Deepak D. Ingole, Juraj Holaza, B. Takács, M. Kvasnica
Over the last decade, anesthesia research community witnessed numerous advances in controllers and their implementation platforms to control the depth of anesthesia (DoA) in a patient undergoing surgery. Today's operating theaters are extremely complex and crowded. New surgical techniques bring new medical technologies and more devices in the operation rooms, which often results in complex configurations, computer based control, and cable clutter. In an effort to reduce hardware size and to the improve quality control of anesthesia, we present a field programmable gate array (FPGA) based explicit model predictive control (EMPC) scheme which can take into account the control and state constraints that naturally arise in anesthesia. Real-time implementation of model predictive control (MPC), mainly requires solving an optimization problem at regular time intervals. We propose an FPGA-based EMPC-on-a-chip algorithm with customized 32-bit floating-point addition, substation, and multiplication algorithms. Simulation results with four compartmental PK-PD model, input constraints and a variable bispectral index (BIS) set-point are presented. The real-time simulation results are achieved with Xilinx's Vertex 4 XC4VLX25-10FF668 FPGA.
{"title":"FPGA-based explicit model predictive control for closed-loop control of intravenous anesthesia","authors":"Deepak D. Ingole, Juraj Holaza, B. Takács, M. Kvasnica","doi":"10.1109/PC.2015.7169936","DOIUrl":"https://doi.org/10.1109/PC.2015.7169936","url":null,"abstract":"Over the last decade, anesthesia research community witnessed numerous advances in controllers and their implementation platforms to control the depth of anesthesia (DoA) in a patient undergoing surgery. Today's operating theaters are extremely complex and crowded. New surgical techniques bring new medical technologies and more devices in the operation rooms, which often results in complex configurations, computer based control, and cable clutter. In an effort to reduce hardware size and to the improve quality control of anesthesia, we present a field programmable gate array (FPGA) based explicit model predictive control (EMPC) scheme which can take into account the control and state constraints that naturally arise in anesthesia. Real-time implementation of model predictive control (MPC), mainly requires solving an optimization problem at regular time intervals. We propose an FPGA-based EMPC-on-a-chip algorithm with customized 32-bit floating-point addition, substation, and multiplication algorithms. Simulation results with four compartmental PK-PD model, input constraints and a variable bispectral index (BIS) set-point are presented. The real-time simulation results are achieved with Xilinx's Vertex 4 XC4VLX25-10FF668 FPGA.","PeriodicalId":173529,"journal":{"name":"2015 20th International Conference on Process Control (PC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122405685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we focus on robust decentralized controller design for Benchmark PID problem brought recently by Morilla [5]: nonlinear drum boiler, having integration term and significant right half plane (RHP) zero in the interaction. We present the linearized uncertain model identified for the working area determined by working points. Robust decentralized controller is designed by Equivalent Subsystem Method approach, using Youla parametrization on subsystem level. We compare decentralized controller design with and without prescribing the designed controller structure. We derive the respective relations and filter, which guarantee the PI structure of controller. After controller design we check the robust stability condition for the determined working area. All controllers are verified on nonlinear boiler model [5].
{"title":"Decentralized controller for nonlinear drum boiler: Youla parametrization approach","authors":"Peter Balko, D. Rosinová","doi":"10.1109/PC.2015.7169940","DOIUrl":"https://doi.org/10.1109/PC.2015.7169940","url":null,"abstract":"In this paper, we focus on robust decentralized controller design for Benchmark PID problem brought recently by Morilla [5]: nonlinear drum boiler, having integration term and significant right half plane (RHP) zero in the interaction. We present the linearized uncertain model identified for the working area determined by working points. Robust decentralized controller is designed by Equivalent Subsystem Method approach, using Youla parametrization on subsystem level. We compare decentralized controller design with and without prescribing the designed controller structure. We derive the respective relations and filter, which guarantee the PI structure of controller. After controller design we check the robust stability condition for the determined working area. All controllers are verified on nonlinear boiler model [5].","PeriodicalId":173529,"journal":{"name":"2015 20th International Conference on Process Control (PC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131731588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In industry the model predictive control (MPC) solutions are for their high computational requirements usually implemented to the PC-based devices. The main goal of our study is to implement an MPC algorithm to the programmable logic controller (PLC). This paper describes an initialization part of our solution, which represents an internal model pre-processing algorithm implemented to the function blocks created in PLC programming standard IEC 61131-3. The pre-processing algorithm changes model representation form of multivariable system (MIMO) from continuous transfer function matrix to generalized discrete state-space model. At the end of the paper there is shown created comfortable interface used for inserting of transfer functions, process model parameterization and future MPC tuning. This interface communicating with pre-processing algorithm running on PLC allows to specify MIMO internal model by continuous transfer function matrix and the discrete state-space internal model used during control is created automatically. The operator does not have to use MATLAB or other expansive mathematical software for creation of discrete state-space representation and the most widespread mathematical description in industry known like continuous transfer function can be used. The constants of transfer functions are usually obtained by operator from current visualizations on the plant.
{"title":"Internal model pre-processing tool","authors":"Radim Hýl, R. Wagnerová","doi":"10.1109/PC.2015.7169990","DOIUrl":"https://doi.org/10.1109/PC.2015.7169990","url":null,"abstract":"In industry the model predictive control (MPC) solutions are for their high computational requirements usually implemented to the PC-based devices. The main goal of our study is to implement an MPC algorithm to the programmable logic controller (PLC). This paper describes an initialization part of our solution, which represents an internal model pre-processing algorithm implemented to the function blocks created in PLC programming standard IEC 61131-3. The pre-processing algorithm changes model representation form of multivariable system (MIMO) from continuous transfer function matrix to generalized discrete state-space model. At the end of the paper there is shown created comfortable interface used for inserting of transfer functions, process model parameterization and future MPC tuning. This interface communicating with pre-processing algorithm running on PLC allows to specify MIMO internal model by continuous transfer function matrix and the discrete state-space internal model used during control is created automatically. The operator does not have to use MATLAB or other expansive mathematical software for creation of discrete state-space representation and the most widespread mathematical description in industry known like continuous transfer function can be used. The constants of transfer functions are usually obtained by operator from current visualizations on the plant.","PeriodicalId":173529,"journal":{"name":"2015 20th International Conference on Process Control (PC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132409079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Takács, Juraj Holaza, Juraj Števek, M. Kvasnica
This paper shows how explicit model predictive control (MPC) strategies can be implemented in Python. They use a pre-calculated map between state measurements and control inputs to simplify and accelerate the calculation of optimal control inputs. By shifting majority of the computational effort off-line, the concept of explicit MPC offers a significantly faster and cheaper implementation of model predictive control. We show how explicit MPC feedbacks are designed and exported to a self-contained Python code that can be easily merged with existing applications. Two examples are provided to illustrate the procedure. One considers the design of an artificial player for a videogame. The second one tackles the problem of quadrocopter control.
{"title":"Export of explicit model predictive control to python","authors":"B. Takács, Juraj Holaza, Juraj Števek, M. Kvasnica","doi":"10.1109/PC.2015.7169942","DOIUrl":"https://doi.org/10.1109/PC.2015.7169942","url":null,"abstract":"This paper shows how explicit model predictive control (MPC) strategies can be implemented in Python. They use a pre-calculated map between state measurements and control inputs to simplify and accelerate the calculation of optimal control inputs. By shifting majority of the computational effort off-line, the concept of explicit MPC offers a significantly faster and cheaper implementation of model predictive control. We show how explicit MPC feedbacks are designed and exported to a self-contained Python code that can be easily merged with existing applications. Two examples are provided to illustrate the procedure. One considers the design of an artificial player for a videogame. The second one tackles the problem of quadrocopter control.","PeriodicalId":173529,"journal":{"name":"2015 20th International Conference on Process Control (PC)","volume":"197 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133847625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For a network of identical linear objects an output synchronization problem is considered. Problem of synchronization is solved under conditions of incomplete measurements, incomplete control and without constructing observers. Parameters of of static controller and sufficient synchronization conditions are obtained by means of passification method.
{"title":"Synchronization of passifiable linear networks by output feedback","authors":"I. Junussov, Alexander L. Fradkov","doi":"10.1109/PC.2015.7169937","DOIUrl":"https://doi.org/10.1109/PC.2015.7169937","url":null,"abstract":"For a network of identical linear objects an output synchronization problem is considered. Problem of synchronization is solved under conditions of incomplete measurements, incomplete control and without constructing observers. Parameters of of static controller and sufficient synchronization conditions are obtained by means of passification method.","PeriodicalId":173529,"journal":{"name":"2015 20th International Conference on Process Control (PC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132478870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automation of cell manipulation is required in several biotechnological fields, such as cloning, to obtain high repeatability and throughput. Noticeably scarce from the existing automated cell manipulation strategy is the estimation of dielectric response of interior and exterior single cell morphologies. We present an alternating current induced electrorotation of a single cell around the yaw-axis (in-plane) and provide a system model approach to obtain a state space model for automated cell rotation. Two pairs of orthogonally arranged square tipped electrodes are applied with sinusoidal signals along with phase shifts to obtain cell rotation. Electrodes are fabricated by micro-milling process, which is cost effective and bio-compatible. We experimentally demonstrate rotation of a single bovine oocyte of approximately 120 micron diameter. Experimental rotation can be used to estimate the hidden parameters for the system model using stochastic estimation. This article demonstrates rotation, providing the data for a system identification method.
{"title":"System identification and stochastic estimation of dielectric properties of a spherical particle using AC-induced electro-rotation","authors":"P. Benhal, J. Chase","doi":"10.1109/PC.2015.7169985","DOIUrl":"https://doi.org/10.1109/PC.2015.7169985","url":null,"abstract":"Automation of cell manipulation is required in several biotechnological fields, such as cloning, to obtain high repeatability and throughput. Noticeably scarce from the existing automated cell manipulation strategy is the estimation of dielectric response of interior and exterior single cell morphologies. We present an alternating current induced electrorotation of a single cell around the yaw-axis (in-plane) and provide a system model approach to obtain a state space model for automated cell rotation. Two pairs of orthogonally arranged square tipped electrodes are applied with sinusoidal signals along with phase shifts to obtain cell rotation. Electrodes are fabricated by micro-milling process, which is cost effective and bio-compatible. We experimentally demonstrate rotation of a single bovine oocyte of approximately 120 micron diameter. Experimental rotation can be used to estimate the hidden parameters for the system model using stochastic estimation. This article demonstrates rotation, providing the data for a system identification method.","PeriodicalId":173529,"journal":{"name":"2015 20th International Conference on Process Control (PC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126851420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-05-21DOI: 10.1109/SIBCON.2015.7147169
A. Zhirabok, S. Pavlov
The paper is devoted to the problem of fault isolation in technical systems described by nonlinear dynamic models containing non-smooth nonlinearities. So-called “model-free” or “data-driven” method is used to solve the problem. The feature of this method is that parameters of the system under consideration may be unknown. The algebra of functions is used to solve the problem under consideration.
{"title":"Data-driven method for fault isolation in technical systems","authors":"A. Zhirabok, S. Pavlov","doi":"10.1109/SIBCON.2015.7147169","DOIUrl":"https://doi.org/10.1109/SIBCON.2015.7147169","url":null,"abstract":"The paper is devoted to the problem of fault isolation in technical systems described by nonlinear dynamic models containing non-smooth nonlinearities. So-called “model-free” or “data-driven” method is used to solve the problem. The feature of this method is that parameters of the system under consideration may be unknown. The algebra of functions is used to solve the problem under consideration.","PeriodicalId":173529,"journal":{"name":"2015 20th International Conference on Process Control (PC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114683277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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