Pub Date : 2024-12-16DOI: 10.1109/LCSYS.2024.3518394
Zishun Liu;Saber Jafarpour;Yongxin Chen
We study the safety verification problem for discrete-time stochastic systems. We propose an approach for safety verification termed set-erosion strategy that verifies the safety of a stochastic system on a safe set through the safety of its associated deterministic system on an eroded subset. The amount of erosion is captured by the probabilistic bound on the distance between stochastic trajectories and their associated deterministic counterpart. Building on recent development of stochastic analysis, we establish a sharp probabilistic bound on this distance. Combining this bound with the set-erosion strategy, we establish a general framework for the safety verification of stochastic systems. Our method is versatile and can work effectively with any deterministic safety verification techniques. We exemplify our method by incorporating barrier functions designed for deterministic safety verification, obtaining barrier certificates much tighter than existing results. Numerical experiments are conducted to demonstrate the efficacy and superiority of our method.
{"title":"Safety Verification of Stochastic Systems: A Set-Erosion Approach","authors":"Zishun Liu;Saber Jafarpour;Yongxin Chen","doi":"10.1109/LCSYS.2024.3518394","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3518394","url":null,"abstract":"We study the safety verification problem for discrete-time stochastic systems. We propose an approach for safety verification termed set-erosion strategy that verifies the safety of a stochastic system on a safe set through the safety of its associated deterministic system on an eroded subset. The amount of erosion is captured by the probabilistic bound on the distance between stochastic trajectories and their associated deterministic counterpart. Building on recent development of stochastic analysis, we establish a sharp probabilistic bound on this distance. Combining this bound with the set-erosion strategy, we establish a general framework for the safety verification of stochastic systems. Our method is versatile and can work effectively with any deterministic safety verification techniques. We exemplify our method by incorporating barrier functions designed for deterministic safety verification, obtaining barrier certificates much tighter than existing results. Numerical experiments are conducted to demonstrate the efficacy and superiority of our method.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2859-2864"},"PeriodicalIF":2.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880413","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}
An approach for automated driving in highway scenarios based on Super-Twisting (STW) Sliding Mode Control (SMC) methodologies supported by the use of Artificial Potential Fields (APF) is presented. The use of APF allows us to propose an effective SMC solution based on the gradient tracking (GT) principle. In this regard, a novel formulation of the APF functions is introduced that exploits a sequence of attractive quadratic functions. This solution simplifies the computation of the fields and allows for trajectory generation with improved regularity properties. Extensive simulation tests, as well as comparisons with baseline and state of the art solutions, show the effectiveness of the proposed approach.
{"title":"A Sliding Mode Control Architecture for Autonomous Driving in Highway Scenarios Based on Quadratic Artificial Potential Fields","authors":"Elisabetta Punta;Massimo Canale;Francesco Cerrito;Valentino Razza","doi":"10.1109/LCSYS.2024.3518927","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3518927","url":null,"abstract":"An approach for automated driving in highway scenarios based on Super-Twisting (STW) Sliding Mode Control (SMC) methodologies supported by the use of Artificial Potential Fields (APF) is presented. The use of APF allows us to propose an effective SMC solution based on the gradient tracking (GT) principle. In this regard, a novel formulation of the APF functions is introduced that exploits a sequence of attractive quadratic functions. This solution simplifies the computation of the fields and allows for trajectory generation with improved regularity properties. Extensive simulation tests, as well as comparisons with baseline and state of the art solutions, show the effectiveness of the proposed approach.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2937-2942"},"PeriodicalIF":2.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10804184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-16DOI: 10.1109/LCSYS.2024.3519034
Zhao Feng Dai;Yash Vardhan Pant;Stephen L. Smith
In this letter, we design a model predictive controller (MPC) for systems to satisfy Signal Temporal Logic (STL) specifications when the system dynamics are partially unknown, and only a nominal model and past runtime data are available. Our approach uses Gaussian process regression to learn a stochastic, data-driven model of the unknown dynamics, and manages uncertainty in the STL specification resulting from the stochastic model using Probabilistic Signal Temporal Logic (PrSTL). The learned model and PrSTL specification are then used to formulate a chance-constrained MPC. For systems with high control rates, we discuss a modification for improving the solution speed of the control optimization. In simulation case studies, our controller increases the frequency of satisfying the STL specification compared to controllers that use only the nominal dynamics model.
{"title":"Model Predictive Control for Systems With Partially Unknown Dynamics Under Signal Temporal Logic Specifications","authors":"Zhao Feng Dai;Yash Vardhan Pant;Stephen L. Smith","doi":"10.1109/LCSYS.2024.3519034","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3519034","url":null,"abstract":"In this letter, we design a model predictive controller (MPC) for systems to satisfy Signal Temporal Logic (STL) specifications when the system dynamics are partially unknown, and only a nominal model and past runtime data are available. Our approach uses Gaussian process regression to learn a stochastic, data-driven model of the unknown dynamics, and manages uncertainty in the STL specification resulting from the stochastic model using Probabilistic Signal Temporal Logic (PrSTL). The learned model and PrSTL specification are then used to formulate a chance-constrained MPC. For systems with high control rates, we discuss a modification for improving the solution speed of the control optimization. In simulation case studies, our controller increases the frequency of satisfying the STL specification compared to controllers that use only the nominal dynamics model.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2931-2936"},"PeriodicalIF":2.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975930","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 : 2024-12-16DOI: 10.1109/LCSYS.2024.3518571
Li Tan;Wei Ren;Junlin Xiong
This letter addresses the control problem of mobile robots with random noises under timed reach-avoid (TRA) tasks. TRA tasks are expressed as signal temporal logic (STL) formulas, and an optimization problem (OP) is formulated such that the chance constraint (CC) is embedded. To deal with the OP in the continuous-time setting, a local-to-global control strategy is proposed. We first decompose the STL formula into a finite number of local ones, and then decompose and convert the CC into deterministic constraints such that a finite number of local OPs are established and solved efficiently. The feasibility of all the local OPs implies the feasibility of the original OP, which results in a control strategy for the task accomplishment. The proposed strategy is further extended to the multi-robot case. Finally, numerical examples and comparisons are presented to illustrate the efficacy of the proposed control strategy.
{"title":"Decomposition-Based Chance-Constrained Control for Timed Reach-Avoid Tasks","authors":"Li Tan;Wei Ren;Junlin Xiong","doi":"10.1109/LCSYS.2024.3518571","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3518571","url":null,"abstract":"This letter addresses the control problem of mobile robots with random noises under timed reach-avoid (TRA) tasks. TRA tasks are expressed as signal temporal logic (STL) formulas, and an optimization problem (OP) is formulated such that the chance constraint (CC) is embedded. To deal with the OP in the continuous-time setting, a local-to-global control strategy is proposed. We first decompose the STL formula into a finite number of local ones, and then decompose and convert the CC into deterministic constraints such that a finite number of local OPs are established and solved efficiently. The feasibility of all the local OPs implies the feasibility of the original OP, which results in a control strategy for the task accomplishment. The proposed strategy is further extended to the multi-robot case. Finally, numerical examples and comparisons are presented to illustrate the efficacy of the proposed control strategy.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2961-2966"},"PeriodicalIF":2.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975926","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 : 2024-12-16DOI: 10.1109/LCSYS.2024.3518397
Jonas Hansson;Emma Tegling
In this letter, we consider the problem of coordinating a collection of nth-order integrator systems. The coordination is achieved through the novel serial consensus design; this control design achieves a stable closed-loop system while adhering to the constraint of only using local and relative measurements. Earlier work has shown that second-order serial consensus can stabilize a collection of double integrators with scalable performance conditions independent of the number of agents and topology. This letter generalizes these performance results to an arbitrary order ${mathrm { n}}geq 1$ . The derived performance bounds depend on the condition number, measured in the vector-induced maximum matrix norm, of a general diagonalizing matrix. We precisely characterize how a minimal condition number can be achieved. Third-order serial consensus is illustrated through a case study of PI-controlled vehicular formation, where the added integrators are used to mitigate the effect of unmeasured load disturbances. The theoretical results are illustrated through examples.
{"title":"Performance Bounds for Multi-Vehicle Networks With Local Integrators","authors":"Jonas Hansson;Emma Tegling","doi":"10.1109/LCSYS.2024.3518397","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3518397","url":null,"abstract":"In this letter, we consider the problem of coordinating a collection of nth-order integrator systems. The coordination is achieved through the novel serial consensus design; this control design achieves a stable closed-loop system while adhering to the constraint of only using local and relative measurements. Earlier work has shown that second-order serial consensus can stabilize a collection of double integrators with scalable performance conditions independent of the number of agents and topology. This letter generalizes these performance results to an arbitrary order <inline-formula> <tex-math>${mathrm { n}}geq 1$ </tex-math></inline-formula>. The derived performance bounds depend on the condition number, measured in the vector-induced maximum matrix norm, of a general diagonalizing matrix. We precisely characterize how a minimal condition number can be achieved. Third-order serial consensus is illustrated through a case study of PI-controlled vehicular formation, where the added integrators are used to mitigate the effect of unmeasured load disturbances. The theoretical results are illustrated through examples.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2901-2906"},"PeriodicalIF":2.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975935","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 : 2024-12-13DOI: 10.1109/LCSYS.2024.3517455
Giorgio Riva;Stefano Radrizzani;Giulio Panzani;Matteo Corno;Sergio M. Savaresi
The growing interest in hybrid and electric racing cars is driving advancements in energy storage systems. Among these, hybrid battery packs (HBPs) are particularly promising, as they combine high power and energy capabilities to enhance performance over race distances. In this letter, we propose a co-design optimization problem (Co-OP) to simultaneously optimize race time and the HBP layout parameters, including the position and the size of a suitable DC/DC converter to connect the two battery packs and the powertrain. To solve it, we employ a three-layer framework based on a minimum race rime (MRT) problem on a fixed trajectory. Considering a Formula E case study, we demonstrate the applicability of the proposed methodology, analyze the optimal design for different layout configurations, and compare them in terms of achievable performance, complexity, and robustness.
{"title":"Optimal Layout Co-Design in Hybrid Battery Packs for Electric Racing Cars","authors":"Giorgio Riva;Stefano Radrizzani;Giulio Panzani;Matteo Corno;Sergio M. Savaresi","doi":"10.1109/LCSYS.2024.3517455","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3517455","url":null,"abstract":"The growing interest in hybrid and electric racing cars is driving advancements in energy storage systems. Among these, hybrid battery packs (HBPs) are particularly promising, as they combine high power and energy capabilities to enhance performance over race distances. In this letter, we propose a co-design optimization problem (Co-OP) to simultaneously optimize race time and the HBP layout parameters, including the position and the size of a suitable DC/DC converter to connect the two battery packs and the powertrain. To solve it, we employ a three-layer framework based on a minimum race rime (MRT) problem on a fixed trajectory. Considering a Formula E case study, we demonstrate the applicability of the proposed methodology, analyze the optimal design for different layout configurations, and compare them in terms of achievable performance, complexity, and robustness.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2883-2888"},"PeriodicalIF":2.4,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10798623","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-13DOI: 10.1109/LCSYS.2024.3517457
Baike She;Lei Xin;Philip E. Paré;Matthew Hale
Modeling epidemic spread is critical for informing policy decisions aimed at mitigation. Accordingly, in this letter we present a new data-driven method based on Gaussian process regression (GPR) to model epidemic spread through the difference on the logarithmic scale of the infected cases. We bound the variance of the predictions made by GPR, which quantifies the impact of epidemic data on the proposed model. Next, we derive a high-probability error bound on the prediction error in terms of the distance between the training points and a testing point, the posterior variance, and the level of change in the spreading process, and we assess how the characteristics of the epidemic spread and infection data influence this error bound. We present examples that use GPR to model and predict epidemic spread by using real-world infection data gathered in the U.K. during the COVID-19 epidemic. These examples illustrate that, under typical conditions, the prediction for the next twenty days has 94.29% of the noisy data located within the 95% confidence interval, validating these predictions. We further compare the modeling and prediction results with other methods, such as polynomial regression, k-nearest neighbors (KNN) regression, and neural networks, to demonstrate the benefits of leveraging GPR in disease spread modeling.
{"title":"Modeling Epidemic Spread: A Gaussian Process Regression Approach","authors":"Baike She;Lei Xin;Philip E. Paré;Matthew Hale","doi":"10.1109/LCSYS.2024.3517457","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3517457","url":null,"abstract":"Modeling epidemic spread is critical for informing policy decisions aimed at mitigation. Accordingly, in this letter we present a new data-driven method based on Gaussian process regression (GPR) to model epidemic spread through the difference on the logarithmic scale of the infected cases. We bound the variance of the predictions made by GPR, which quantifies the impact of epidemic data on the proposed model. Next, we derive a high-probability error bound on the prediction error in terms of the distance between the training points and a testing point, the posterior variance, and the level of change in the spreading process, and we assess how the characteristics of the epidemic spread and infection data influence this error bound. We present examples that use GPR to model and predict epidemic spread by using real-world infection data gathered in the U.K. during the COVID-19 epidemic. These examples illustrate that, under typical conditions, the prediction for the next twenty days has 94.29% of the noisy data located within the 95% confidence interval, validating these predictions. We further compare the modeling and prediction results with other methods, such as polynomial regression, k-nearest neighbors (KNN) regression, and neural networks, to demonstrate the benefits of leveraging GPR in disease spread modeling.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2847-2852"},"PeriodicalIF":2.4,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912587","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 : 2024-12-13DOI: 10.1109/LCSYS.2024.3517458
Thomas de Jong;Mircea Lazar
This letter presents a kernelized offset-free data-driven predictive control scheme for nonlinear systems. Traditional model-based and data-driven predictive controllers often struggle with inaccurate predictors or persistent disturbances, especially in the case of nonlinear dynamics, leading to tracking offsets and stability issues. To overcome these limitations, we employ kernel methods to parameterize the nonlinear terms of a velocity model, preserving its structure and efficiently learning unknown parameters through a least squares approach. This results in a offset-free data-driven predictive control scheme formulated as a nonlinear program, but solvable via sequential quadratic programming. We provide a framework for analyzing recursive feasibility and stability of the developed method and we demonstrate its effectiveness through simulations on a nonlinear benchmark example.
{"title":"Kernelized Offset-Free Data-Driven Predictive Control for Nonlinear Systems","authors":"Thomas de Jong;Mircea Lazar","doi":"10.1109/LCSYS.2024.3517458","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3517458","url":null,"abstract":"This letter presents a kernelized offset-free data-driven predictive control scheme for nonlinear systems. Traditional model-based and data-driven predictive controllers often struggle with inaccurate predictors or persistent disturbances, especially in the case of nonlinear dynamics, leading to tracking offsets and stability issues. To overcome these limitations, we employ kernel methods to parameterize the nonlinear terms of a velocity model, preserving its structure and efficiently learning unknown parameters through a least squares approach. This results in a offset-free data-driven predictive control scheme formulated as a nonlinear program, but solvable via sequential quadratic programming. We provide a framework for analyzing recursive feasibility and stability of the developed method and we demonstrate its effectiveness through simulations on a nonlinear benchmark example.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2877-2882"},"PeriodicalIF":2.4,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890348","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 : 2024-12-11DOI: 10.1109/LCSYS.2024.3516637
Fethi Bencherki;Anders Rantzer
This letter presents and analyzes an adaptive data-driven controller that learns the optimal processing rate in a multi-unit processing network in the presence of disturbances. We formulate an optimization problem of linear cost, linear dynamics for the processing network model and an affine constraint on the dispatcher policy. A data-driven linear equation is constructed, based on which the online dispatcher policy is updated. An upper bound on the gap between the optimal cost and the cost incurred by the data-driven controller is extracted.
{"title":"Data-Driven Adaptive Dispatching Policies for Processing Networks","authors":"Fethi Bencherki;Anders Rantzer","doi":"10.1109/LCSYS.2024.3516637","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3516637","url":null,"abstract":"This letter presents and analyzes an adaptive data-driven controller that learns the optimal processing rate in a multi-unit processing network in the presence of disturbances. We formulate an optimization problem of linear cost, linear dynamics for the processing network model and an affine constraint on the dispatcher policy. A data-driven linear equation is constructed, based on which the online dispatcher policy is updated. An upper bound on the gap between the optimal cost and the cost incurred by the data-driven controller is extracted.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2841-2846"},"PeriodicalIF":2.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912384","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 : 2024-12-11DOI: 10.1109/LCSYS.2024.3516073
Arash Rahmanidehkordi;Amir H. Ghasemi
This letter introduces a traffic management algorithm for heterogeneous highway corridors consisting of both human-driven vehicles (HVs) and autonomous vehicles (AVs). The traffic flow dynamics are modeled using the heterogeneous METANET model, with variable speed control employed to maintain desired vehicle densities and reduce congestion. To generate speed control commands, we developed a hybrid framework that combines feedback linearization (FL) and model predictive control (MPC), treating the traffic system as an over-actuated, constrained nonlinear system. The FL component linearizes the nonlinear dynamics, while the MPC component handles constraints by generating virtual control inputs that ensure control limits are respected. To address the over-actuated nature of the system, we introduce a novel constraint mapping algorithm within the MPC that links virtual control input constraints to the actual control commands. Additionally, we propose a real-time reference density generation method that accounts for both AVs and HVs to mitigate congestion. Numerical simulations were conducted for two scenarios: controlling only AVs and controlling both AVs and HVs. The results demonstrate that the proposed FL-MPC framework effectively reduces congestion, even when speed control is applied exclusively to AVs.
{"title":"Traffic Density Control for Heterogeneous Highway Systems With Input Constraints","authors":"Arash Rahmanidehkordi;Amir H. Ghasemi","doi":"10.1109/LCSYS.2024.3516073","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3516073","url":null,"abstract":"This letter introduces a traffic management algorithm for heterogeneous highway corridors consisting of both human-driven vehicles (HVs) and autonomous vehicles (AVs). The traffic flow dynamics are modeled using the heterogeneous METANET model, with variable speed control employed to maintain desired vehicle densities and reduce congestion. To generate speed control commands, we developed a hybrid framework that combines feedback linearization (FL) and model predictive control (MPC), treating the traffic system as an over-actuated, constrained nonlinear system. The FL component linearizes the nonlinear dynamics, while the MPC component handles constraints by generating virtual control inputs that ensure control limits are respected. To address the over-actuated nature of the system, we introduce a novel constraint mapping algorithm within the MPC that links virtual control input constraints to the actual control commands. Additionally, we propose a real-time reference density generation method that accounts for both AVs and HVs to mitigate congestion. Numerical simulations were conducted for two scenarios: controlling only AVs and controlling both AVs and HVs. The results demonstrate that the proposed FL-MPC framework effectively reduces congestion, even when speed control is applied exclusively to AVs.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2787-2792"},"PeriodicalIF":2.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858882","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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