Pub Date : 2024-08-30DOI: 10.1109/TCST.2024.3445660
Gregory Conde;Sandra M. Guzmán
The challenge of increasing irrigation efficiency to meet the demands of a growing population while protecting natural resources requires the contributions of multiple disciplines, including engineering, agronomical, horticultural, and environmental sciences. Specifically, automatic control can play a pivotal role in improving irrigation scheduling. In this context, incorporating real-time soil moisture (SM) forecasting in irrigation can potentially improve the efficiency of crop water management. However, the complexity of the analytical models that describe soil-water dynamics limits the development of practical and accurate solutions that include SM forecasting in decision-making. Currently, irrigation decisions are based on present and past SM data. This approach can be enhanced if, in addition to those, future or SM forecasting is incorporated. We formulated an SM model-based moving horizon estimation (MHE) and prediction strategy. For this, we propose a parametrizable blue SM control-oriented prediction model (SMCOPM) that obeys a soil-water balance. The SMCOPM is periodically parametrized using a proposed MHE approach, which provides adaptability, guarantees optimality, prevents overfitting, and ensures the water balance fulfillment and stability of the SMCOPM. The SM forecasting is performed by solving the parametrized SMCOPM as a function of rain, irrigation, and temperature forecasts. We evaluated the MHE and prediction strategy using, as a case study, observed data from a commercial sweetcorn field using subsurface irrigation in South Florida. The results show that by using this strategy, the SM can be predicted three days in advance with an average SM prediction error and a dispersion that significantly improves as the SMCOPM adapts over time, demonstrating convergence toward an error less than 2% and dispersion less than 3%. Consequently, the results corroborate the SMCOPM suitability, the proposed estimation strategy’s quality, and the SM behavior’s predictability. The proposed strategy has the potential for use in formulating predictive control approaches toward automating the irrigation process or scheduling irrigation actions.
{"title":"Control-Oriented Forecasting for Soil Moisture","authors":"Gregory Conde;Sandra M. Guzmán","doi":"10.1109/TCST.2024.3445660","DOIUrl":"10.1109/TCST.2024.3445660","url":null,"abstract":"The challenge of increasing irrigation efficiency to meet the demands of a growing population while protecting natural resources requires the contributions of multiple disciplines, including engineering, agronomical, horticultural, and environmental sciences. Specifically, automatic control can play a pivotal role in improving irrigation scheduling. In this context, incorporating real-time soil moisture (SM) forecasting in irrigation can potentially improve the efficiency of crop water management. However, the complexity of the analytical models that describe soil-water dynamics limits the development of practical and accurate solutions that include SM forecasting in decision-making. Currently, irrigation decisions are based on present and past SM data. This approach can be enhanced if, in addition to those, future or SM forecasting is incorporated. We formulated an SM model-based moving horizon estimation (MHE) and prediction strategy. For this, we propose a parametrizable blue SM control-oriented prediction model (SMCOPM) that obeys a soil-water balance. The SMCOPM is periodically parametrized using a proposed MHE approach, which provides adaptability, guarantees optimality, prevents overfitting, and ensures the water balance fulfillment and stability of the SMCOPM. The SM forecasting is performed by solving the parametrized SMCOPM as a function of rain, irrigation, and temperature forecasts. We evaluated the MHE and prediction strategy using, as a case study, observed data from a commercial sweetcorn field using subsurface irrigation in South Florida. The results show that by using this strategy, the SM can be predicted three days in advance with an average SM prediction error and a dispersion that significantly improves as the SMCOPM adapts over time, demonstrating convergence toward an error less than 2% and dispersion less than 3%. Consequently, the results corroborate the SMCOPM suitability, the proposed estimation strategy’s quality, and the SM behavior’s predictability. The proposed strategy has the potential for use in formulating predictive control approaches toward automating the irrigation process or scheduling irrigation actions.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"106-118"},"PeriodicalIF":4.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219180","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}
In this brief, we propose a sequential convex programming (SCP) framework for minimizing the terminal state dispersion of a stochastic dynamical system about a prescribed destination—an important property in high-risk contexts such as spacecraft landing. Our proposed approach seeks to minimize the conditional value-at-risk (CVaR) of the dispersion, thereby shifting the probability distribution away from the tails. This approach provides an optimization framework that is not overly conservative and can accurately capture more information about true distribution, compared with methods which consider only the expected value, or robust optimization methods. The main contribution of this brief is to present an approach that: 1) establishes an optimization problem with CVaR dispersion cost 2) approximated with one of the two novel surrogates which is then 3) solved using an efficient SCP algorithm. In 2), two approximation methods, a sampling approximation (SA) and a symmetric polytopic approximation (SPA), are introduced for transforming the stochastic objective function into a deterministic form. The accuracy of the SA increases with sample size at the cost of problem size and computation time. To overcome this, we introduce the SPA, which avoids sampling by using an alternative approximation and thus offers significant computational benefits. Monte Carlo simulations indicate that our proposed approaches minimize the CVaR of the dispersion successfully.
{"title":"Dispersion Sensitive Optimal Control: A Conditional Value-at-Risk-Based Tail Flattening Approach via Sequential Convex Programming","authors":"Kazuya Echigo;Oliver Sheridan;Samuel Buckner;Behçet Açıkmeşe","doi":"10.1109/TCST.2024.3427910","DOIUrl":"10.1109/TCST.2024.3427910","url":null,"abstract":"In this brief, we propose a sequential convex programming (SCP) framework for minimizing the terminal state dispersion of a stochastic dynamical system about a prescribed destination—an important property in high-risk contexts such as spacecraft landing. Our proposed approach seeks to minimize the conditional value-at-risk (CVaR) of the dispersion, thereby shifting the probability distribution away from the tails. This approach provides an optimization framework that is not overly conservative and can accurately capture more information about true distribution, compared with methods which consider only the expected value, or robust optimization methods. The main contribution of this brief is to present an approach that: 1) establishes an optimization problem with CVaR dispersion cost 2) approximated with one of the two novel surrogates which is then 3) solved using an efficient SCP algorithm. In 2), two approximation methods, a sampling approximation (SA) and a symmetric polytopic approximation (SPA), are introduced for transforming the stochastic objective function into a deterministic form. The accuracy of the SA increases with sample size at the cost of problem size and computation time. To overcome this, we introduce the SPA, which avoids sampling by using an alternative approximation and thus offers significant computational benefits. Monte Carlo simulations indicate that our proposed approaches minimize the CVaR of the dispersion successfully.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2468-2475"},"PeriodicalIF":4.9,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219181","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 : 2024-08-26DOI: 10.1109/TCST.2024.3443703
Josef Matouš;Kristin Y. Pettersen;Damiano Varagnolo;Claudio Paliotta
This article presents a distributed null-space-based behavioral (NSB) algorithm for the formation path-following problem of vehicles moving in three dimensions. The algorithm is applied to fleets of underactuated autonomous underwater vehicles (AUVs). The algorithm combines null-space-based control with consensus methods. First, we present a continuous-time version of the algorithm and prove its stability using Lyapunov analysis. Then, we present a discrete-time event-triggered version that, compared to similar formation path-following methods, can achieve the same steady state-error performance with fewer inter-vehicle transmissions. The effectiveness of both the continuous-time and the discrete-time algorithm is verified in numerical simulations. Furthermore, the discrete-time version is tested in experiments.
{"title":"A Distributed NSB Algorithm for Formation Path Following","authors":"Josef Matouš;Kristin Y. Pettersen;Damiano Varagnolo;Claudio Paliotta","doi":"10.1109/TCST.2024.3443703","DOIUrl":"10.1109/TCST.2024.3443703","url":null,"abstract":"This article presents a distributed null-space-based behavioral (NSB) algorithm for the formation path-following problem of vehicles moving in three dimensions. The algorithm is applied to fleets of underactuated autonomous underwater vehicles (AUVs). The algorithm combines null-space-based control with consensus methods. First, we present a continuous-time version of the algorithm and prove its stability using Lyapunov analysis. Then, we present a discrete-time event-triggered version that, compared to similar formation path-following methods, can achieve the same steady state-error performance with fewer inter-vehicle transmissions. The effectiveness of both the continuous-time and the discrete-time algorithm is verified in numerical simulations. Furthermore, the discrete-time version is tested in experiments.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"16-31"},"PeriodicalIF":4.9,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219182","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}
Our power and energy systems are becoming more and more integrated and interconnected. The increasing integration of edge devices and dependence on cyber infrastructure provides both the potential for benefits and risks. The integration enables more dynamic and flexible control paradigms while at the same time increasing the cyberattack surface and uncertainty of behavior. Control methodology in this new world must be designed for resilience and must have the ability to withstand, react, and respond to both physical faults and cyber-induced threats [1] . Understanding system resilience under adverse conditions requires studying control performance and how cyber infrastructure can integrate with and support the overall resilience of the system.
{"title":"Guest Editorial: Special section on Resilient Control of Cyber-Physical Power and Energy Systems","authors":"Veronica Adetola;Thomas Edgar;Sai Pushpak Nandanoori;Quanyan Zhu;Craig Rieger;Masoud Abbaszadeh","doi":"10.1109/TCST.2024.3403515","DOIUrl":"https://doi.org/10.1109/TCST.2024.3403515","url":null,"abstract":"Our power and energy systems are becoming more and more integrated and interconnected. The increasing integration of edge devices and dependence on cyber infrastructure provides both the potential for benefits and risks. The integration enables more dynamic and flexible control paradigms while at the same time increasing the cyberattack surface and uncertainty of behavior. Control methodology in this new world must be designed for resilience and must have the ability to withstand, react, and respond to both physical faults and cyber-induced threats \u0000<xref>[1]</xref>\u0000. Understanding system resilience under adverse conditions requires studying control performance and how cyber infrastructure can integrate with and support the overall resilience of the system.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 5","pages":"1688-1691"},"PeriodicalIF":4.9,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-16DOI: 10.1109/TCST.2024.3440249
Boxi Jiang;Mohammad Reza Amini;Yingqian Liao;Kartik Naik;Joaquim R. R. A. Martins;Jing Sun
Hydrokinetic turbine (HKT) controllers are traditionally optimized after determining physical turbine variables. However, simultaneously varying controls and turbine shape by considering the interactions between the control space and the turbine shape can significantly enhance the system performance in contrast to the conventional sequential design approach. This article delves into this prospect by introducing a control co-design (CCD) framework tailored for this simultaneous optimization for a variable-speed HKT rotor. The proposed CCD framework integrates a dynamic-hydrodynamic model that captures the intricate interplay between hydrodynamic performance and control strategies for the HKT under time-varying flow profiles. We systematically investigate cases with diverse control constraints in a time-varying flow environment to explore the coupling between the control space and the physical system. We demonstrate the advantages of the CCD framework over the conventional sequential design methodology through comparative study cases. CCD optimization considering a single flow condition leads to an overly specialized design that underperforms at other off-design conditions. The stochastic nature of the flow thereby highlights the need to account for a broader range of flow speeds in the HKT design process. To address this challenge, we introduce a multipoint CCD optimization that accounts for the annual flow probability distribution. The multipoint CCD approach demonstrates higher annual energy extraction compared to optimizations based on a single flow condition.
{"title":"Control Co-Design of Hydrokinetic Turbines Considering Dynamic–Hydrodynamic Coupling","authors":"Boxi Jiang;Mohammad Reza Amini;Yingqian Liao;Kartik Naik;Joaquim R. R. A. Martins;Jing Sun","doi":"10.1109/TCST.2024.3440249","DOIUrl":"10.1109/TCST.2024.3440249","url":null,"abstract":"Hydrokinetic turbine (HKT) controllers are traditionally optimized after determining physical turbine variables. However, simultaneously varying controls and turbine shape by considering the interactions between the control space and the turbine shape can significantly enhance the system performance in contrast to the conventional sequential design approach. This article delves into this prospect by introducing a control co-design (CCD) framework tailored for this simultaneous optimization for a variable-speed HKT rotor. The proposed CCD framework integrates a dynamic-hydrodynamic model that captures the intricate interplay between hydrodynamic performance and control strategies for the HKT under time-varying flow profiles. We systematically investigate cases with diverse control constraints in a time-varying flow environment to explore the coupling between the control space and the physical system. We demonstrate the advantages of the CCD framework over the conventional sequential design methodology through comparative study cases. CCD optimization considering a single flow condition leads to an overly specialized design that underperforms at other off-design conditions. The stochastic nature of the flow thereby highlights the need to account for a broader range of flow speeds in the HKT design process. To address this challenge, we introduce a multipoint CCD optimization that accounts for the annual flow probability distribution. The multipoint CCD approach demonstrates higher annual energy extraction compared to optimizations based on a single flow condition.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"48-60"},"PeriodicalIF":4.9,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219083","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 deals with the design of a decentralized dynamic control scheme to regulate the voltage of a direct current (dc) microgrid composed of boost converters supplying unknown loads. Moreover, the proposed control scheme guarantees that physical system constraints are satisfied at each time instant. Specifically, we guarantee that the voltages evolve in the positive orthant and that the duty cycle of each boost converter remains within specified bounds. The control design is based on Lyapunov theory and, more precisely, we use a Krasovskii Lyapunov function to estimate a feasible domain of attraction of the closed-loop system. Then, we guarantee that for any initial condition inside the estimated domain of attraction, the desired equilibrium point is asymptotically stable and the physical constraints are satisfied at each time instant. Finally, we assess the effectiveness of the proposed control scheme through extensive and realistic simulation scenarios.
{"title":"Decentralized Voltage Control of Boost Converters in DC Microgrids: Feasibility Guarantees","authors":"Morteza Nazari Monfared;Yu Kawano;Michele Cucuzzella","doi":"10.1109/TCST.2024.3440228","DOIUrl":"10.1109/TCST.2024.3440228","url":null,"abstract":"This article deals with the design of a decentralized dynamic control scheme to regulate the voltage of a direct current (dc) microgrid composed of boost converters supplying unknown loads. Moreover, the proposed control scheme guarantees that physical system constraints are satisfied at each time instant. Specifically, we guarantee that the voltages evolve in the positive orthant and that the duty cycle of each boost converter remains within specified bounds. The control design is based on Lyapunov theory and, more precisely, we use a Krasovskii Lyapunov function to estimate a feasible domain of attraction of the closed-loop system. Then, we guarantee that for any initial condition inside the estimated domain of attraction, the desired equilibrium point is asymptotically stable and the physical constraints are satisfied at each time instant. Finally, we assess the effectiveness of the proposed control scheme through extensive and realistic simulation scenarios.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"3-15"},"PeriodicalIF":4.9,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637465","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1109/tcst.2024.3437163
Jean Pierre Allamaa, Panagiotis Patrinos, Herman Van der Auweraer, Tong Duy Son
{"title":"Learning-Based NMPC Adaptation for Autonomous Driving Using Parallelized Digital Twin","authors":"Jean Pierre Allamaa, Panagiotis Patrinos, Herman Van der Auweraer, Tong Duy Son","doi":"10.1109/tcst.2024.3437163","DOIUrl":"https://doi.org/10.1109/tcst.2024.3437163","url":null,"abstract":"","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"8 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219078","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 : 2024-08-14DOI: 10.1109/TCST.2024.3437142
Haixu Yu;Xudong Zhao;Chunlin Chen
Reinforcement learning (RL) is considered a powerful technology with the potential to revolutionize quantum control. However, the application effectiveness of traditional RL is often limited by some insurmountable experimental conditions. Thus, developing new RL algorithms that can efficiently manipulate the quantum system dynamics is a crucial task. Prior research has shown that incorporating quantum mechanical properties into RL can improve learning performance. In this article, we consider the quantum control problem where only the target state can be accurately identified and introduce a quantum-inspired RL (QiRL) method. In particular, we propose a quantum-inspired exploration strategy to replace a commonly used $epsilon $ -greedy strategy, as well as a quantum-inspired reward scheme to incentivize the learning agent. Numerical results on three quantum system control problems, i.e., one-qubit closed quantum system, two-level open quantum system, and many-qubit closed quantum system, verify the effectiveness of QiRL. Comparison results show that the proposed QiRL outperforms existing RL algorithms (deep Q-network and proximal policy optimization) in terms of stability and efficiency for solving quantum control problems.
{"title":"Quantum-Inspired Reinforcement Learning for Quantum Control","authors":"Haixu Yu;Xudong Zhao;Chunlin Chen","doi":"10.1109/TCST.2024.3437142","DOIUrl":"10.1109/TCST.2024.3437142","url":null,"abstract":"Reinforcement learning (RL) is considered a powerful technology with the potential to revolutionize quantum control. However, the application effectiveness of traditional RL is often limited by some insurmountable experimental conditions. Thus, developing new RL algorithms that can efficiently manipulate the quantum system dynamics is a crucial task. Prior research has shown that incorporating quantum mechanical properties into RL can improve learning performance. In this article, we consider the quantum control problem where only the target state can be accurately identified and introduce a quantum-inspired RL (QiRL) method. In particular, we propose a quantum-inspired exploration strategy to replace a commonly used \u0000<inline-formula> <tex-math>$epsilon $ </tex-math></inline-formula>\u0000-greedy strategy, as well as a quantum-inspired reward scheme to incentivize the learning agent. Numerical results on three quantum system control problems, i.e., one-qubit closed quantum system, two-level open quantum system, and many-qubit closed quantum system, verify the effectiveness of QiRL. Comparison results show that the proposed QiRL outperforms existing RL algorithms (deep Q-network and proximal policy optimization) in terms of stability and efficiency for solving quantum control problems.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"61-76"},"PeriodicalIF":4.9,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219184","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 : 2024-08-06DOI: 10.1109/TCST.2024.3433229
Abraham P. Vinod;Sleiman Safaoui;Tyler H. Summers;Nobuyuki Yoshikawa;Stefano Di Cairano
We propose a decentralized, multiagent motion planner that guarantees the probabilistic safety of a team subject to stochastic uncertainty in the agent model and environment. Our scalable approach generates safe motion plans in real-time using off-the-shelf, single-agent reinforcement learning (RL) rendered safe using distributionally robust, convex optimization and buffered Voronoi cells. We guarantee the recursive feasibility of the mean trajectories and mitigate the conservativeness using a temporal discounting of safety. We show in simulation that our approach generates safe and high-performant trajectories as compared to existing approaches, and further validate these observations in physical experiments using drones.
{"title":"Decentralized, Safe, Multiagent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning","authors":"Abraham P. Vinod;Sleiman Safaoui;Tyler H. Summers;Nobuyuki Yoshikawa;Stefano Di Cairano","doi":"10.1109/TCST.2024.3433229","DOIUrl":"10.1109/TCST.2024.3433229","url":null,"abstract":"We propose a decentralized, multiagent motion planner that guarantees the probabilistic safety of a team subject to stochastic uncertainty in the agent model and environment. Our scalable approach generates safe motion plans in real-time using off-the-shelf, single-agent reinforcement learning (RL) rendered safe using distributionally robust, convex optimization and buffered Voronoi cells. We guarantee the recursive feasibility of the mean trajectories and mitigate the conservativeness using a temporal discounting of safety. We show in simulation that our approach generates safe and high-performant trajectories as compared to existing approaches, and further validate these observations in physical experiments using drones.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2492-2499"},"PeriodicalIF":4.9,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939880","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 : 2024-08-02DOI: 10.1109/tcst.2024.3433206
Viet-Anh Le, Behdad Chalaki, Filippos N. Tzortzoglou, Andreas A. Malikopoulos
{"title":"Stochastic Time-Optimal Trajectory Planning for Connected and Automated Vehicles in Mixed-Traffic Merging Scenarios","authors":"Viet-Anh Le, Behdad Chalaki, Filippos N. Tzortzoglou, Andreas A. Malikopoulos","doi":"10.1109/tcst.2024.3433206","DOIUrl":"https://doi.org/10.1109/tcst.2024.3433206","url":null,"abstract":"","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"215 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881503","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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