Pub Date : 2022-11-29DOI: 10.3389/fcteg.2022.1058980
Julian Schneider, S. Rothfuss, S. Hohmann
In this work, a cooperative local trajectory planner based on negotiation theory for human‐robot interaction is developed. It is implemented on a robot, which accompanies patients to examination rooms as part of the HoLLiECares project. For this purpose, an existing human–machine cooperation model for decision-making in one-time conflict cases is applied to a time-repeated negotiation of motion primitives. In negotiation theory, time pressure in the form of deadlines is classically used to achieve agreements. Since deadlines do not naturally exist in all technical applications and their artificial insertion would create an unintuitive system behavior for an involved human, a reciprocal tit-for-tat strategy for the automation is applied in the present work to achieve agreements. This leads to a system behavior that is able to dynamically change between human-in-the-lead behavior or automation-in-the-lead behavior and everything in between depending on the concession of the human and thus on human’s desire. The cooperative negotiation-based local trajectory planner is tested simulatively.
{"title":"Negotiation-based cooperative planning of local trajectories","authors":"Julian Schneider, S. Rothfuss, S. Hohmann","doi":"10.3389/fcteg.2022.1058980","DOIUrl":"https://doi.org/10.3389/fcteg.2022.1058980","url":null,"abstract":"In this work, a cooperative local trajectory planner based on negotiation theory for human‐robot interaction is developed. It is implemented on a robot, which accompanies patients to examination rooms as part of the HoLLiECares project. For this purpose, an existing human–machine cooperation model for decision-making in one-time conflict cases is applied to a time-repeated negotiation of motion primitives. In negotiation theory, time pressure in the form of deadlines is classically used to achieve agreements. Since deadlines do not naturally exist in all technical applications and their artificial insertion would create an unintuitive system behavior for an involved human, a reciprocal tit-for-tat strategy for the automation is applied in the present work to achieve agreements. This leads to a system behavior that is able to dynamically change between human-in-the-lead behavior or automation-in-the-lead behavior and everything in between depending on the concession of the human and thus on human’s desire. The cooperative negotiation-based local trajectory planner is tested simulatively.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48307608","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 : 2022-11-28DOI: 10.3389/fcteg.2022.1055915
Mohamed Radjeb Oudainia, C. Sentouh, Anh‐Tu Nguyen, J. Popieul
The work described in this paper proposes a new conflict minimisation strategy in shared driving control for lane keeping systems (LKS) in intelligent vehicles. This strategy takes into account a dynamic driver model, where the driver’s parameters are identified online using the Lyapunov approach. The design of an adaptive shared controller is based on the dynamic parameters of the driver model which changes according to the driver and the situation encountered. Based on Lyapunov stability arguments, the overall asymptotic stability of the closed-loop control system with the adaptive driver model and the variation of the vehicle speed is proved and an LMI optimization is used to formulate the control design. The simulation results, conducted with the SHERPA dynamic car simulator under real-world driving situations, show the importance of integrating a dynamic driver model in the controller design in order to decrease the conflict between the driver and the lane keeping system and to ensure the safety of the vehicle as well as to increase the confidence and acceptability of the driver.
{"title":"Online driver model parameter identification using the Lyapunov approach based shared control","authors":"Mohamed Radjeb Oudainia, C. Sentouh, Anh‐Tu Nguyen, J. Popieul","doi":"10.3389/fcteg.2022.1055915","DOIUrl":"https://doi.org/10.3389/fcteg.2022.1055915","url":null,"abstract":"The work described in this paper proposes a new conflict minimisation strategy in shared driving control for lane keeping systems (LKS) in intelligent vehicles. This strategy takes into account a dynamic driver model, where the driver’s parameters are identified online using the Lyapunov approach. The design of an adaptive shared controller is based on the dynamic parameters of the driver model which changes according to the driver and the situation encountered. Based on Lyapunov stability arguments, the overall asymptotic stability of the closed-loop control system with the adaptive driver model and the variation of the vehicle speed is proved and an LMI optimization is used to formulate the control design. The simulation results, conducted with the SHERPA dynamic car simulator under real-world driving situations, show the importance of integrating a dynamic driver model in the controller design in order to decrease the conflict between the driver and the lane keeping system and to ensure the safety of the vehicle as well as to increase the confidence and acceptability of the driver.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45872572","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 : 2022-11-25DOI: 10.3389/fcteg.2022.1061830
V. Alfaro, R. Vilanova
One of the major drawbacks of the basic parallel formulations of a PID controller is the effects of proportional and derivative kick. In order to minimize these effects, modified forms of parallel controller structures such as PI-D and I-PD are usually considered in practice. In addition, there is a usual servo/regulation tradeoff regarding closed-loop control system operation. Appropriate tuning is needed for each situation. One way of focusing explicitly on load disturbance is by the appropriate selection of a controller equation. A gap is generated here between the conception of a tuning rule and its final application that may need deployment on different controller equations. There is no danger when we go from PI-D to I-PD as we just change reference processing. However, there will be a loss of performance. The potential loss of performance, depending on the final controller equations used, motivates the authors to introduce the idea of resilient PID tuning: minimize the effects of changing the controller equation on the achieved performance/robustness. Today, this can be seen as a complement to the well-known controller fragility concept. On the basis of this scenario, this paper motivates the analysis of a tuning rule from such a point of view and also emphasizes the benefits that a better process model may provide from such an aspect.
{"title":"PID control: Resilience with respect to controller implementation","authors":"V. Alfaro, R. Vilanova","doi":"10.3389/fcteg.2022.1061830","DOIUrl":"https://doi.org/10.3389/fcteg.2022.1061830","url":null,"abstract":"One of the major drawbacks of the basic parallel formulations of a PID controller is the effects of proportional and derivative kick. In order to minimize these effects, modified forms of parallel controller structures such as PI-D and I-PD are usually considered in practice. In addition, there is a usual servo/regulation tradeoff regarding closed-loop control system operation. Appropriate tuning is needed for each situation. One way of focusing explicitly on load disturbance is by the appropriate selection of a controller equation. A gap is generated here between the conception of a tuning rule and its final application that may need deployment on different controller equations. There is no danger when we go from PI-D to I-PD as we just change reference processing. However, there will be a loss of performance. The potential loss of performance, depending on the final controller equations used, motivates the authors to introduce the idea of resilient PID tuning: minimize the effects of changing the controller equation on the achieved performance/robustness. Today, this can be seen as a complement to the well-known controller fragility concept. On the basis of this scenario, this paper motivates the analysis of a tuning rule from such a point of view and also emphasizes the benefits that a better process model may provide from such an aspect.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44286092","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 : 2022-11-24DOI: 10.3389/fcteg.2022.1008134
S. Saha, Syed Muhammad Amrr, J. Bhutto, Anas Ayesh Aljohani, M. Nabi
In rotor dynamics, the deviation of the shaft is a common phenomenon. The main reasons for the deviation are non-linear attractive forces, harmonic disturbances, system parameter variations, etc. Active magnetic bearings (AMBs) are used to support the rotor inside the air gap in rotating machines, thus avoiding wear and tear and possible breakdowns. This paper proposes a fuzzy sliding mode-inspired control (FSMIC) technique for the five-degrees-of-freedom (DOF) AMB system in the presence of system uncertainties and measurement noises. The fuzzy logic is used to estimate the auxiliary control input of the sliding mode control (SMC) to attenuate the chattering. The variable gains are designed with the help of superintended fuzzy logic to bring more flexibility to the controller performance. The stability analysis is presented with the help of the Lyapunov function candidate. The simulation studies for the AMB system under distinct types of control techniques, i.e., PID, SMC, and FSMIC, illustrate the effectiveness of the proposed control strategy.
{"title":"Fuzzy logic control of five-DOF active magnetic bearing system based on sliding mode concept","authors":"S. Saha, Syed Muhammad Amrr, J. Bhutto, Anas Ayesh Aljohani, M. Nabi","doi":"10.3389/fcteg.2022.1008134","DOIUrl":"https://doi.org/10.3389/fcteg.2022.1008134","url":null,"abstract":"In rotor dynamics, the deviation of the shaft is a common phenomenon. The main reasons for the deviation are non-linear attractive forces, harmonic disturbances, system parameter variations, etc. Active magnetic bearings (AMBs) are used to support the rotor inside the air gap in rotating machines, thus avoiding wear and tear and possible breakdowns. This paper proposes a fuzzy sliding mode-inspired control (FSMIC) technique for the five-degrees-of-freedom (DOF) AMB system in the presence of system uncertainties and measurement noises. The fuzzy logic is used to estimate the auxiliary control input of the sliding mode control (SMC) to attenuate the chattering. The variable gains are designed with the help of superintended fuzzy logic to bring more flexibility to the controller performance. The stability analysis is presented with the help of the Lyapunov function candidate. The simulation studies for the AMB system under distinct types of control techniques, i.e., PID, SMC, and FSMIC, illustrate the effectiveness of the proposed control strategy.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43174512","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 : 2022-10-28DOI: 10.3389/fcteg.2022.1046764
Gagan Acharya, Sebastian F. Ruf, Erfan Nozari
Neurostimulation technologies have seen a recent surge in interest from the neuroscience and controls communities alike due to their proven potential to treat conditions such as epilepsy, Parkinson’s Disease, and depression. The provided stimulation can be of different types, such as electric, magnetic, and optogenetic, and is generally applied to a specific region of the brain in order to drive the local and/or global neural dynamics to a desired state of (in)activity. For most neurostimulation techniques, however, an underlying theoretical understanding of their efficacy is still lacking. From a control-theoretic perspective, it is important to understand how each stimulus modality interacts with the inherent complex network dynamics of the brain in order to assess the controllability of the system and develop neurophysiologically relevant computational models that can be used to design the stimulation profile systematically and in closed loop. In this paper, we review the computational modeling studies of 1) deep brain stimulation, 2) transcranial magnetic stimulation, 3) direct current stimulation, 4) transcranial electrical stimulation, and 5) optogenetics as five of the most popular and commonly used neurostimulation technologies in research and clinical settings. For each technology, we split the reviewed studies into 1) theory-driven biophysical models capturing the low-level physics of the interactions between the stimulation source and neuronal tissue, 2) data-driven stimulus-response models which capture the end-to-end effects of stimulation on various biomarkers of interest, and 3) data-driven dynamical system models that extract the precise dynamics of the brain’s response to neurostimulation from neural data. While our focus is particularly on the latter category due to their greater utility in control design, we review key works in the former two categories as the basis and context in which dynamical system models have been and will be developed. In all cases, we highlight the strength and weaknesses of the reviewed works and conclude the review with discussions on outstanding challenges and critical avenues for future work.
{"title":"Brain modeling for control: A review","authors":"Gagan Acharya, Sebastian F. Ruf, Erfan Nozari","doi":"10.3389/fcteg.2022.1046764","DOIUrl":"https://doi.org/10.3389/fcteg.2022.1046764","url":null,"abstract":"Neurostimulation technologies have seen a recent surge in interest from the neuroscience and controls communities alike due to their proven potential to treat conditions such as epilepsy, Parkinson’s Disease, and depression. The provided stimulation can be of different types, such as electric, magnetic, and optogenetic, and is generally applied to a specific region of the brain in order to drive the local and/or global neural dynamics to a desired state of (in)activity. For most neurostimulation techniques, however, an underlying theoretical understanding of their efficacy is still lacking. From a control-theoretic perspective, it is important to understand how each stimulus modality interacts with the inherent complex network dynamics of the brain in order to assess the controllability of the system and develop neurophysiologically relevant computational models that can be used to design the stimulation profile systematically and in closed loop. In this paper, we review the computational modeling studies of 1) deep brain stimulation, 2) transcranial magnetic stimulation, 3) direct current stimulation, 4) transcranial electrical stimulation, and 5) optogenetics as five of the most popular and commonly used neurostimulation technologies in research and clinical settings. For each technology, we split the reviewed studies into 1) theory-driven biophysical models capturing the low-level physics of the interactions between the stimulation source and neuronal tissue, 2) data-driven stimulus-response models which capture the end-to-end effects of stimulation on various biomarkers of interest, and 3) data-driven dynamical system models that extract the precise dynamics of the brain’s response to neurostimulation from neural data. While our focus is particularly on the latter category due to their greater utility in control design, we review key works in the former two categories as the basis and context in which dynamical system models have been and will be developed. In all cases, we highlight the strength and weaknesses of the reviewed works and conclude the review with discussions on outstanding challenges and critical avenues for future work.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41290504","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 : 2022-09-27DOI: 10.3389/fcteg.2022.954164
Xiaomeng Li, Shoulin Hao, Tao Liu, B. Yan, Yongzhi Zhou
For industrial processes subject to input delay, a predictor-based phase-lead active disturbance rejection control (ADRC) scheme is proposed in this article for improving disturbance rejection performance by introducing a phase-lead module for feedback control. First, an extended state observer (ESO) in combination with a generalized delay-free output predictor is presented to estimate the delay-free system state together with load disturbance lumped with process uncertainties. To reduce the phase lag caused by not only ESO but also the delay-free output predictor, a phase-lead module is then added into the disturbance observation channel so as to expedite disturbance estimation and thus improve the disturbance rejection performance. Consequently, the ESO gain vector and feedback controller are analytically designed by specifying the desired poles for the observer and the closed-loop system, respectively. Moreover, a digital implementation of the proposed scheme is presented to facilitate the practical applications, followed by a robust stability analysis of the closed-loop system based on the small gain theorem. Illustrative examples from the literature are used to demonstrate the effectiveness and merits of the proposed method over the existing methods.
{"title":"Predictor-based phase-lead active disturbance rejection control design for industrial processes with input delay","authors":"Xiaomeng Li, Shoulin Hao, Tao Liu, B. Yan, Yongzhi Zhou","doi":"10.3389/fcteg.2022.954164","DOIUrl":"https://doi.org/10.3389/fcteg.2022.954164","url":null,"abstract":"For industrial processes subject to input delay, a predictor-based phase-lead active disturbance rejection control (ADRC) scheme is proposed in this article for improving disturbance rejection performance by introducing a phase-lead module for feedback control. First, an extended state observer (ESO) in combination with a generalized delay-free output predictor is presented to estimate the delay-free system state together with load disturbance lumped with process uncertainties. To reduce the phase lag caused by not only ESO but also the delay-free output predictor, a phase-lead module is then added into the disturbance observation channel so as to expedite disturbance estimation and thus improve the disturbance rejection performance. Consequently, the ESO gain vector and feedback controller are analytically designed by specifying the desired poles for the observer and the closed-loop system, respectively. Moreover, a digital implementation of the proposed scheme is presented to facilitate the practical applications, followed by a robust stability analysis of the closed-loop system based on the small gain theorem. Illustrative examples from the literature are used to demonstrate the effectiveness and merits of the proposed method over the existing methods.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49137672","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 : 2022-09-16DOI: 10.3389/fcteg.2022.922308
M. Elsaadany, Muhammad Qasim Elahi, Faris AtaAllah, H. Rehman, S. Mukhopadhyay
Because of their enhanced performance, the fractional order proportional-integral (FOPI) controllers are becoming an appealing choice for controlling induction motor speed. To implement FOPI controllers, several fractional order integral approximations are available in the literature. The approximation used, and the order of approximation affects the speed tracking, transient response, and induction motor power consumption. This further affects the energy consumption analysis if simulations are conducted based on such approximations. In this paper an electric vehicle (EV) traction system is simulated to investigate the effect of such approximations on the simulations of a battery powered, induction motor driven EV system. The system consists of an indirect field-oriented induction motor, a lithium-ion battery bank, and a three-phase inverter. This work presents a quantitative analysis of the performance of FOPI controllers using different approximations, and order of approximations is presented. The controllers are evaluated based on speed tracking, transient response, computational time, and power consumption. Both step functions and standard drive cycles are used as the speed reference signal to evaluate the effects of using different approximations and different orders of approximation, when different references are used. This work establishes a reference set of simulations that can be used to infer the amount of error in battery state of charge, and state of health analysis conducted on such an EV system, when dealing with FOPI controllers under different approximations and related settings.
{"title":"Comparative analysis of different FOPI approximations and number of terms used on simulations of a battery-powered, field-oriented induction motor based electric vehicle traction system","authors":"M. Elsaadany, Muhammad Qasim Elahi, Faris AtaAllah, H. Rehman, S. Mukhopadhyay","doi":"10.3389/fcteg.2022.922308","DOIUrl":"https://doi.org/10.3389/fcteg.2022.922308","url":null,"abstract":"Because of their enhanced performance, the fractional order proportional-integral (FOPI) controllers are becoming an appealing choice for controlling induction motor speed. To implement FOPI controllers, several fractional order integral approximations are available in the literature. The approximation used, and the order of approximation affects the speed tracking, transient response, and induction motor power consumption. This further affects the energy consumption analysis if simulations are conducted based on such approximations. In this paper an electric vehicle (EV) traction system is simulated to investigate the effect of such approximations on the simulations of a battery powered, induction motor driven EV system. The system consists of an indirect field-oriented induction motor, a lithium-ion battery bank, and a three-phase inverter. This work presents a quantitative analysis of the performance of FOPI controllers using different approximations, and order of approximations is presented. The controllers are evaluated based on speed tracking, transient response, computational time, and power consumption. Both step functions and standard drive cycles are used as the speed reference signal to evaluate the effects of using different approximations and different orders of approximation, when different references are used. This work establishes a reference set of simulations that can be used to infer the amount of error in battery state of charge, and state of health analysis conducted on such an EV system, when dealing with FOPI controllers under different approximations and related settings.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48973045","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 : 2022-09-06DOI: 10.3389/fcteg.2022.953768
J. Normey-Rico, T. Santos, R. Flesch, B. C. Torrico
This review paper deals with the analysis, design, and tuning of dead-time compensators for stable and unstable multi-input multi-output (MIMO) processes with multiple time delays. It is well known that, even in the single-input single-output case, processes with significant dead times are difficult to control using standard feedback controllers. For MIMO systems, the study of processes with dead time is more involved, particularly when the process behavior exhibits different dead times in the different input-output relationships. Because of this, much research has been conducted in the last 50 years on this subject, with different approaches and proposals of controllers for covering a variety of objectives. Thus, this paper gives an overview of this important topic, focusing on the solutions derived from the Smith Predictor. First, a historical perspective of the different controllers proposed in the literature is presented. Then, the general solution of the problem is developed, paying particular attention to robustness and disturbance rejection properties, because of their importance and usefulness in industrial processes. All the development is done in the discrete-time case, which allows direct digital implementation. Two simulation case studies are presented to illustrate some of the ideas discussed in the paper, and an experimental case study is used to discuss aspects of practical implementation.
{"title":"Control of dead-time process: From the Smith predictor to general multi-input multi-output dead-time compensators","authors":"J. Normey-Rico, T. Santos, R. Flesch, B. C. Torrico","doi":"10.3389/fcteg.2022.953768","DOIUrl":"https://doi.org/10.3389/fcteg.2022.953768","url":null,"abstract":"This review paper deals with the analysis, design, and tuning of dead-time compensators for stable and unstable multi-input multi-output (MIMO) processes with multiple time delays. It is well known that, even in the single-input single-output case, processes with significant dead times are difficult to control using standard feedback controllers. For MIMO systems, the study of processes with dead time is more involved, particularly when the process behavior exhibits different dead times in the different input-output relationships. Because of this, much research has been conducted in the last 50 years on this subject, with different approaches and proposals of controllers for covering a variety of objectives. Thus, this paper gives an overview of this important topic, focusing on the solutions derived from the Smith Predictor. First, a historical perspective of the different controllers proposed in the literature is presented. Then, the general solution of the problem is developed, paying particular attention to robustness and disturbance rejection properties, because of their importance and usefulness in industrial processes. All the development is done in the discrete-time case, which allows direct digital implementation. Two simulation case studies are presented to illustrate some of the ideas discussed in the paper, and an experimental case study is used to discuss aspects of practical implementation.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49056916","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 : 2022-09-06DOI: 10.3389/fcteg.2022.935018
Benjamin Smart, Irene de Cesare, L. Renson, L. Marucci
Recent advancements in cybergenetics have led to the development of new computational and experimental platforms that enable us to robustly steer cellular dynamics by applying external feedback control. Such technologies have never been applied to regulate intracellular dynamics of cancer cells. Here, we show in silico that adaptive model predictive control (MPC) can effectively be used to steer the simulated signalling dynamics of Non-Small Cell Lung Cancer (NSCLC) cells to resemble those of wild type cells. Our optimisation-based control algorithm enables tailoring the cost function to force the controller to alternate different drugs and/or reduce drug exposure, minimising both drug-induced toxicity and resistance to treatment. Our results pave the way for new cybergenetics experiments in cancer cells, and, longer term, can support the design of improved drug combination therapies in biomedical applications.
{"title":"Model predictive control of cancer cellular dynamics: a new strategy for therapy design","authors":"Benjamin Smart, Irene de Cesare, L. Renson, L. Marucci","doi":"10.3389/fcteg.2022.935018","DOIUrl":"https://doi.org/10.3389/fcteg.2022.935018","url":null,"abstract":"Recent advancements in cybergenetics have led to the development of new computational and experimental platforms that enable us to robustly steer cellular dynamics by applying external feedback control. Such technologies have never been applied to regulate intracellular dynamics of cancer cells. Here, we show in silico that adaptive model predictive control (MPC) can effectively be used to steer the simulated signalling dynamics of Non-Small Cell Lung Cancer (NSCLC) cells to resemble those of wild type cells. Our optimisation-based control algorithm enables tailoring the cost function to force the controller to alternate different drugs and/or reduce drug exposure, minimising both drug-induced toxicity and resistance to treatment. Our results pave the way for new cybergenetics experiments in cancer cells, and, longer term, can support the design of improved drug combination therapies in biomedical applications.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42479433","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 : 2022-09-02DOI: 10.3389/fcteg.2022.982463
Lina Owino, D. Söffker
With a rapidly expanding global population placing an ever growing demand on freshwater resources, an increased focus on irrigation techniques tailored to the specific needs of plant appears as one solution to minimize overall freshwater consumption. Precision irrigation methods seek to realize an acceptable compromise between yield and irrigation water consumption through control of the timing and quantity of water supplied to plants. The goal is to maintain the water content of the soil, achieve specific water use efficiency with regard to yield or maintain the physiological response of the plant to water stress within predetermined limits. Reliance on soil moisture measurements to establish irrigation water demand inadequately addresses heterogenous distribution of water in soil. Growing research interest is observed detailing the determination of plant water status directly from physiological responses. This paper reviews irrigation control approaches based on different plant water status assessment techniques. A distinct focus is made on application scale of the discussed control approaches, an aspect that has not been considered intensively enough in previous discussions of irrigation control approaches. A discussion of the observed strengths and shortcomings and technological advances supporting the various methods used to quantify plant water status extends the review. Emerging trends that are likely to have an impact on plant water status determination and optimal timing and quantification of irrigation water requirements are integrated to show latest results. A peek into the future of precision irrigation foresees greater reliance on plant-based signals, both in characterization of the control variable, namely the plant water status, and in generation of controller outputs in terms of quantity and timing.
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