Pub Date : 2025-03-11DOI: 10.1109/TCST.2025.3546564
Lucas Novaki Ribeiro;Pablo Borja;Cosimo Della Santina;Bastian Deutschmann
The existing model-based control strategies for tendon-driven continuum soft robots neglect the dynamics of the actuation system. Nevertheless, such dynamics have an important impact on the closed-loop performance. This work analyzes the influence of the actuation dynamics in tendon-driven continuum soft robots performing trajectory-tracking tasks. To this end, we use singular perturbation (SP) theory to design controllers that account for such dynamics. We provide the analytical formulation of the SP controllers and their in-depth experimental validation. Additionally, we use high- and low-stiffness tendons to experimentally compare the performance of the proposed SP controllers against traditional feedback control schemes that disregard the actuation dynamics. The experimental results show that SP controllers outperform the approaches that neglect the actuation dynamics by reducing oscillations and achieving lower errors without relying on high gains. Furthermore, it is shown that neglecting the actuation dynamics may lead to instability when the tendons have a low stiffness coefficient.
{"title":"Singular-Perturbation Control of a Tendon-Driven Soft Robot: Theory and Experiments","authors":"Lucas Novaki Ribeiro;Pablo Borja;Cosimo Della Santina;Bastian Deutschmann","doi":"10.1109/TCST.2025.3546564","DOIUrl":"https://doi.org/10.1109/TCST.2025.3546564","url":null,"abstract":"The existing model-based control strategies for tendon-driven continuum soft robots neglect the dynamics of the actuation system. Nevertheless, such dynamics have an important impact on the closed-loop performance. This work analyzes the influence of the actuation dynamics in tendon-driven continuum soft robots performing trajectory-tracking tasks. To this end, we use singular perturbation (SP) theory to design controllers that account for such dynamics. We provide the analytical formulation of the SP controllers and their in-depth experimental validation. Additionally, we use high- and low-stiffness tendons to experimentally compare the performance of the proposed SP controllers against traditional feedback control schemes that disregard the actuation dynamics. The experimental results show that SP controllers outperform the approaches that neglect the actuation dynamics by reducing oscillations and achieving lower errors without relying on high gains. Furthermore, it is shown that neglecting the actuation dynamics may lead to instability when the tendons have a low stiffness coefficient.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1929-1936"},"PeriodicalIF":3.9,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-07DOI: 10.1109/TCST.2025.3561056
Branimir Škugor;Jakov Topić;Joško Deur;Vladimir Ivanovic;H. Eric Tseng
The proposed autonomous vehicle (AV) safe speed strategy is based on a scenario- and grid-based stochastic model predictive control (SMPC) and a probabilistic neural network (NN) model aimed to predict pedestrian behavior when approaching unsignalized crosswalk. The SMPC problem is formulated to minimize the vehicle traveling time, while accounting for vehicle-pedestrian interaction and keeping the risk of collision with pedestrian low. The vehicle control trajectory is conveniently described by only two parameters to be optimized: the vehicle acceleration and the target speed. Apart from reducing the computational complexity, this simplification facilitates the NN prediction model design in terms of lowering the number of model inputs. The proposed SMPC strategy is verified against a baseline control strategy by means of large-scale stochastic simulations. The verification results indicate that the SMPC strategy on average results in significantly lower vehicle traveling time and less aggressive decelerations, while avoiding pedestrian collisions.
{"title":"Interaction-Aware Optimal Safe Speed Control for Autonomous Vehicles Approaching Unsignalized Crosswalks With Pedestrians","authors":"Branimir Škugor;Jakov Topić;Joško Deur;Vladimir Ivanovic;H. Eric Tseng","doi":"10.1109/TCST.2025.3561056","DOIUrl":"https://doi.org/10.1109/TCST.2025.3561056","url":null,"abstract":"The proposed autonomous vehicle (AV) safe speed strategy is based on a scenario- and grid-based stochastic model predictive control (SMPC) and a probabilistic neural network (NN) model aimed to predict pedestrian behavior when approaching unsignalized crosswalk. The SMPC problem is formulated to minimize the vehicle traveling time, while accounting for vehicle-pedestrian interaction and keeping the risk of collision with pedestrian low. The vehicle control trajectory is conveniently described by only two parameters to be optimized: the vehicle acceleration and the target speed. Apart from reducing the computational complexity, this simplification facilitates the NN prediction model design in terms of lowering the number of model inputs. The proposed SMPC strategy is verified against a baseline control strategy by means of large-scale stochastic simulations. The verification results indicate that the SMPC strategy on average results in significantly lower vehicle traveling time and less aggressive decelerations, while avoiding pedestrian collisions.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1864-1878"},"PeriodicalIF":3.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-07DOI: 10.1109/TCST.2025.3546076
Jihao Huang;Jun Zeng;Xuemin Chi;Koushil Sreenath;Zhitao Liu;Hongye Su
Designing safety-critical controllers for acceleration-controlled unicycle robots is challenging, as control inputs may not appear in the constraints of control Lyapunov functions (CLFs) and control barrier functions (CBFs), leading to invalid controllers. Existing methods often rely on state-feedback-based CLFs and high-order CBFs (HOCBFs), which are computationally expensive to construct and fail to maintain effectiveness in dynamic environments with fast-moving, nearby obstacles. To address these challenges, we propose constructing velocity obstacle (VO)-based CBFs (VOCBFs) in the velocity space to enhance dynamic collision avoidance capabilities, instead of relying on distance-based CBFs that require the introduction of HOCBFs. Additionally, by extending VOCBFs using variants of VO, we enable reactive collision avoidance between robots. We formulate a safety-critical controller for acceleration-controlled unicycle robots as a mixed-integer quadratic programming (MIQP), integrating state-feedback-based CLFs for navigation and VOCBFs for collision avoidance. To enhance the efficiency of solving the MIQP, we split the MIQP into multiple suboptimization problems and employ a decision network to reduce computational costs. Numerical simulations demonstrate that our approach effectively guides the robot to its target while avoiding collisions. Compared to HOCBFs, VOCBFs exhibit significantly improved dynamic obstacle avoidance performance, especially when obstacles are fast moving and close to the robot. Furthermore, we extend our method to distributed multirobot systems.
{"title":"Dynamic Collision Avoidance Using Velocity Obstacle-Based Control Barrier Functions","authors":"Jihao Huang;Jun Zeng;Xuemin Chi;Koushil Sreenath;Zhitao Liu;Hongye Su","doi":"10.1109/TCST.2025.3546076","DOIUrl":"https://doi.org/10.1109/TCST.2025.3546076","url":null,"abstract":"Designing safety-critical controllers for acceleration-controlled unicycle robots is challenging, as control inputs may not appear in the constraints of control Lyapunov functions (CLFs) and control barrier functions (CBFs), leading to invalid controllers. Existing methods often rely on state-feedback-based CLFs and high-order CBFs (HOCBFs), which are computationally expensive to construct and fail to maintain effectiveness in dynamic environments with fast-moving, nearby obstacles. To address these challenges, we propose constructing velocity obstacle (VO)-based CBFs (VOCBFs) in the velocity space to enhance dynamic collision avoidance capabilities, instead of relying on distance-based CBFs that require the introduction of HOCBFs. Additionally, by extending VOCBFs using variants of VO, we enable reactive collision avoidance between robots. We formulate a safety-critical controller for acceleration-controlled unicycle robots as a mixed-integer quadratic programming (MIQP), integrating state-feedback-based CLFs for navigation and VOCBFs for collision avoidance. To enhance the efficiency of solving the MIQP, we split the MIQP into multiple suboptimization problems and employ a decision network to reduce computational costs. Numerical simulations demonstrate that our approach effectively guides the robot to its target while avoiding collisions. Compared to HOCBFs, VOCBFs exhibit significantly improved dynamic obstacle avoidance performance, especially when obstacles are fast moving and close to the robot. Furthermore, we extend our method to distributed multirobot systems.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1601-1615"},"PeriodicalIF":3.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-06DOI: 10.1109/TCST.2025.3560511
Georgiy A. Bondar;Robert Gifford;Linh Thi Xuan Phan;Abhishek Halder
We propose to learn the time-varying stochastic computational resource usage of software as a graph-structured Schrödinger bridge problem (SBP). In general, learning the computational resource usage from data is challenging because resources, such as the number of CPU instructions and the number of last level cache requests are both time-varying and statistically correlated. Our proposed method enables learning the joint time-varying stochasticity in computational resource usage from the measured profile snapshots in a nonparametric manner. The method can be used to predict the most-likely time-varying distribution of computational resource availability at a desired time. We provide detailed algorithms for stochastic learning in both single-core and multicore cases, discuss the convergence guarantees, computational complexities, and demonstrate their practical use in two case studies: a single-core nonlinear model predictive controller (NMPC) and a synthetic multicore software.
{"title":"Stochastic Learning of Computational Resource Usage as Graph-Structured Multimarginal Schrödinger Bridge","authors":"Georgiy A. Bondar;Robert Gifford;Linh Thi Xuan Phan;Abhishek Halder","doi":"10.1109/TCST.2025.3560511","DOIUrl":"https://doi.org/10.1109/TCST.2025.3560511","url":null,"abstract":"We propose to learn the time-varying stochastic computational resource usage of software as a graph-structured Schrödinger bridge problem (SBP). In general, learning the computational resource usage from data is challenging because resources, such as the number of CPU instructions and the number of last level cache requests are both time-varying and statistically correlated. Our proposed method enables learning the joint time-varying stochasticity in computational resource usage from the measured profile snapshots in a nonparametric manner. The method can be used to predict the most-likely time-varying distribution of computational resource availability at a desired time. We provide detailed algorithms for stochastic learning in both single-core and multicore cases, discuss the convergence guarantees, computational complexities, and demonstrate their practical use in two case studies: a single-core nonlinear model predictive controller (NMPC) and a synthetic multicore software.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1835-1850"},"PeriodicalIF":3.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Autonomous underwater vehicles (AUVs) present several challenges due to the complex and simultaneous interplay of various factors, including but not limited to unmodeled dynamics, highly nonlinear behavior, intercouplings, communication delays, and environmental disturbances. In particular, environmental disturbances degrade trajectory tracking performance for model-based controllers, e.g., model predictive control (MPC) algorithms. Data-driven methods such as the Gaussian process (GP) are effective at learning disturbances in real time; however, the underlying offline hyperparameter tuning process limits their overall effectiveness. To overcome this limitation, we propose a novel dynamic forgetting GP (DF-GP) methodology that compensates for operational disturbances, thus circumventing the need for hyperparameter retuning. In essence, the proposed method optimally combines the predictions of individual GPs—designed with handcrafted forgetting factors, rendering precise disturbance estimation of varying timescales. What is more, the predicted disturbances update the model parameters in MPC, facilitating a learning-based control framework that ensures accurate tracking performance in different underwater scenarios. Rigorous simulation and real-world experiments demonstrate the efficiency and efficacy of the proposed framework. The results show a 25% improvement in disturbance estimation and tracking performance, demonstrating that the proposed framework outperforms its direct competitors.
{"title":"Empowering Autonomous Underwater Vehicles Using Learning-Based Model Predictive Control With Dynamic Forgetting Gaussian Processes","authors":"Abdelhakim Amer;Mohit Mehndiratta;Yury Brodskiy;Erdal Kayacan","doi":"10.1109/TCST.2025.3539218","DOIUrl":"https://doi.org/10.1109/TCST.2025.3539218","url":null,"abstract":"Autonomous underwater vehicles (AUVs) present several challenges due to the complex and simultaneous interplay of various factors, including but not limited to unmodeled dynamics, highly nonlinear behavior, intercouplings, communication delays, and environmental disturbances. In particular, environmental disturbances degrade trajectory tracking performance for model-based controllers, e.g., model predictive control (MPC) algorithms. Data-driven methods such as the Gaussian process (GP) are effective at learning disturbances in real time; however, the underlying offline hyperparameter tuning process limits their overall effectiveness. To overcome this limitation, we propose a novel dynamic forgetting GP (DF-GP) methodology that compensates for operational disturbances, thus circumventing the need for hyperparameter retuning. In essence, the proposed method optimally combines the predictions of individual GPs—designed with handcrafted forgetting factors, rendering precise disturbance estimation of varying timescales. What is more, the predicted disturbances update the model parameters in MPC, facilitating a learning-based control framework that ensures accurate tracking performance in different underwater scenarios. Rigorous simulation and real-world experiments demonstrate the efficiency and efficacy of the proposed framework. The results show a 25% improvement in disturbance estimation and tracking performance, demonstrating that the proposed framework outperforms its direct competitors.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1913-1920"},"PeriodicalIF":3.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-05DOI: 10.1109/TCST.2025.3561504
Edoardo Catenaro;Lorenzo Sabug;Giulio Panzani;Davide Sette;Fredy Ruiz;Lorenzo Fagiano;Sergio M. Savaresi
A comparison of different approaches to the automatic online, data-driven calibration of assisted gearshift settings for a motorcycle is presented. An objective function associated with the component stress and clutch resynchronization time is exploited and optimized during operation using different strategies: from naïve space-filling approaches to learning-based black-box optimization algorithms. The performance of various methods is compared in real-world experiments using metrics related to the experimental convergence rate and the quality of the best found result.
{"title":"Automatic Learning-Based Calibration of Assisted Motorcycle Gearshift: A Comparative Study","authors":"Edoardo Catenaro;Lorenzo Sabug;Giulio Panzani;Davide Sette;Fredy Ruiz;Lorenzo Fagiano;Sergio M. Savaresi","doi":"10.1109/TCST.2025.3561504","DOIUrl":"https://doi.org/10.1109/TCST.2025.3561504","url":null,"abstract":"A comparison of different approaches to the automatic online, data-driven calibration of assisted gearshift settings for a motorcycle is presented. An objective function associated with the component stress and clutch resynchronization time is exploited and optimized during operation using different strategies: from naïve space-filling approaches to learning-based black-box optimization algorithms. The performance of various methods is compared in real-world experiments using metrics related to the experimental convergence rate and the quality of the best found result.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1771-1784"},"PeriodicalIF":3.9,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-04DOI: 10.1109/TCST.2025.3545346
Stefano Radrizzani;Giulio Panzani;Sergio M. Savaresi
Given the intrinsic hybrid nature of electric bikes (e-bikes), they call for energy management strategies (EMSs), to optimize the energy and power flows in the vehicle. The additional challenge in e-bikes, compared to traditional hybrid electric vehicles (HEVs), is having a human power source. Specifically, human power cannot be directly controlled and his/her energy consumption cannot be easily measured or estimated. In this work, we address the energy management for a series-parallel bike, focusing in particular on the series architecture. In detail, we propose a comfort-oriented EMS able to indirectly control the human power to make the cyclist ride close to his/her preferred operating point. Toward this aim, the equivalent consumption minimization strategy (ECMS) is properly extended, to deal with this unique scenario. The solution is implemented on the vehicle control unit (VCU); therefore, the effectiveness of the ECMS-based energy management is proven through an experimental validation when the rider is present in the loop. Results show how the cyclist’s operating point is indirectly affected by the behavior of the proposed EMS keeping it close to the preferred operating point.
{"title":"Comfort-Oriented Energy Management for an e-Bike in Series Configuration With Experimental Validation","authors":"Stefano Radrizzani;Giulio Panzani;Sergio M. Savaresi","doi":"10.1109/TCST.2025.3545346","DOIUrl":"https://doi.org/10.1109/TCST.2025.3545346","url":null,"abstract":"Given the intrinsic hybrid nature of electric bikes (e-bikes), they call for energy management strategies (EMSs), to optimize the energy and power flows in the vehicle. The additional challenge in e-bikes, compared to traditional hybrid electric vehicles (HEVs), is having a human power source. Specifically, human power cannot be directly controlled and his/her energy consumption cannot be easily measured or estimated. In this work, we address the energy management for a series-parallel bike, focusing in particular on the series architecture. In detail, we propose a comfort-oriented EMS able to indirectly control the human power to make the cyclist ride close to his/her preferred operating point. Toward this aim, the equivalent consumption minimization strategy (ECMS) is properly extended, to deal with this unique scenario. The solution is implemented on the vehicle control unit (VCU); therefore, the effectiveness of the ECMS-based energy management is proven through an experimental validation when the rider is present in the loop. Results show how the cyclist’s operating point is indirectly affected by the behavior of the proposed EMS keeping it close to the preferred operating point.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1560-1571"},"PeriodicalIF":3.9,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article introduces a procedure for determining an approximation of the optimal amount of photovoltaics (PVs) for powering water distribution networks (WDNs) through grid-connected PVs. The procedure aims to find the PV amount, minimizing the total expected cost of the WDN over the lifespan of the PVs. The approach follows an iterative process, starting with an initial estimate of the PV quantity and then calculating the total cost of WDN operation. To calculate the total cost of the WDN, we sample PV power profiles that represent the future production based on a probabilistic PV production model. Simulations are conducted assuming these sampled PV profiles power the WDN, and pump flow rates are determined using a control method designed for PV-powered WDNs. Following the simulations, the overall WDN cost is calculated. Since we lack access to derivative information, we employ the derivative-free Nelder-Mead method for iteratively adjusting the PV quantity to find an approximation of the optimal value. The procedure is applied for the WDN of Randers, a Danish town. By determining an approximation of the optimal quantity of PVs, we observe a 14.5% decrease in WDN costs compared to the scenario without PV installations, assuming a 25-year lifespan for the PV panels.
{"title":"Optimizing Photovoltaic Panel Quantity for Water Distribution Networks","authors":"Mirhan Ürkmez;Carsten Kallesøe;Jan Dimon Bendtsen;John Leth","doi":"10.1109/TCST.2025.3560173","DOIUrl":"https://doi.org/10.1109/TCST.2025.3560173","url":null,"abstract":"The article introduces a procedure for determining an approximation of the optimal amount of photovoltaics (PVs) for powering water distribution networks (WDNs) through grid-connected PVs. The procedure aims to find the PV amount, minimizing the total expected cost of the WDN over the lifespan of the PVs. The approach follows an iterative process, starting with an initial estimate of the PV quantity and then calculating the total cost of WDN operation. To calculate the total cost of the WDN, we sample PV power profiles that represent the future production based on a probabilistic PV production model. Simulations are conducted assuming these sampled PV profiles power the WDN, and pump flow rates are determined using a control method designed for PV-powered WDNs. Following the simulations, the overall WDN cost is calculated. Since we lack access to derivative information, we employ the derivative-free Nelder-Mead method for iteratively adjusting the PV quantity to find an approximation of the optimal value. The procedure is applied for the WDN of Randers, a Danish town. By determining an approximation of the optimal quantity of PVs, we observe a 14.5% decrease in WDN costs compared to the scenario without PV installations, assuming a 25-year lifespan for the PV panels.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1727-1742"},"PeriodicalIF":3.9,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Safety is of paramount importance in control systems to avoid costly risks and catastrophic damages. The control barrier function (CBF) method, a promising solution for safety-critical control, poses a new challenge of enhancing control performance due to its direct modification of original control design and the introduction of uncalibrated parameters. In this work, we shed light on the crucial role of configurable parameters in the CBF method for performance enhancement with a systematical categorization. Based on that, we propose a novel framework combining the CBF method with Bayesian optimization (BO) to optimize the safe control performance. Considering feasibility/safety-critical constraints, we develop a safe version of BO using the barrier-based interior method to efficiently search for promising feasible configurable parameters. Furthermore, we provide theoretical criteria of our framework regarding safety and optimality. An essential advantage of our framework lies in that it can work in model-agnostic environments, leaving sufficient flexibility in designing objective and constraint functions. Finally, simulations on swing-up control and high-fidelity adaptive cruise control (ACC) are conducted to demonstrate the effectiveness of our framework.
{"title":"Optimal Parameter Adaptation for Safety-Critical Control via Safe Barrier Bayesian Optimization","authors":"Shengbo Wang;Ke Li;Zheng Yan;Zhenyuan Guo;Song Zhu;Guanghui Wen;Shiping Wen","doi":"10.1109/TCST.2025.3561059","DOIUrl":"https://doi.org/10.1109/TCST.2025.3561059","url":null,"abstract":"Safety is of paramount importance in control systems to avoid costly risks and catastrophic damages. The control barrier function (CBF) method, a promising solution for safety-critical control, poses a new challenge of enhancing control performance due to its direct modification of original control design and the introduction of uncalibrated parameters. In this work, we shed light on the crucial role of configurable parameters in the CBF method for performance enhancement with a systematical categorization. Based on that, we propose a novel framework combining the CBF method with Bayesian optimization (BO) to optimize the safe control performance. Considering feasibility/safety-critical constraints, we develop a safe version of BO using the barrier-based interior method to efficiently search for promising feasible configurable parameters. Furthermore, we provide theoretical criteria of our framework regarding safety and optimality. An essential advantage of our framework lies in that it can work in model-agnostic environments, leaving sufficient flexibility in designing objective and constraint functions. Finally, simulations on swing-up control and high-fidelity adaptive cruise control (ACC) are conducted to demonstrate the effectiveness of our framework.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1953-1959"},"PeriodicalIF":3.9,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents a novel control approach for flexible joint robots that use servo systems to control their motion. Under this configuration, the servo system is understood to have an inner feedback loop that accepts motor positions as inputs and uses torques as outputs. Hence, the soft robot uses motor positions as control inputs instead of torques. To address this system configuration with a reliable control system of high performance, we aim to generalize the elastic structure preserving (ESP) control approach, which previously has been proposed for backdrivable torque-controlled elastic robots, to robots with position-controlled elastic actuators. This scheme results in a dynamic feedback controller that recovers the elastic structure of the uncontrolled robot in the closed loop. At the same time, damping is injected, thus achieving a control system with high compliance and desired energy dissipation. Our results are supported by a rigorous analysis, where local input-to-state stability and output strict passivity can be concluded if the inner feedback loop from the servo system satisfies some assumptions. Experiments on two platforms validate the proposed control scheme and show the overall control system’s performance.
{"title":"Elastic Structure Preserving Control for Flexible Joint Robots With Position-Controlled Actuators","authors":"Jerónimo Moyrón;Christian Ott;Annika Kirner;Javier Moreno-Valenzuela","doi":"10.1109/TCST.2025.3562024","DOIUrl":"https://doi.org/10.1109/TCST.2025.3562024","url":null,"abstract":"This article presents a novel control approach for flexible joint robots that use servo systems to control their motion. Under this configuration, the servo system is understood to have an inner feedback loop that accepts motor positions as inputs and uses torques as outputs. Hence, the soft robot uses motor positions as control inputs instead of torques. To address this system configuration with a reliable control system of high performance, we aim to generalize the elastic structure preserving (ESP) control approach, which previously has been proposed for backdrivable torque-controlled elastic robots, to robots with position-controlled elastic actuators. This scheme results in a dynamic feedback controller that recovers the elastic structure of the uncontrolled robot in the closed loop. At the same time, damping is injected, thus achieving a control system with high compliance and desired energy dissipation. Our results are supported by a rigorous analysis, where local input-to-state stability and output strict passivity can be concluded if the inner feedback loop from the servo system satisfies some assumptions. Experiments on two platforms validate the proposed control scheme and show the overall control system’s performance.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1810-1819"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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