Abstract The control of complex systems is often challenging due to high dimensional nonlinear models, unmodeled phenomena, and parameter uncertainty. The increasing ubiquity of sensors measuring such systems and increased computational resources has led to an interest in purely data-driven control methods, particularly using the Koopman operator. In this paper, we elucidate the construction of a linear predictor based on a sequence of time realizations of observables drawn from a data archive of different trajectories combined with subspace identification methods for linear systems. This approach is free of any predefined set of basis functions but instead depends on the time realization of these basis functions. The prediction and control are demonstrated with examples. The basis functions can be constructed using timedelayed coordinates of the outputs, enabling the application to purely data-driven systems. The paper thus shows the link between Koopman operator-based control methods and classical subspace identification methods. The approach in this paper can be extended to adaptive online learning and control.
{"title":"Koopman Operator Based Predictive Control With a Data Archive of Observables","authors":"Kartik Loya, Jake Buzhardt, Phanindra Tallapragada","doi":"10.1115/1.4063604","DOIUrl":"https://doi.org/10.1115/1.4063604","url":null,"abstract":"Abstract The control of complex systems is often challenging due to high dimensional nonlinear models, unmodeled phenomena, and parameter uncertainty. The increasing ubiquity of sensors measuring such systems and increased computational resources has led to an interest in purely data-driven control methods, particularly using the Koopman operator. In this paper, we elucidate the construction of a linear predictor based on a sequence of time realizations of observables drawn from a data archive of different trajectories combined with subspace identification methods for linear systems. This approach is free of any predefined set of basis functions but instead depends on the time realization of these basis functions. The prediction and control are demonstrated with examples. The basis functions can be constructed using timedelayed coordinates of the outputs, enabling the application to purely data-driven systems. The paper thus shows the link between Koopman operator-based control methods and classical subspace identification methods. The approach in this paper can be extended to adaptive online learning and control.","PeriodicalId":327130,"journal":{"name":"ASME Letters in Dynamic Systems and Control","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135696143","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}
Hady Benyamen, Mozammal Chowdhury, Shawn S. Keshmiri
Abstract This work presents mathematical and practical frameworks for designing deep deterministic policy gradient (DDPG) flight controllers for fixed-wing aircraft. The aim is to design reinforcement learning (RL) flight controllers and accelerate training by substituting the six degrees of freedom aircraft models with linear time-invariant (LTI) dynamic models. The initial validation flight tests of the DDPG RL flight controller exhibited poor performance. Post-flight test investigation revealed that the unsatisfactory performance of the RL flight controller could be attributed to the high reliance of the LTI model on accurate control trim values and the substantial errors observed in the predicted trim values generated by the engineering-level dynamic analysis software. A complementary real-time learning Gaussian process (GP) regression was designed to mitigate this critical shortcoming of the LTI-based RL flight controller. The GP estimates and updates the trim control surfaces using observed flight data. The GP regression method incorporates real-time corrections to the trim control surfaces to enhance the performance of the flight controller. Flight test validation was repeated, and the results show that the RL controller, bolstered by the GP trim-finding algorithm, can successfully control the aircraft with excellent tracking performance.
{"title":"Reinforcement Learning Based Aircraft Controller Enhanced By Gaussian Process Trim Finding","authors":"Hady Benyamen, Mozammal Chowdhury, Shawn S. Keshmiri","doi":"10.1115/1.4063605","DOIUrl":"https://doi.org/10.1115/1.4063605","url":null,"abstract":"Abstract This work presents mathematical and practical frameworks for designing deep deterministic policy gradient (DDPG) flight controllers for fixed-wing aircraft. The aim is to design reinforcement learning (RL) flight controllers and accelerate training by substituting the six degrees of freedom aircraft models with linear time-invariant (LTI) dynamic models. The initial validation flight tests of the DDPG RL flight controller exhibited poor performance. Post-flight test investigation revealed that the unsatisfactory performance of the RL flight controller could be attributed to the high reliance of the LTI model on accurate control trim values and the substantial errors observed in the predicted trim values generated by the engineering-level dynamic analysis software. A complementary real-time learning Gaussian process (GP) regression was designed to mitigate this critical shortcoming of the LTI-based RL flight controller. The GP estimates and updates the trim control surfaces using observed flight data. The GP regression method incorporates real-time corrections to the trim control surfaces to enhance the performance of the flight controller. Flight test validation was repeated, and the results show that the RL controller, bolstered by the GP trim-finding algorithm, can successfully control the aircraft with excellent tracking performance.","PeriodicalId":327130,"journal":{"name":"ASME Letters in Dynamic Systems and Control","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135696368","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}
Abstract Atomic Force Microscopy (AFM) serves characterization and actuation in nanoscale applications. We study the stochastic dynamics of an AFM cantilever under tip-sample interactions represented by the Lennard–Jones and Morse potential energy functions. In both cases, we also study the contrasting dynamic effects of additive (external) and multiplicative (internal) noise. Moreover, for multiplicative noise, we study the two sub-cases arising from the Ito and Stratonovich interpretations of stochastic integrals. In each case, we also investigate stochastic stability of the system by tracing the time evolution of the maximal Lyapunov exponent. Additionally, we obtain stationary probability densities for the unforced dynamics using stochastic averaging.
{"title":"On the nonlinear stochastic dynamics of an Atomic Force Microscope cantilever","authors":"Aman K Singh, Subramanian Ramakrishnan","doi":"10.1115/1.4063601","DOIUrl":"https://doi.org/10.1115/1.4063601","url":null,"abstract":"Abstract Atomic Force Microscopy (AFM) serves characterization and actuation in nanoscale applications. We study the stochastic dynamics of an AFM cantilever under tip-sample interactions represented by the Lennard–Jones and Morse potential energy functions. In both cases, we also study the contrasting dynamic effects of additive (external) and multiplicative (internal) noise. Moreover, for multiplicative noise, we study the two sub-cases arising from the Ito and Stratonovich interpretations of stochastic integrals. In each case, we also investigate stochastic stability of the system by tracing the time evolution of the maximal Lyapunov exponent. Additionally, we obtain stationary probability densities for the unforced dynamics using stochastic averaging.","PeriodicalId":327130,"journal":{"name":"ASME Letters in Dynamic Systems and Control","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135696622","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}
Abstract Ground vehicles operate under different driving conditions, which requires the analysis of varying parameter values. It is essential to ensure the vehicle's safe operation under all these conditions of the parameter variation. In this paper, we investigate the safe operating limits of a ground vehicle by performing the reachability analysis for varying parameters using the Koopman-spectrum approach. The reachable set is computed using the Koopman principal eigenfunctions obtained from a convex optimization formulation for different values of the parameter. We consider the two degrees-of-freedom non-linear quarter car model to simulate the vehicle's dynamics. Based on the obtained reachable sets, we provide the mean and variance computation framework with parametric uncertainty. The results show that the reachable set for each value provides valuable information regarding the safe operating limits of the vehicle and can assist in developing safe driving strategies.
{"title":"Safe Operating Limits of Vehicle Dynamics under Parameter Uncertainty using Koopman Spectrum","authors":"Alok Kumar, Bhagyashree Umathe, Umesh Vaidya, Atul Kelkar","doi":"10.1115/1.4063479","DOIUrl":"https://doi.org/10.1115/1.4063479","url":null,"abstract":"Abstract Ground vehicles operate under different driving conditions, which requires the analysis of varying parameter values. It is essential to ensure the vehicle's safe operation under all these conditions of the parameter variation. In this paper, we investigate the safe operating limits of a ground vehicle by performing the reachability analysis for varying parameters using the Koopman-spectrum approach. The reachable set is computed using the Koopman principal eigenfunctions obtained from a convex optimization formulation for different values of the parameter. We consider the two degrees-of-freedom non-linear quarter car model to simulate the vehicle's dynamics. Based on the obtained reachable sets, we provide the mean and variance computation framework with parametric uncertainty. The results show that the reachable set for each value provides valuable information regarding the safe operating limits of the vehicle and can assist in developing safe driving strategies.","PeriodicalId":327130,"journal":{"name":"ASME Letters in Dynamic Systems and Control","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060593","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}
Jiamin Xu, Alexander Keller, Nazli Demirer, He Zhang, Kaixiao Tian, Ketan Bhaidasna, Robert Darbe, Dongmei Chen
Abstract This paper presents the development and validation of a nonlinear delay differential equation (DDE) model for borehole propagation in the inclination plane. Most importantly, built upon a quasi-linear model, the nonlinear approach incorporates information pertaining to the floating stabilizers and bit tilt saturation by formulating a linear complementarity problem. As a result, the outputs of the nonlinear model were in a better agreement with the field data when compared with the quasi-linear model. The maximum modeling error of the nonlinear DDE is less than 1 degrees over a drilled depth of 600 feet.
{"title":"Experimentally Validated Nonlinear Delayed Differential Approach to Model Borehole Propagation for Directional Drilling","authors":"Jiamin Xu, Alexander Keller, Nazli Demirer, He Zhang, Kaixiao Tian, Ketan Bhaidasna, Robert Darbe, Dongmei Chen","doi":"10.1115/1.4063477","DOIUrl":"https://doi.org/10.1115/1.4063477","url":null,"abstract":"Abstract This paper presents the development and validation of a nonlinear delay differential equation (DDE) model for borehole propagation in the inclination plane. Most importantly, built upon a quasi-linear model, the nonlinear approach incorporates information pertaining to the floating stabilizers and bit tilt saturation by formulating a linear complementarity problem. As a result, the outputs of the nonlinear model were in a better agreement with the field data when compared with the quasi-linear model. The maximum modeling error of the nonlinear DDE is less than 1 degrees over a drilled depth of 600 feet.","PeriodicalId":327130,"journal":{"name":"ASME Letters in Dynamic Systems and Control","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060249","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}
Abstract This article presents an adaptive artificial neural network-based control scheme that strategically integrates the attracting-manifold design and a smooth Lipschitz-constant projection operator. The essence of the scheme is elaborated through the design of the reference-command tracking control law of an nth-order single-input uncertain nonlinear system that can be transformed into the Brunovsky form. The method offers two major advantages: First, by employing the attracting-manifold design, it is possible to achieve asymptotic recovery of the ideal (deterministic) closed-loop dynamics. In other words, the perturbation caused by parametric uncertainties will be driven to zero asymptotically as a result of such a design, fostering a superior control performance. Second, the established smooth projection operator ensures the Lipschitz constant of the adaptive artificial neural network is bounded from above, thereby providing a certain degree of robustness against adversarial perturbations. The proposed method is validated through a numerical simulation example and compared with a standard certainty-equivalent neural-adaptive control method to demonstrate its superior performance.
{"title":"Adaptive Artificial Neural Network-based Control Through Attracting-Manifold Design and Lipschitz Constant Projection","authors":"Xingyu Zhou, John Maweu, Junmin Wang","doi":"10.1115/1.4063474","DOIUrl":"https://doi.org/10.1115/1.4063474","url":null,"abstract":"Abstract This article presents an adaptive artificial neural network-based control scheme that strategically integrates the attracting-manifold design and a smooth Lipschitz-constant projection operator. The essence of the scheme is elaborated through the design of the reference-command tracking control law of an nth-order single-input uncertain nonlinear system that can be transformed into the Brunovsky form. The method offers two major advantages: First, by employing the attracting-manifold design, it is possible to achieve asymptotic recovery of the ideal (deterministic) closed-loop dynamics. In other words, the perturbation caused by parametric uncertainties will be driven to zero asymptotically as a result of such a design, fostering a superior control performance. Second, the established smooth projection operator ensures the Lipschitz constant of the adaptive artificial neural network is bounded from above, thereby providing a certain degree of robustness against adversarial perturbations. The proposed method is validated through a numerical simulation example and compared with a standard certainty-equivalent neural-adaptive control method to demonstrate its superior performance.","PeriodicalId":327130,"journal":{"name":"ASME Letters in Dynamic Systems and Control","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060263","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}
Abstract The NOx conversion efficiency of a combined Selective Catalytic Reduction and Ammonia Slip Catalyst (SCR-ASC) in a Diesel Aftertreatment (AT) system degrades with time. A novel model-informed data-driven On-Board Diagnostic (OBD) binary classification strategy is proposed in this paper to distinguish an End of Useful Life (EUL) SCR-ASC catalyst from Degreened (DG) ones. An optimized, supervised machine learning model was used for the classification with a calibrated single-cell 3-state Continuous Stirred Tank Reactor (CSTR) observer used for state estimation. Percentage of samples classified as EUL (%EUL), w.r.t. classification boundary of 50%, was used as an objective criterion of classification. The method resulted in 87.5% classification accuracy when tested on 8 day-files from 4 trucks (2 day-files per truck; 1 DG and 1 EUL) operating in real-world on-road conditions. Each day-file had ~86,000 samples of data. Mileage of the same truck was used as ground truth for classification. However, mileage across different trucks cannot be used for classification since the operating conditions would vary across trucks.
{"title":"Data-Based On-Board Diagnostics for Diesel Engine NOx-Reduction Aftertreatment Systems","authors":"Atharva Tandale, Kaushal Jain, Peter H. Meckl","doi":"10.1115/1.4063473","DOIUrl":"https://doi.org/10.1115/1.4063473","url":null,"abstract":"Abstract The NOx conversion efficiency of a combined Selective Catalytic Reduction and Ammonia Slip Catalyst (SCR-ASC) in a Diesel Aftertreatment (AT) system degrades with time. A novel model-informed data-driven On-Board Diagnostic (OBD) binary classification strategy is proposed in this paper to distinguish an End of Useful Life (EUL) SCR-ASC catalyst from Degreened (DG) ones. An optimized, supervised machine learning model was used for the classification with a calibrated single-cell 3-state Continuous Stirred Tank Reactor (CSTR) observer used for state estimation. Percentage of samples classified as EUL (%EUL), w.r.t. classification boundary of 50%, was used as an objective criterion of classification. The method resulted in 87.5% classification accuracy when tested on 8 day-files from 4 trucks (2 day-files per truck; 1 DG and 1 EUL) operating in real-world on-road conditions. Each day-file had ~86,000 samples of data. Mileage of the same truck was used as ground truth for classification. However, mileage across different trucks cannot be used for classification since the operating conditions would vary across trucks.","PeriodicalId":327130,"journal":{"name":"ASME Letters in Dynamic Systems and Control","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060451","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}
Abstract We present a system identification method based on recursive least-squares (RLS) and coprime collaborative sensing, which can recover system dynamics from non-uniform temporal data. Focusing on systems with fast input sampling and slow output sampling, we use a polynomial transformation to reparameterize the system model and create an auxiliary model that can be identified from the non-uniform data. We show the identifiability of the auxiliary model using a Diophantine-equation approach. Numerical examples demonstrate successful system reconstruction and the ability to capture fast system response with limited temporal feedback.
{"title":"A Recursive System Identification with Non-uniform Temporal Feedback under Coprime Collaborative Sensing","authors":"Jinhua Ouyang, Xu Chen","doi":"10.1115/1.4063481","DOIUrl":"https://doi.org/10.1115/1.4063481","url":null,"abstract":"Abstract We present a system identification method based on recursive least-squares (RLS) and coprime collaborative sensing, which can recover system dynamics from non-uniform temporal data. Focusing on systems with fast input sampling and slow output sampling, we use a polynomial transformation to reparameterize the system model and create an auxiliary model that can be identified from the non-uniform data. We show the identifiability of the auxiliary model using a Diophantine-equation approach. Numerical examples demonstrate successful system reconstruction and the ability to capture fast system response with limited temporal feedback.","PeriodicalId":327130,"journal":{"name":"ASME Letters in Dynamic Systems and Control","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060758","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}
Abstract In the past decade, the number of battery electric vehicles (BEV) on the road has been growing rapidly in response to global climate change and cyclic gasoline shortages. Due to the limited driving range of most commercial BEVs, individuals who use BEVs for long-distance travel tend to spend much more time on the road than owners of traditional internal combustion engine vehicles. To reduce travel time in long-distance trips, a social-aware trip planner is necessary to coordinate driving speed, vehicle charging, and social activities (e.g., dining, visit of places of interest). This paper formulates this travel time minimization problem into a mixed-integer programming model and utilizes Genetic Algorithm (GA) to solve for the optimal driving speed, vehicle charging, and the schedule of dining. The proposed planner is first tested numerically based on two real-world routes. Then Monte Carlo Simulations are performed to give a thorough analysis on the performance of the proposed planner. The simulation results show that the proposed method outperforms the baseline on both routes. Additionally, real-world tests are conducted to further validate the accuracy of the mixed-integer programming model.
{"title":"Social-aware Long-distance Trip Planner for Electric Vehicles Using Genetic Algorithm","authors":"Zifei Su, Maxavier D Lamantia, Pingen Chen","doi":"10.1115/1.4063483","DOIUrl":"https://doi.org/10.1115/1.4063483","url":null,"abstract":"Abstract In the past decade, the number of battery electric vehicles (BEV) on the road has been growing rapidly in response to global climate change and cyclic gasoline shortages. Due to the limited driving range of most commercial BEVs, individuals who use BEVs for long-distance travel tend to spend much more time on the road than owners of traditional internal combustion engine vehicles. To reduce travel time in long-distance trips, a social-aware trip planner is necessary to coordinate driving speed, vehicle charging, and social activities (e.g., dining, visit of places of interest). This paper formulates this travel time minimization problem into a mixed-integer programming model and utilizes Genetic Algorithm (GA) to solve for the optimal driving speed, vehicle charging, and the schedule of dining. The proposed planner is first tested numerically based on two real-world routes. Then Monte Carlo Simulations are performed to give a thorough analysis on the performance of the proposed planner. The simulation results show that the proposed method outperforms the baseline on both routes. Additionally, real-world tests are conducted to further validate the accuracy of the mixed-integer programming model.","PeriodicalId":327130,"journal":{"name":"ASME Letters in Dynamic Systems and Control","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060609","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}
Navaneeth Pushpalayam, Lee Alexander, Rajesh Rajamani
Abstract This paper develops a position estimation system for a robot moving over a two-dimensional plane with three degrees-of-freedom. The position estimation system is based on an external rotating platform containing a permanent magnet and a monocular camera. The robot is equipped with a two-axes magnetic sensor. The rotation of the external platform is controlled using the monocular camera so as to always point at the robot as it moves over the 2D plane. The radial distance to the robot can then be obtained using a one-degree-of-freedom nonlinear magnetic field model and a nonlinear observer. Extensive experimental results are presented on the performance of the developed system. Results show that the position of the robot can be estimated with sub-mm accuracy over a radial distance range of +/−60 cm from the magnet.
{"title":"Non-Contacting Two-Dimensional Position Estimation using an External Magnet and Monocular Computer Vision","authors":"Navaneeth Pushpalayam, Lee Alexander, Rajesh Rajamani","doi":"10.1115/1.4063480","DOIUrl":"https://doi.org/10.1115/1.4063480","url":null,"abstract":"Abstract This paper develops a position estimation system for a robot moving over a two-dimensional plane with three degrees-of-freedom. The position estimation system is based on an external rotating platform containing a permanent magnet and a monocular camera. The robot is equipped with a two-axes magnetic sensor. The rotation of the external platform is controlled using the monocular camera so as to always point at the robot as it moves over the 2D plane. The radial distance to the robot can then be obtained using a one-degree-of-freedom nonlinear magnetic field model and a nonlinear observer. Extensive experimental results are presented on the performance of the developed system. Results show that the position of the robot can be estimated with sub-mm accuracy over a radial distance range of +/−60 cm from the magnet.","PeriodicalId":327130,"journal":{"name":"ASME Letters in Dynamic Systems and Control","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135806165","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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