Pub Date : 2025-11-24DOI: 10.1109/OJCSYS.2025.3636033
Ainur Zhaikhan;Ali H. Sayed
This study proposes the use of a social learning method to estimate a global state within a multi-agent off-policy actor-critic algorithm for reinforcement learning (RL) operating in a partially observable environment. We assume that the network of agents operates in a fully-decentralized manner, possessing the capability to exchange variables with their immediate neighbors. The proposed design methodology is supported by an analysis demonstrating that the difference between final outcomes, obtained when the global state is fully observed versus estimated through the social learning method, is $varepsilon$-bounded when an appropriate number of iterations of social learning updates are implemented. Unlike many existing dec-POMDP-based RL approaches, the proposed algorithm is suitable for model-free multi-agent reinforcement learning as it does not require knowledge of a transition model. Furthermore, experimental results illustrate the efficacy of the algorithm and demonstrate its superiority over the current state-of-the-art methods.
{"title":"Multi-Agent Off-Policy Actor-Critic Reinforcement Learning for Partially Observable Environments","authors":"Ainur Zhaikhan;Ali H. Sayed","doi":"10.1109/OJCSYS.2025.3636033","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3636033","url":null,"abstract":"This study proposes the use of a social learning method to estimate a global state within a multi-agent off-policy actor-critic algorithm for reinforcement learning (RL) operating in a partially observable environment. We assume that the network of agents operates in a fully-decentralized manner, possessing the capability to exchange variables with their immediate neighbors. The proposed design methodology is supported by an analysis demonstrating that the difference between final outcomes, obtained when the global state is fully observed versus estimated through the social learning method, is <inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>-bounded when an appropriate number of iterations of social learning updates are implemented. Unlike many existing dec-POMDP-based RL approaches, the proposed algorithm is suitable for model-free multi-agent reinforcement learning as it does not require knowledge of a transition model. Furthermore, experimental results illustrate the efficacy of the algorithm and demonstrate its superiority over the current state-of-the-art methods.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11266921","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729357","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 : 2025-11-21DOI: 10.1109/OJCSYS.2025.3635449
Rajan K. Aggarwal;J. Christian Gerdes
Autonomous vehicle control in low-friction environments should be capable of using all of the available traction at the road to accomplish maneuvering objectives. In these environments, however, the limit of traction is difficult to estimate, which challenges standard motion planning techniques. In this paper, we introduce a trajectory optimization framework that natively incorporates the complex and nonlinear effects of friction uncertainty into the planning process to improve both the performance and robustness of maneuvering at high accelerations. The core approach of the method is to explicitly consider a range of possible dynamics models, inclusive of their closed-loop behavior, simultaneously in the optimization. We illustrate this method through a racing example, where the minimum-time objective facilitates intuitive performance and robustness metrics (lap time and tracking error limits), all while necessitating vehicle maneuvering through nonlinear and friction-sensitive regions of the state space. Experiments on an autonomous VW Golf GTI on a challenging winter ice track demonstrate the efficacy of this approach.
{"title":"Friction-Robust Autonomous Racing Using Trajectory Optimization Over Multiple Models","authors":"Rajan K. Aggarwal;J. Christian Gerdes","doi":"10.1109/OJCSYS.2025.3635449","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3635449","url":null,"abstract":"Autonomous vehicle control in low-friction environments should be capable of using all of the available traction at the road to accomplish maneuvering objectives. In these environments, however, the limit of traction is difficult to estimate, which challenges standard motion planning techniques. In this paper, we introduce a trajectory optimization framework that natively incorporates the complex and nonlinear effects of friction uncertainty into the planning process to improve both the performance and robustness of maneuvering at high accelerations. The core approach of the method is to explicitly consider a range of possible dynamics models, <italic>inclusive</i> of their closed-loop behavior, simultaneously in the optimization. We illustrate this method through a racing example, where the minimum-time objective facilitates intuitive performance and robustness metrics (lap time and tracking error limits), all while necessitating vehicle maneuvering through nonlinear and friction-sensitive regions of the state space. Experiments on an autonomous VW Golf GTI on a challenging winter ice track demonstrate the efficacy of this approach.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"16-30"},"PeriodicalIF":0.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11264289","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778225","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 : 2025-10-24DOI: 10.1109/OJCSYS.2025.3625465
Josefine B. Graebener;Apurva S. Badithela;Denizalp Goktas;Wyatt Ubellacker;Eric V. Mazumdar;Aaron D. Ames;Richard M. Murray
Designing tests for autonomous systems is challenging due to their complexity. This work proposes a flow-based approach for reactive test synthesis from temporal logic specifications, enabling the synthesis of test environments consisting of static and reactive obstacles, and. These specifications describe desired test behavior, including system requirements as well as a test objective not revealed to the system. The synthesized test strategy places restrictions on system actions in closed-loop with system behavior, accomplishing the test objective while ensuring realizability of the system’s objective without aiding it (a general-sum setting). Automata theory and flow networks are leveraged to formulate a mixed-linear program (MILP) for test synthesis. For a dynamic test agent, the agent strategy is synthesized for a generalized reactivity of rank 1 (GR(1)) specification constructed from the MILP solution. This flow-based, reactive test synthesis is conducted offline and is agnostic to the system controller. Finally, the resulting test strategy is demonstrated in simulation and hardware experiments on a pair of quadrupedal robots for a variety of specifications.
{"title":"Flow-Based Synthesis of Reactive Tests for Discrete Decision-Making Systems With Temporal Logic Specifications","authors":"Josefine B. Graebener;Apurva S. Badithela;Denizalp Goktas;Wyatt Ubellacker;Eric V. Mazumdar;Aaron D. Ames;Richard M. Murray","doi":"10.1109/OJCSYS.2025.3625465","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3625465","url":null,"abstract":"Designing tests for autonomous systems is challenging due to their complexity. This work proposes a flow-based approach for reactive test synthesis from temporal logic specifications, enabling the synthesis of test environments consisting of static and reactive obstacles, and. These specifications describe desired test behavior, including system requirements as well as a test objective not revealed to the system. The synthesized test strategy places restrictions on system actions in closed-loop with system behavior, accomplishing the test objective while ensuring realizability of the system’s objective without aiding it (a general-sum setting). Automata theory and flow networks are leveraged to formulate a mixed-linear program (MILP) for test synthesis. For a dynamic test agent, the agent strategy is synthesized for a generalized reactivity of rank 1 (GR(1)) specification constructed from the MILP solution. This flow-based, reactive test synthesis is conducted offline and is agnostic to the system controller. Finally, the resulting test strategy is demonstrated in simulation and hardware experiments on a pair of quadrupedal robots for a variety of specifications.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"597-617"},"PeriodicalIF":0.0,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11217209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612057","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 : 2025-10-24DOI: 10.1109/OJCSYS.2025.3625364
Jeremy D. Watson
Predictive control can either be data-based (e.g. data-enabled predictive control, or DeePC) or model-based (model predictive control). In this paper we aim to bridge the gap between the two by investigating the case where only a partial model is available, i.e. incorporating model knowledge into DeePC. In our formulation, the partial knowledge takes the form of known state and output equations that are a subset of the complete model equations. We formulate an approach to take advantage of partial model knowledge which we call hybrid data-enabled predictive control (HDeePC). We prove feasible set equivalence and equivalent closed-loop behavior in the noiseless, LTI case. As we show, this has potential advantages over a purely data-based approach in terms of computational expense and robustness to noise in some cases. Furthermore, this allows applications to certain linear time-varying and nonlinear systems. Finally, a number of case studies, including the control of an energy storage system in a microgrid, a triple-mass system, and a larger power system, illustrate the potential of HDeePC.
{"title":"Hybrid Data-Enabled Predictive Control: Incorporating Model Knowledge Into the DeePC","authors":"Jeremy D. Watson","doi":"10.1109/OJCSYS.2025.3625364","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3625364","url":null,"abstract":"Predictive control can either be data-based (e.g. data-enabled predictive control, or DeePC) or model-based (model predictive control). In this paper we aim to bridge the gap between the two by investigating the case where only a partial model is available, i.e. incorporating model knowledge into DeePC. In our formulation, the partial knowledge takes the form of known state and output equations that are a subset of the complete model equations. We formulate an approach to take advantage of partial model knowledge which we call hybrid data-enabled predictive control (HDeePC). We prove feasible set equivalence and equivalent closed-loop behavior in the noiseless, LTI case. As we show, this has potential advantages over a purely data-based approach in terms of computational expense and robustness to noise in some cases. Furthermore, this allows applications to certain linear time-varying and nonlinear systems. Finally, a number of case studies, including the control of an energy storage system in a microgrid, a triple-mass system, and a larger power system, illustrate the potential of HDeePC.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"549-561"},"PeriodicalIF":0.0,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11217168","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510198","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 : 2025-10-23DOI: 10.1109/OJCSYS.2025.3624615
Rupam Kalyan Chakraborty;Geethu Joseph;Chandra R. Murthy
Sparsity constraints on the control inputs of a linear dynamical system naturally arise in several practical applications such as networked control, computer vision, seismic signal processing, and cyber-physical systems. In this work, we consider the problem of jointly estimating the states and sparse inputs of such systems from low-dimensional (compressive) measurements. Due to the low-dimensional measurements, conventional Kalman filtering and smoothing algorithms fail to accurately estimate the states and inputs. We present a Bayesian approach that exploits the input sparsity to significantly improve estimation accuracy. Sparsity in the input estimates is promoted by using different prior distributions on the input. We investigate two main approaches: regularizer-based maximum a posteriori estimation and Bayesian learning-based estimation. We also extend the approaches to handle control inputs with common support and analyze the time and memory complexities of the presented algorithms. Finally, using numerical simulations, we show that our algorithms outperform the state-of-the-art methods in terms of accuracy and time/memory complexities, especially in the low-dimensional measurement regime.
{"title":"State and Sparse Input Estimation in Linear Dynamical Systems Using Low-Dimensional Measurements","authors":"Rupam Kalyan Chakraborty;Geethu Joseph;Chandra R. Murthy","doi":"10.1109/OJCSYS.2025.3624615","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3624615","url":null,"abstract":"Sparsity constraints on the control inputs of a linear dynamical system naturally arise in several practical applications such as networked control, computer vision, seismic signal processing, and cyber-physical systems. In this work, we consider the problem of jointly estimating the states and sparse inputs of such systems from low-dimensional (compressive) measurements. Due to the low-dimensional measurements, conventional Kalman filtering and smoothing algorithms fail to accurately estimate the states and inputs. We present a Bayesian approach that exploits the input sparsity to significantly improve estimation accuracy. Sparsity in the input estimates is promoted by using different prior distributions on the input. We investigate two main approaches: regularizer-based maximum a posteriori estimation and Bayesian learning-based estimation. We also extend the approaches to handle control inputs with common support and analyze the time and memory complexities of the presented algorithms. Finally, using numerical simulations, we show that our algorithms outperform the state-of-the-art methods in terms of accuracy and time/memory complexities, especially in the low-dimensional measurement regime.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"581-596"},"PeriodicalIF":0.0,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11215643","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560628","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 : 2025-10-10DOI: 10.1109/OJCSYS.2025.3620209
Fernando Castañeda;Jason J. Choi;Wonsuhk Jung;Bike Zhang;Claire J. Tomlin;Koushil Sreenath
Learning-based control has demonstrated great promise for handling complex tasks in various applications. However, ensuring system safety under uncertain dynamics remains a significant challenge. Control Barrier Functions (CBFs) offer mathematical tools for enforcing safety constraints given known system dynamics, yet their guarantees can be lost in the presence of model errors. In this article, we present a framework that combines model-based safety methods with data-driven techniques to guarantee safety recursively for systems with uncertain dynamics. We build upon our previous work, where Gaussian Process (GP) regression was utilized to quantify uncertainty in model-based CBF constraints, resulting in a second-order cone program (SOCP) controller. When the SOCP is feasible at a state, it provides a pointwise probabilistic safety guarantee. A critical innovation we develop further in this work is an event-triggered online data collection algorithm that actively and safely gathers data to provide the recursive feasibility of the SOCP-based controller. By continuously assessing the sufficiency of data based on the feasibility measure of the SOCP, our method triggers safe exploratory actions when necessary to reduce the uncertainty in critical control directions. This approach ensures that a feasible, safety-preserving control input is always available, thereby establishing forward invariance of the safe set with high probability, even in previously unexplored regions. We validate the proposed framework through two numerical simulation experiments.
{"title":"Recursively Feasible Probabilistic Safe Online Learning With Control Barrier Functions","authors":"Fernando Castañeda;Jason J. Choi;Wonsuhk Jung;Bike Zhang;Claire J. Tomlin;Koushil Sreenath","doi":"10.1109/OJCSYS.2025.3620209","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3620209","url":null,"abstract":"Learning-based control has demonstrated great promise for handling complex tasks in various applications. However, ensuring system safety under uncertain dynamics remains a significant challenge. Control Barrier Functions (CBFs) offer mathematical tools for enforcing safety constraints given known system dynamics, yet their guarantees can be lost in the presence of model errors. In this article, we present a framework that combines model-based safety methods with data-driven techniques to guarantee safety recursively for systems with uncertain dynamics. We build upon our previous work, where Gaussian Process (GP) regression was utilized to quantify uncertainty in model-based CBF constraints, resulting in a second-order cone program (SOCP) controller. When the SOCP is feasible at a state, it provides a pointwise probabilistic safety guarantee. A critical innovation we develop further in this work is an event-triggered online data collection algorithm that actively and safely gathers data to provide the recursive feasibility of the SOCP-based controller. By continuously assessing the sufficiency of data based on the feasibility measure of the SOCP, our method triggers safe exploratory actions when necessary to reduce the uncertainty in critical control directions. This approach ensures that a feasible, safety-preserving control input is always available, thereby establishing forward invariance of the safe set with high probability, even in previously unexplored regions. We validate the proposed framework through two numerical simulation experiments.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"531-548"},"PeriodicalIF":0.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11199905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455922","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 : 2025-10-10DOI: 10.1109/OJCSYS.2025.3620149
Shanting Wang;Panagiotis Typaldos;Chenjun Li;Andreas A. Malikopoulos
In this paper, we introduce VisioPath, a novel framework combining vision-language models (VLMs) with model predictive control (MPC) to enable safe autonomous driving in dynamic traffic environments. The proposed approach leverages a bird's-eye view video processing pipeline and zero-shot VLM capabilities to obtain structured information about surrounding vehicles, including their positions, dimensions, and velocitie, while providing semantically-informed initial trajectory guesses that warm-start the optimizer and enable contextually-aware navigation decisions (e.g., yielding to emergency vehicles). Using this rich perception output, we shape elliptical collision-avoidance potential fields around other traffic participants, which are seamlessly integrated into a finite-horizon optimal control problem for trajectory planning. The resulting trajectory optimization is solved via differential dynamic programming and is embedded in an event-triggered MPC loop. To ensure collision-free motion, a safety verification layer is incorporated in the framework that provides an assessment of potential unsafe trajectories. Extensive simulations in SUMO and CARLA simulators demonstrate that VisioPath outperforms other baseline approaches, such as conventional MPC, A*, RRT and CBF methods, across multiple metrics. By combining modern AI-driven perception with the rigorous foundation of optimal control, VisioPath represents a significant step forward in safe trajectory planning for complex traffic systems.
{"title":"VisioPath: Vision-Language Enhanced Model Predictive Control for Safe Autonomous Navigation in Mixed Traffic","authors":"Shanting Wang;Panagiotis Typaldos;Chenjun Li;Andreas A. Malikopoulos","doi":"10.1109/OJCSYS.2025.3620149","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3620149","url":null,"abstract":"In this paper, we introduce <italic>VisioPath</i>, a novel framework combining vision-language models (VLMs) with model predictive control (MPC) to enable safe autonomous driving in dynamic traffic environments. The proposed approach leverages a bird's-eye view video processing pipeline and zero-shot VLM capabilities to obtain structured information about surrounding vehicles, including their positions, dimensions, and velocitie, while providing semantically-informed initial trajectory guesses that warm-start the optimizer and enable contextually-aware navigation decisions (e.g., yielding to emergency vehicles). Using this rich perception output, we shape elliptical collision-avoidance potential fields around other traffic participants, which are seamlessly integrated into a finite-horizon optimal control problem for trajectory planning. The resulting trajectory optimization is solved via differential dynamic programming and is embedded in an event-triggered MPC loop. To ensure collision-free motion, a safety verification layer is incorporated in the framework that provides an assessment of potential unsafe trajectories. Extensive simulations in SUMO and CARLA simulators demonstrate that <italic>VisioPath</i> outperforms other baseline approaches, such as conventional MPC, A*, RRT and CBF methods, across multiple metrics. By combining modern AI-driven perception with the rigorous foundation of optimal control, <italic>VisioPath</i> represents a significant step forward in safe trajectory planning for complex traffic systems.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"562-580"},"PeriodicalIF":0.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11199901","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510199","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 : 2025-10-09DOI: 10.1109/OJCSYS.2025.3619810
V. Scordamaglia;M. Mattei;G. Franzé
In this paper, a novel solution for addressing the control allocation problem for over-actuated autonomous aircraft is presented. Inparticular, a detailed High Altitude Performance Demonstrator (HAPD) is used to show the effectiveness of the control/allocation architecture. The novelty of the proposed solution consists of designing a model predictive controller compliant with input saturations, geometric constraints, model uncertainties and enjoying tracking capabilities to be used during the online operations to adapt the nominal allocation unit to the time-varying conditions arising from the nonlinear aircraft dynamics. To make this approach viable, the state trajectories of the nonlinear envelope are formally embedded into those pertaining to a norm-bounded linear description. Then the allocation task is addressed by defining an online reference generator in charge of providing a feasible reference trajectory compatible with time-varying flight conditions. Finally, the nominal allocation is adapted online by exploiting state prediction features of the model predictive controller. A simulation campaign, involving comparisons with a well-known competitor, is performed by enlightening the effectiveness of the proposed approach in fulfilling constraints, ensuring accurate trajectory tracking and optimally allocating the control effort.
{"title":"Norm-Bounded Model Predictive Control Allocation Strategy for an Over-Actuated Aircraft","authors":"V. Scordamaglia;M. Mattei;G. Franzé","doi":"10.1109/OJCSYS.2025.3619810","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3619810","url":null,"abstract":"In this paper, a novel solution for addressing the control allocation problem for over-actuated autonomous aircraft is presented. Inparticular, a detailed High Altitude Performance Demonstrator (HAPD) is used to show the effectiveness of the control/allocation architecture. The novelty of the proposed solution consists of designing a model predictive controller compliant with input saturations, geometric constraints, model uncertainties and enjoying tracking capabilities to be used during the online operations to adapt the nominal allocation unit to the time-varying conditions arising from the nonlinear aircraft dynamics. To make this approach viable, the state trajectories of the nonlinear envelope are formally embedded into those pertaining to a norm-bounded linear description. Then the allocation task is addressed by defining an online reference generator in charge of providing a feasible reference trajectory compatible with time-varying flight conditions. Finally, the nominal allocation is adapted online by exploiting state prediction features of the model predictive controller. A simulation campaign, involving comparisons with a well-known competitor, is performed by enlightening the effectiveness of the proposed approach in fulfilling constraints, ensuring accurate trajectory tracking and optimally allocating the control effort.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"518-530"},"PeriodicalIF":0.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11197644","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405353","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}
This manuscript presents a framework for trajectory generation for soft continuum robots using principles from optimal control. The problem is constrained over the partial differential kinematic equations of the Cosserat rod model, capturing all modes of deformation which soft continuum systems can achieve. The derived optimal control problem is transformed to a nonlinear programming problem which can be solved using the Bernstein polynomial basis. Non-unit quaternions are used to discretely apply rotational transformations to values approximated over Bernstein polynomials, allowing individual components of strain to be constrained separately. Included within this manuscript are numerical results as well as validation through experimental results.
{"title":"Constrained Path Planning for Soft Continuum Robots With Bernstein Surfaces","authors":"Maxwell Hammond;Ean Lovett;Vincenzo Pugliese;Venanzio Cichella;Caterina Lamuta","doi":"10.1109/OJCSYS.2025.3617288","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3617288","url":null,"abstract":"This manuscript presents a framework for trajectory generation for soft continuum robots using principles from optimal control. The problem is constrained over the partial differential kinematic equations of the Cosserat rod model, capturing all modes of deformation which soft continuum systems can achieve. The derived optimal control problem is transformed to a nonlinear programming problem which can be solved using the Bernstein polynomial basis. Non-unit quaternions are used to discretely apply rotational transformations to values approximated over Bernstein polynomials, allowing individual components of strain to be constrained separately. Included within this manuscript are numerical results as well as validation through experimental results.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"618-628"},"PeriodicalIF":0.0,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11190071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674798","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 : 2025-09-26DOI: 10.1109/OJCSYS.2025.3614875
Thomas O. de Jong;Khemraj Shukla;Mircea Lazar
In this paper, we consider the design of model predictive control (MPC) algorithms based on deep operator neural networks (DeepONets) (Lu et al. 2021). These neural networks are capable of accurately approximating real- and complex-valued solutions (Jiang et al. 2024) of continuous-time nonlinear systems without relying on recurrent architectures. The DeepONet architecture is made up of two feedforward neural networks: the branch network, which encodes the input function space, and the trunk network, which represents dependencies on temporal variables or initial conditions. Utilizing the original DeepONet architecture (Lu et al. 2021) as a predictor within MPC for Multi-Input Multi-Output (MIMO) systems requires multiple branch networks, to generate multi-output predictions, one for each input. Moreover, to predict multiple time steps into the future, the network has to be evaluated multiple times. Motivated by this, we introduce a multi-step DeepONet (MS-DeepONet) architecture that computes in one-shot multi-step predictions of system outputs from multi-step input sequences, which is better suited for MPC. We prove that the MS-DeepONet is a universal approximator in terms of multi-step sequence prediction. Additionally, we develop automated hyperparameter selection strategies and implement MPC frameworks using both the standard DeepONet and the proposed MS-DeepONet architectures in PyTorch. We compare MS-DeepONet, standard DeepONet, and LSTM-based controllers on learning and predictive control tasks for the Van der Pol oscillator and the quadruple tank process. The MS-DeepONet is also evaluated on a challenging cart–pendulum system, where it successfully learns swing-up and stabilization policies. Across the examples, MS-DeepONet outperforms standard DeepONet in prediction accuracy and control performance, and achieves significantly lower computation times than Long Short-Term Memory (LSTM) based MPC.
在本文中，我们考虑了基于深度算子神经网络（DeepONets）的模型预测控制（MPC）算法的设计（Lu et al. 2021）。这些神经网络能够准确地逼近连续时间非线性系统的实值和复值解（Jiang et al. 2024），而不依赖于循环架构。DeepONet架构由两个前馈神经网络组成：分支网络编码输入函数空间，主干网络表示对时间变量或初始条件的依赖关系。利用原始DeepONet架构（Lu et al. 2021）作为多输入多输出（MIMO）系统的MPC预测器，需要多个分支网络来生成多输出预测，每个输入一个。此外，为了预测未来的多个时间步长，必须对网络进行多次评估。基于此，我们引入了一种多步DeepONet （MS-DeepONet）架构，该架构可以对多步输入序列的系统输出进行一次多步预测，更适合MPC。我们证明了MS-DeepONet在多步序列预测方面是一个通用逼近器。此外，我们开发了自动超参数选择策略，并在PyTorch中使用标准DeepONet和提议的MS-DeepONet架构实现MPC框架。我们比较了MS-DeepONet、标准DeepONet和基于lstm的控制器对Van der Pol振荡器和四缸过程的学习和预测控制任务。MS-DeepONet还在一个具有挑战性的小车摆系统中进行了评估，成功地学习了摆动和稳定策略。在所有示例中，MS-DeepONet在预测精度和控制性能方面优于标准DeepONet，并且比基于长短期记忆（LSTM）的MPC实现了显着降低的计算时间。
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