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3rd ifac Conference on Advances in Proportional-Integral-Derivative control (PID)

作者： Kuti, Jozsef Galambos, Peter Obuda Univ Antal Bejczy Ctr Intelligent Robot Becsi Ut 96-B H-1034 Budapest Hungary

This study investigates the computer-regulated propofol administration in anesthesia during medical interventions considering output feedback and robust PID control. The paper applies the Affine Tensor Product Model Transformation to derive the appropriate polytopic quasi-LPV representation of the closed-loop dynamics. This model form enables the use of LMI-based optimisation techniques to evaluate the closed loop performance. Despite the highly non-convex nature of this output feedback problem, the PID gains can be locally tuned through simplex optimisation. The proposed method provides a systematic way of tuning PIDcontrolled propofol administration for individual patients with theoretically established worst case performance measures. (C) 2018, ifac (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： modelling and control of biomedical systems control Design Optimal control Anesthesia Propofol administration

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Exploring Fuzzy Logic and Random Forest for Car Drivers' Fuel Consumption Estimation in IoT-Enabled Serious Games 14

Exploring Fuzzy Logic and Random Forest for Car Drivers' Fue...

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14th IEEE International symposium on Autonomous Decentralized systems (ISADS) / 3rd International Workshop on Service Assurance in System Wide Information Management (SASWIM)

作者： Massoud, Rana Bellotti, Francesco Berta, Riccardo De Gloria, Alessandro Poslad, Stefan Univ Genoa Dipartimento Ingn Navale Elettr Elettron & Teleco Elios Lab Genoa Italy Queen Mary Univ London QMUL Elect Engn IoT2US Lab London England

ISBN: (纸本)9781728116723

Internet of Things (IoT) technologies have a promising potential for instructional serious games related to field operations. We explore IoT's potential for serious games in the automotive application domain to improve driving, choosing fuel consumption (FC) as an indicator of the driver performance as it is strongly influenced by driving styles and can be quantified and validated. We propose a FC prediction model, exploiting three vehicular signals that are controllable by the driver (player), that are able to provide direct coaching feedback to the driver and are easily available through the widely available On-Board Diagnostic-II (OBD-II) vehicular interface: throttle position, engine rotation speed (RPM) and car speed. We processed the data with two techniques, random forest (RF) and fuzzy logic (FL). Implementation, training and testing of both models, were made using the enviroCar database which freely provides a significant amount of naturalistic drive data. Results show that RF achieves quite a higher estimation accuracy, which complements FL's ability to provide driver with easily understandable feedback. We thus argue that the combination of the two models can supply valuable information usable by game designers in the automotive environment.

关键词： IoT Serious Games user modelling random forest machine learning fuzzy logic fuel consumption On-Board Diagnostics-II (OBD-II)

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modelling and Torque control of an Non-Linear Friction Inverted Pendulum driven with a Rotary Series Elastic Actuator

Modelling and Torque Control of an Non-Linear Friction Inver...

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International symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Mohammad Javad Fotuhi Zied Ben Hazem Zafer Bingül Automation Research Laboratory Kocaeli University Turkey

Rotary Series Elastic Actuators (RSEA) are one of the most appropriate actuation methods for legged locomotion. RSEA systems have been shown a large shock tolerance and introduce additional dynamics behavior which are useful to achieve walking or running comportment. In this paper, a modelling and torque control analysis of a Rotary Series Elastic Actuator (RSEA) for an inverted pendulum with non-linear frictions are studied. Here, two different controllers; Torque PID+FF (Feed-Forward) controllers without friction estimation model and with friction estimation model were developed. The inputs of the two controllers are the deflection position errors and the rate of deflection position errors of the RSEA. The feedforward inverse model was used in the PID+FF controller. The feedforward technics and the integral action are used to reduce the steady state error of the control system. The PID parameters were optimized using a Genetic Algorithm (GL). In order to have more robust controller, a Non-Linear Friction Estimation Model (NLFEM) was used in PID+FF controller. The experimental results showed that the torque and trajectory tracking of RESA is satisfactory and overall the inverted pendulum stability has improved using the PID+FF with NLFEM.

关键词： Friction Torque Mathematical model DC motors Estimation Load modeling Legged locomotion

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H_∞ control of nonlinear systems with positive input with application to antiangiogenic therapy

H<sub>∞</sub> control of nonlinear systems with positive in...

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Joint Meeting of the 2nd ifac Workshop on Linear Parameter Varying systems (LPVS) / 9th ifac symposium on Robust control Design (ROCOND)

作者： Drexler, Daniel A. Sapi, Johanna Kovacs, Levente Obuda Univ Physiol Controls Res Ctr Univ Res Innovat & Serv Ctr Budapest Hungary

There are many systems in practice that can have only positive (nonnegative) input, typical examples for such systems are physiological systems. Moreover, the parameters of these systems are usually not known exactly or may vary over time, thus application of robust controllers represents a reasonable possibility. Most model-based controller design methods are developed for systems that can (or must) have negative and positive inputs as well, thus a dynamical extension is given here to the original system that ensures that the input of the original system is positive but the extended system can have negative input as well. The current paper investigates a robust control design method with positive input for an automatic therapy possibility in the case of antiangiogenic targeted molecular therapy using a recently published tumor growth model based on mice experiments. The extended system is transformed into an integrator series that is further modified using state-feedback to prepare the system for H-infinity norm-based controller synthesis. The simulations demonstrate the robustness of the controller and the positivity of the input. (C) 2018, ifac (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Positive systems Robust control Output feedback control biomedical system modeling and simulation Decision support and control

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Performance Visualization in Industrial systems for Informed Decision Making

Performance Visualization in Industrial Systems for Informed...

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16th ifac symposium on Information control Problems in Manufacturing (INCOM)

作者： Shah, L. A. Etienne, A. Siadat, A. Vernadat, F. B. COMSATS Inst Informat Technol Pk Rd Islamabad Islamabad Pakistan Ctr Metz Arts & Metiers ParisTech LCFC F-57070 Metz France Univ Metz LGIPM BP 45112 F-57073 Metz 3 France

Information visualization is a key component of many decision support tools in sciences and engineering. Graph is a visual construct that is widely used to model information for visualization. In this paper, a value-risk graph is proposed to visualize the results of value-risk based performance measurement systems (PMSs). The proposed graph for PMS divides the overall performance of industrial systems into distinct zones to facilitate the decision-making process. The upper bound, lower bound, and target value of each measure are decided by the performance evaluator and, then, transformed into normalized values using value theory principles. The aggregation of normalized measures along value and risk lines when combined defines "highly desirable", "feasible", "risky", and "unacceptable" zones. Scenario performance data when plotted on the graph visualize the overall performance of the system in terms of value and risk. The proposed decision-making value-risk graph is illustrated with an example dealing with manufacturing process design but it can be applied to any kind of system evaluation. (C) 2018, ifac (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Information Visualization Value Risk measures Performance Measurement systems Decision Support

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control THEORY APPLICATIONS TO OPERATIONS systems, SUPPLY CHAIN MANAGEMENT AND INDUSTRY 4.0 NETWORKS

CONTROL THEORY APPLICATIONS TO OPERATIONS SYSTEMS, SUPPLY CH...

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16th ifac symposium on Information control Problems in Manufacturing (INCOM)

作者： Dolgui, Alexandre Ivanov, Dmitry Sethi, Suresh Sokolov, Boris IMT Atlantique LS2N CNRS 4 Rue Alfred Kastler F-44300 Nantes France Berlin Sch Econ & Law Dept Business Adm Chair Int Supply Chain Management D-10825 Berlin Germany Univ Texas Dallas Naveen Jindal Sch Management Ctr Intelligent Supply Networks C4iSN Eugene McDermott Chair 800 W Campbell Rd Richardson TX 75080 USA RAS St Petersburg Inst Informat & Automat SPIIRAS VO 14 Line39 St Petersburg 199178 Russia ITMO Univ St Petersburg Russia

Uncertainty, feedback cycles and dynamics are major challenges in production and logistics systems, supply chains, and Industry 4.0 networks. From this perspective, control theory is an important research avenue in operations and supply chain management. We briefly survey the applications of control theory to engineering and management problems in supply chains and operations areas during the period 1960-2017. Our analysis is based on bridging the fundamentals of control and systems theory to supply chains and operations management. Although operations and supply chain systems resemble control systems, they have some peculiarities which do not allow a direct application of control theoretic methods. The required modifications and possible limitations are discussed. In this setting, further development of interdisciplinary approaches to supply chain optimization is argued. Copyright (C) 2018 ifac (C) 2018, ifac (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： supply chain operations Industry 4.0 dynamics planning scheduling control feedback optimal program control adaptation resilience

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A Multi Agent System architecture to implement Collaborative Learning for social industrial assets

A Multi Agent System architecture to implement Collaborative...

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16th ifac symposium on Information control Problems in Manufacturing (INCOM)

作者： Bakliwal, Kshitij Dhada, Maharshi Harshadbhai Palau, Adria Salvador Parlikad, Ajith Kumar Lad, Bhupesh Kumar Indian Inst Technol IIT Indore Khandwa Rd Indore 453552 Madhya Pradesh India Univ Cambridge Inst Mfg DIAL 17 Charles Babbage Rd Cambridge CB3 0FS England

The 'Industrial Internet of Things' aims to connect industrial assets with one another and benefit from the data that is generated, and shared, among these assets. In recent years, the extensive instrumentation of machines and the advancements in Information Communication Technologies are re-shaping the role of assets in our industrial systems. An emerging concept here is that of 'social assets': assets that collaborate with each other in order to improve system optimisation. Cyber-Physical systems (CPSs) are formed by embedding the assets with computers, or microcontrollers, which run real-time decision-making algorithms over the data originating from the asset. These are known as the 'Digital Twins' of the assets, and form the backbone of social assets. It is essential to have an architecture which enables a seamless integration of these technological advances for an industry. This paper proposes a Multi Agent System (MAS) architecture for collaborative learning, and presents the findings of an implementation of this architecture for a prognostics problem. Collaboration among assets is performed by calculating inter-asset similarity during operating condition to identify 'friends' and sharing operational data within these clusters of friends. The architecture described in this paper also presents a generic model for the Digital Twins of assets. Prognostics is demonstrated for the C-MAPSS turbofan engine degradation simulated data-set (Saxena and Goebel (2008)). (C) 2018, ifac (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Cyber-Physical systems Industrial Internet of Things Digital Twins Collaborative Learning Industry Automation Multi Agent systems Distributed Computing

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Intelligent Sliding Mode Scheme for Regenerative Braking control

Intelligent Sliding Mode Scheme for Regenerative Braking Con...

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Joint Meeting of the 2nd ifac Workshop on Linear Parameter Varying systems (LPVS) / 9th ifac symposium on Robust control Design (ROCOND)

作者： Rajendran, Sulakshan Spurgeon, Sarah Tsampardoukas, Georgios Hampson, Ric UCL Elect & Elect Engn Dept London England Jaguar Land Rover W-1-26 Abbey Rd Coventry CV3 4LF W Midlands England

controller design for an Anti-Lock Braking System (ABS) of a Hybrid Electric Vehicle (HEV) or Electric Vehicle (EV) is a challenging task because of the trade-off between braking efficiency and energy recuperation efficiency. In hybrid vehicles, the brake torque demand is met by both the conventional friction braking system and an electric Regenerative Braking System (RBS). Hence, an effective ABS controller is required to achieve high braking efficiency without losing energy recuperation efficiency. This paper presents an Intelligent Sliding Mode Scheme (ISMS) to retain high energy recuperation efficiency as well as good braking efficiency of an EV with a unique braking configuration. The ISMS has a supervisory logic based motor torque limiter and slip controller. The slip controller is designed based on a two-wheeled model which has a hydraulic unit at the front producing frictional braking cooperating with a regenerative braking system with a brake-by-wire unit at the rear wheels. The slip controller is designed considering the hydraulics and motor actuator dynamics and the complete Magic Formula (MF) is used for tyre force estimation. The logic-based torque limiter not only regulates the brake torque to follow an assigned brake force distribution but also ensures that the battery is not overcharged. (C) 2018, ifac (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： SMC ABS control Vehicle control Nonlinear control

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Classification of Alzheimer and MCI Phenotypes on MRI Data Using SVM 3rd

Classification of Alzheimer and MCI Phenotypes on MRI Data U...

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3rd International symposium on Signal Processing and Intelligent Recognition systems (SIRS)

作者： Kruthika, K. R. Rajeswari Pai, Akshay Maheshappa, H. D. Acharya Inst Technol Dept Elect & Commun Engn Bangalore Karnataka India Univ Copenhagen Dept Comp Sci Copenhagen Denmark

ISBN: (纸本)9783319679341;9783319679334

Alzheimer disease (AD) is a common form of dementia affecting people older than the age of 65. Moreover, AD is commonly diagnosed by behavioural paradormants, cognitive tests, and is followed by brain scans. Computer Aided Diagnosis (CAD), applies medical imaging and machine learning algorithms, to aid in the early diagnosis of Alzheimer's severity and advancement from prodromal stages i.e. Mild Cognitive Impairment (MCI) to diagnosed Alzheimer's disease. In this work, SVM (support vector machine) is used for dementia stage classification. Anatomical structures of the brain were obtained from FreeSurfer's processing of structural Magnetic Resonance Imaging (MRI) data and is utilized for as features for SVM. To be more precise, the system is processed using T1-weighted brain MRI datasets consisting of: 150 mild cognitive impairment (MCI) patients, 80 AD patients and 130 normal controls (NC) obtained from Alzheimer Disease Neuroimaging Initiative (ADNI) database. The volumes of brain structures (hippocampus, medial temporal lobe, whole brain, ventricular, cortical grey matter, entorhinal cortex and fusiform) are employed as biomarkers for multi-class classification of AD, MCI, and NC.

关键词： Alzheimer disease Mild cognitive impairment Normal control Structural magnetic resonance imaging FreeSurfer Machine learning SVM

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Robust multi-objective control of hybrid renewable microgeneration systems with energy storage

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APPLIED THERMAL ENGINEERING 2017年 114卷 1498-1506页

作者： Allison, John Univ Strathclyde Energy Syst Res Unit Dept Mech & Aerosp Engn James Weir Bldg75 Montrose St Glasgow G1 1XJ Lanark Scotland

Microgeneration technologies are positioned to address future building energy efficiency requirements and facilitate the integration of renewables into buildings to ensure a sustainable, energy-secure future. This paper explores the development of a robust multi-input multi-output (MIMO) controller applicable to the control of hybrid renewable microgeneration systems with the objective of minimising the electrical grid utilisation of a building while fulfilling the thermal demands. The controller employs the inverse dynamics of the building, servicing systems, and energy storage with a robust control methodology. These inverse dynamics provides the control system with knowledge of the complex cause and effect. relationships between the system, the controlled inputs, and the external disturbances, while an outer-loop control ensures robust, stable control in the presence of modelling deficiencies/uncertainty and unknown disturbances. Variable structure control compensates for the physical limitations of the systems whereby the control strategy employed switches depending on the current utilisation and availability of the energy supplies. Preliminary results presented for a system consisting of a micro-CHP unit, solar PV, and battery storage indicate that the control strategy is effective in minimising the interaction with the local electrical network and maximising the utilisation of the available renewable energy. (C) 2016 Published by Elsevier Ltd.

关键词： Microgeneration Multi-objective control Hybrid renewable energy systems Simulation Feedback linearisation Robust control

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