems needs to be monitored continuously. Out-of-adjustment push rods and leakages in the air brake system are two major reasons for increased braking distances in trucks, and result in safety violations. Air leakages can occur due to small cracks or loose/improperly fit couplings which do not affect the overall braking capacity but contribute greatly to increasing the braking lag and reducing the maximum braking torque at the wheels. Similarly, an increased stroke of push rod leads to a larger delay in brake response and a smaller value of the brake torque at the wheels. Currently, the condition of an air brake system is inspected manually by measuring the push rod offset, checking the couplings and hoses of the system for air leakages. These inspections are highly labor intensive, subjective, time consuming and do not accurately quantify how adversely the braking system is affected. Having an on-board diagnostic device that can monitor the health of air brakes would be crucial in the prevention of road accidents, especially when considering any level of automation and comply with FMSCA safety requirements. The focus of this paper is to aid the development of such a diagnostic system that facilitates enforcement and pre-trip inspections and continuous on-board monitoring of trucks through the development of a model for its multi-chamber braking system;this model can be used to estimate the severity of leakage and the push rod stroke using real time brake pressure transients. A machine learning algorithm for estimating the air leakage in the air brake system and its experimental corroboration is presented in this paper. Copyright (c) 2024 The Authors.

关键词： Intelligent Autonomous Vehicles Transportation systems Modelling Identification and Signal Processing adaptive and learning systems Commercial Vehicles

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Towards non-invasive quality monitoring and control of stem cell-derived pancreatic islet manufacturing 12

Towards non-invasive quality monitoring and control of stem ...

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12th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM)

作者： Singh, Rohan Orimi, Hamid Ebrahimi Pedabaliyarasimhuni, Praveen Kumar Raju Hoesli, Corinne Chioua, Moncef Polytech Montreal Dept Chem Engn 2500 Chemin Polytech Montreal PQ H3T 1J4 Canada McGill Univ Dept Chem Engn 845 Sherbrooke St Montreal H3A 0G4 PQ Canada Polytech Montreal Dept Chem Engn 2500 Chemin Polytech Montreal H3T 1J4 PQ Canada

Quality monitoring is important for biomanufacturing and often the methods used in laboratory setting do not translate well to industrial setting. In this regard, we present a non-invasive quality control method for classification of well differentiated cells from poorly differentiated ones that is scalable and can be used in online setting for adherent culture systems. The method was implemented on stage 4 of pluripotent stem cell differentiation into beta cells and we use textural analysis to extract features from phase contrast microscopic (PCM) images which were used to train our classifier that achieved an accuracy of 94.04 %. These preliminary findings show promise for its application in the area of processes monitoring in bio-reactors. Copyright (c) 2024 The Authors.

关键词： adaptive and learning systems Estimation and Robust Estimation Machine learning Assisted Modeling

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Neural network based adaptive control of gantry cranes 22

Neural network based adaptive control of gantry cranes

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Otto, E. Maksakov, A. Golovin, I. Palis, S. Otto von Guericke Univ D-39106 Magdeburg Germany IU Int Univ Appl Sci D-99084 Erfurt Germany

ISBN: (纸本)9781713872344

During the operation of large gantry cranes oscillations of flexible structures caused by the trolley movement lead to undesired wear of the construction and deteriorate the efficiency of the crane operation. Therefore, a neural network based adaptive control system for tracking control of the payload position and damping of oscillations is presented and validated in numerical simulations. By comparison to a previously derived nonlinear controller, it is shown that the proposed control law achieves good tracking and damping results with minimal a priori knowledge of the crane dynamics and without knowledge about specific plant parameters, therefore reducing the required modeling effort significantly.

关键词： adaptive and learning systems nonlinear adaptive control data-based control lyapunov methods system identification and adaptive control of distributed parameter systems

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Neural-adaptive Switching Control of Task-Space Objectives on Strict-Feedback Robot 22

Neural-Adaptive Switching Control of Task-Space Objectives o...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Doctolero, Samuel C. Westwick, David T. Univ Calgary Schulich Sch Engn Calgary AB Canada

ISBN: (纸本)9781713872344

Switching between tasks in robot applications is necessary for autonomous behaviour, requiring a control framework for switching between objectives. Thus, this paper proposes a switching control framework that allows transitions in position-dependent objectives for robot systems in strict-feedback form with adaptive capabilities. The proposed controller integrates artificial neural networks in two ways;one approximates control transforms while another approximates additive terms. Furthermore, the proposed method includes a dynamic damped inverse to ensure the invertible control transform matrices. The proposed controller is derived using the backstepping method and is stable in a Lyapunov sense. The proposed task-switching controller is first demonstrated on a generic robot manipulator. Then, a simulated two-degree-of-freedom manipulator with a camera at the end-effector tasked with position tracking and visual servoing verifies the capabilities of the proposed controller. A baseline switching controller shows that the proposed controller has superior performance, specifically, root mean square (RMS) error and control effort. Also, executing the simulation for multiple cycles shows that the proposed controller's artificial neural network and adaptive laws are *** (c) 2023 The Authors.

关键词： adaptive and learning systems Non-Linear Control systems Computational Intelligence in Control Robotics Control Design

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Fast identification and stabilization of unknown linear systems 22

Fast identification and stabilization of unknown linear syst...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Gramlich, Dennis Ebenbauer, Christian Rhein Westfal TH Aachen Chair Intelligent Control Syst D-52074 Aachen Germany

ISBN: (纸本)9781713872344

In the present work, a simple algorithm for stabilizing an unknown linear time-invariant system is proposed, assuming only that this system is stabilizable. The suggested algorithm is based on first performing a partial identification of the system and then stabilizing the controllable subsystem. It should be emphasized that our approach does not depend on any prior model knowledge and requires only a minimal number of samples. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： adaptive and learning systems Identification for control Input and excitation design Linear time-invariant systems Reinforcement learning control

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Generalized adaptive smoothing based neural network architecture for traffic state estimation 22

Generalized adaptive smoothing based neural network architec...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Yang, Chuhan Ramana, Ambadipudi Sai Venkata Jabari, Saif Eddin New York Univ Abu Dhabi Div Engn Abu Dhabi 129188 U Arab Emirates NYU Tandon Sch Engn New York NY 10003 USA

ISBN: (纸本)9781713872344

The adaptive smoothing method (ASM) is a standard data-driven technique used in traffic state estimation. The ASM has free parameters which, in practice, are chosen to be some generally acceptable values based on intuition. However, we note that the heuristically chosen values often result in un-physical predictions by the ASM. In this work, we propose a neural network based on the ASM which tunes those parameters automatically by learning from sparse data from road sensors. We refer to it as the adaptive smoothing neural network (ASNN). We also propose a modified ASNN (MASNN), which makes it a strong learner by using ensemble averaging. The ASNN and MASNN are trained and tested with two real-world datasets. Our experiments reveal that the ASNN and the MASNN outperform the conventional ASM.

关键词： adaptive smoothing method Neural networks Ensemble learning Transportation systems Intelligent Autonomous Vehicles adaptive and learning systems

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Fast identification and stabilization of unknown linear systems

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IFAC-PapersOnLine 2023年第2期56卷 6241-6246页

作者： Dennis Gramlich Christian Ebenbauer

关键词： adaptive and learning systems Identification for control Input and excitation design Linear time-invariant systems Reinforcement learning control

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Neural-adaptive Switching Control of Task-Space Objectives on Strict-Feedback Robot

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IFAC-PapersOnLine 2023年第2期56卷 9215-9220页

作者： Samuel C. Doctolero David T. Westwick Schulich School of Engineering University of Calgary Calgary Alberta Canada

Switching between tasks in robot applications is necessary for autonomous behaviour, requiring a control framework for switching between objectives. Thus, this paper proposes a switching control framework that allows transitions in position-dependent objectives for robot systems in strict-feedback form with adaptive capabilities. The proposed controller integrates artificial neural networks in two ways; one approximates control transforms while another approximates additive terms. Furthermore, the proposed method includes a dynamic damped inverse to ensure the invertible control transform matrices. The proposed controller is derived using the backstepping method and is stable in a Lyapunov sense. The proposed task-switching controller is first demonstrated on a generic robot manipulator. Then, a simulated two-degree-of-freedom manipulator with a camera at the end-effector tasked with position tracking and visual servoing verifies the capabilities of the proposed controller. A baseline switching controller shows that the proposed controller has superior performance, specifically, root mean square (RMS) error and control effort. Also, executing the simulation for multiple cycles shows that the proposed controller's artificial neural network and adaptive laws are stable.

关键词： adaptive and learning systems Non-Linear Control systems Computational Intelligence in Control Robotics Control Design

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Neural network based adaptive control of gantry cranes

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IFAC-PapersOnLine 2023年第2期56卷 8091-8096页

作者： E. Otto A. Maksakov I. Golovin S. Palis Otto von Guericke University Magdeburg 39106 Magdeburg Germany IU International University of Applied Sciences 99084 Erfurt Germany

关键词： adaptive and learning systems nonlinear adaptive control data-based control lyapunov methods system identification and adaptive control of distributed parameter systems

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Generalized adaptive smoothing based neural network architecture for traffic state estimation

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IFAC-PapersOnLine 2023年第2期56卷 3483-3490页

作者： Chuhan Yang Ambadipudi Sai Venkata Ramana Saif Eddin Jabari Division of Engineering New York University Abu Dhabi Abu Dhabi UAE - 129188 Tandon School of Engineering New York University NY USA

关键词： adaptive smoothing method Neural networks Ensemble learning Transportation systems Intelligent Autonomous Vehicles adaptive and learning systems

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