Health-Aware Model-Predictive Control of a Cooperative AGV-Based Production System

In the paper, a new scheduling strategy for assembly systems consisting of cooperating Automated Guided Vehicles (AGVs) based on their remaining operational time is developed. The operational time is associated with state of charge and state of health of the AGV battery. While the latter is defined as a possible number of repetitions of a set of given tasks, both are impossible to measure on-line directly with conventional sensors. Therefore, a novel state-of-charge estimator is proposed, which uses battery current and voltage sensor readings. In contrast to the approaches presented in the literature, a comprehensive analysis of its convergence is provided. Subsequently, a state-of-health predictor is developed. With the above measures, a new control strategy for cooperative AGVs is proposed. It is achieved by the allocation of alternative tasks among two cooperating robots referring to the state of the accomplished tasks from the previous stage of the assembly process. The proposed method allows a predictive control of assembly processes with several constraints, e.g., productivity of each assembly station, speed of the communication, or operation capability of the robots involved in the assembling process. The final part of the paper shows an experimental study exhibiting the performance of the proposed approach.

[1]  Bo-Suk Yang,et al.  Intelligent prognostics for battery health monitoring based on sample entropy , 2011, Expert Syst. Appl..

[2]  Noureddine Zerhouni,et al.  Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction , 2016, J. Intell. Manuf..

[3]  Jeffrey M. Alden,et al.  Agile manufacturing systems in the automotive industry , 2004 .

[4]  Delphine Riu,et al.  A review on lithium-ion battery ageing mechanisms and estimations for automotive applications , 2013 .

[5]  A. Hamrol.  A new look at some aspects of maintenance and improvement of production processes , 2018 .

[6]  Moshe Kam,et al.  Mathematical Programming Approaches for Multi-Vehicle Motion Planning: Linear, Nonlinear, and Mixed Integer Programming , 2013, Found. Trends Robotics.

[7]  B. De Schutter,et al.  Modelling and control of discrete event systems using switching max-plus-linear systems , 2004 .

[8]  Giorgio Rizzoni,et al.  A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control , 2015 .

[9]  Shengbo Eben Li,et al.  Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles , 2015 .

[10]  Yaguo Lei,et al.  Machinery health prognostics: A systematic review from data acquisition to RUL prediction , 2018 .

[11]  Beata Mrugalska.  A bounded-error approach to actuator fault diagnosis and remaining useful life prognosis of Takagi-Sugeno fuzzy systems. , 2018, ISA transactions.

[12]  Michael Pecht,et al.  A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors , 2018, Journal of Power Sources.

[13]  Stéphane Lafortune,et al.  Active fault tolerant control of discrete event systems using online diagnostics , 2011, Autom..

[14]  Iryna Snihir,et al.  Battery open-circuit voltage estimation by a method of statistical analysis , 2006 .

[15]  Lin Ma,et al.  Prognostic modelling options for remaining useful life estimation by industry , 2011 .

[16]  Douglas E. Adams,et al.  Health monitoring of structural materials and components : methods with applications , 2007 .

[17]  Satoshi Hoshino,et al.  Development of a Flexible and Agile Multi-robot Manufacturing System , 2008 .

[18]  Beata Mrugalska,et al.  Towards Enhanced Performance of Neural-Network-Based Fault Detection Using an Sequential D-Optimum Experimental Design , 2018, Applied Sciences.

[19]  P. Butkovic.  Max-linear Systems: Theory and Algorithms , 2010 .

[20]  Dirk Uwe Sauer,et al.  Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries , 2013 .

[21]  Tarek Raïssi,et al.  Set-membership methodology for model-based prognosis. , 2017, ISA transactions.

[22]  I. Villarreal,et al.  Critical review of state of health estimation methods of Li-ion batteries for real applications , 2016 .

[23]  Bart De Schutter,et al.  Model predictive control for max-plus-linear discrete event systems , 2001, Autom..

[24]  Ralf Stetter,et al.  Towards Robust Predictive Fault-Tolerant Control For A Battery Assembly System , 2015, Int. J. Appl. Math. Comput. Sci..

[25]  Tongdan Jin,et al.  Near-extreme system condition and near-extreme remaining useful time for a group of products , 2017, Reliab. Eng. Syst. Saf..

[26]  Stephan M. Wagner,et al.  Decision model for the application of just-in-sequence , 2011 .

[27]  Noureddine Zerhouni,et al.  A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models , 2012, IEEE Transactions on Reliability.

[28]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..

[29]  Zbigniew Antoni Banaszak,et al.  The Performance Evaluation Tool for Automated Prototyping of Concurrent Cyclic Processes , 2004, Fundam. Informaticae.

[30]  Linxia Liao,et al.  Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction , 2014, IEEE Transactions on Reliability.

[31]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[32]  Michael Osterman,et al.  Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me , 2011 .

[33]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[34]  Hao Yuan,et al.  Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus , 2018, IEEE Transactions on Vehicular Technology.

[35]  Joseph Mathew,et al.  Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .

[36]  Afshin Izadian,et al.  Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries , 2015, IEEE Transactions on Industrial Electronics.

[37]  Geert Jan Olsder,et al.  Synchronization and Linearity: An Algebra for Discrete Event Systems , 1994 .

[38]  Xiangyu Kong,et al.  Online updating with a wiener-process-based prediction model using UKF algorithm for remaining useful life estimation , 2014, 2014 Prognostics and System Health Management Conference (PHM-2014 Hunan).

[39]  Gautam Biswas,et al.  Methodologies for system-level remaining useful life prediction , 2016, Reliab. Eng. Syst. Saf..

[40]  Zijian Mao,et al.  Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach , 2017 .

[41]  Peng Chen,et al.  New Particle Filter Based on GA for Equipment Remaining Useful Life Prediction , 2017, Sensors.

[42]  Shankar Sankararaman,et al.  Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction , 2015 .

[43]  Ying Peng,et al.  Current status of machine prognostics in condition-based maintenance: a review , 2010 .

[44]  Ralf Stetter,et al.  Model-Based Requirements Management in Gear Systems Design Based On Graph-Based Design Languages , 2017 .

[45]  Lothar Seybold,et al.  A fault-tolerant approach to the control of a battery assembly system , 2016, 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR).

[46]  Göran Lindbergh,et al.  A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation , 2014 .

[47]  Eric Diller.  Mathematical Programming Approaches for Multi-Vehicle Motion Planning: Linear, Nonlinear, and Mixed Integer Programming , 2011 .

[48]  Lifeng Xi,et al.  Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods , 2007 .

[49]  P. Lall,et al.  Prognostics and health management of electronics , 2006, 2006 11th International Symposium on Advanced Packaging Materials: Processes, Properties and Interface.

[50]  Baocang Ding,et al.  Dynamic Output Feedback Predictive Control for Nonlinear Systems Represented by a Takagi–Sugeno Model , 2011, IEEE Transactions on Fuzzy Systems.

[51]  David He,et al.  A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology , 2007 .

[52]  Jong-Myon Kim,et al.  A Reliable Health Indicator for Fault Prognosis of Bearings , 2018, Sensors.

[53]  Michael Buchholz,et al.  On-board state-of-health monitoring of lithium-ion batteries using linear parameter-varying models , 2013 .

[54]  Giorgio Battistelli,et al.  Design of state estimators for uncertain linear systems using quadratic boundedness , 2006, Autom..

[55]  S. Onori,et al.  Advanced battery management system design for SOC / SOH estimation for e-bikes applications , 2015 .

[56]  Liang Guo,et al.  A recurrent neural network based health indicator for remaining useful life prediction of bearings , 2017, Neurocomputing.

[57]  Keith Worden,et al.  An introduction to structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[58]  Xiaolin Tang,et al.  Nonlinear Fractional-Order Estimator With Guaranteed Robustness and Stability for Lithium-Ion Batteries , 2018, IEEE Transactions on Industrial Electronics.

[59]  Markus Till,et al.  Digital representation of product functions in multicopter design , 2017 .

[60]  Ralf Stetter,et al.  Intelligent steering system for electrical power trains , 2010, 2010 Emobility - Electrical Power Train.

[61]  Junmin Wang,et al.  Autonomous ground vehicle control system for high-speed and safe operation , 2008, 2008 American Control Conference.

[62]  Michael Buchholz,et al.  Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods , 2013 .

[63]  Bo-Suk Yang,et al.  Estimation and forecasting of machine health condition using ARMA/GARCH model , 2010 .

[64]  Eam Khwang Teoh,et al.  Fuzzy speed and steering control of an AGV , 2002, IEEE Trans. Control. Syst. Technol..

[65]  Qi Li,et al.  Progress in electrolytes for rechargeable Li-based batteries and beyond , 2016 .

[66]  Feixiang Wu,et al.  Li-ion battery materials: present and future , 2015 .

[67]  Selin Aviyente,et al.  Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings , 2015, IEEE Transactions on Industrial Electronics.

[68]  Michael G. Pecht,et al.  A prognostics and health management roadmap for information and electronics-rich systems , 2010, Microelectron. Reliab..

[69]  Michael A. Osborne,et al.  Gaussian process regression for forecasting battery state of health , 2017, 1703.05687.

[70]  M. V. Iordache,et al.  Diagnosis and Fault-Tolerant Control , 2007, IEEE Transactions on Automatic Control.

[71]  Ralf Stetter,et al.  Mechatronics Engineering on the Example of an Innovative Production Vehicle , 2009 .

[72]  Noureddine Zerhouni,et al.  Health assessment and life prediction of cutting tools based on support vector regression , 2015, J. Intell. Manuf..

[73]  Joseph Mathew,et al.  A review on prognostic techniques for non-stationary and non-linear rotating systems , 2015 .

[74]  Jay Lee,et al.  Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility , 2014 .

[75]  Ralf Stetter,et al.  Virtual Diagnostic Sensors Design for an Automated Guided Vehicle , 2018 .

[76]  K. Goebel,et al.  Prognostics in Battery Health Management , 2008, IEEE Instrumentation & Measurement Magazine.

[77]  Dongpu Cao,et al.  Condition Monitoring in Advanced Battery Management Systems: Moving Horizon Estimation Using a Reduced Electrochemical Model , 2018, IEEE/ASME Transactions on Mechatronics.

[78]  B. Ding,et al.  Constrained robust model predictive control via parameter-dependent dynamic output feedback , 2010, Autom..

[79]  Jae Sik Chung,et al.  A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation , 2010 .