Pub Date : 2026-03-02DOI: 10.1109/OJCSYS.2026.3669038
S. Hafner;T. De Ponti;E. Smeur
In the aerospace control domain, Nonlinear Dynamic Inversion (NDI)-based control laws are widely spread. As a variation to Incremental Nonlinear Dynamic Inversion (INDI), the sensory Nonlinear Dynamic Inversion (sNDI) method was recently developed. Both methods rely on replacing model knowledge with sensor measurements. However, the methods differ in how the pseudo-controls are allocated: INDI allocates them incrementally, while sNDI allocates them globally, with corresponding advantages and disadvantages. While INDI requires a restoring mechanism in the control allocation due to path dependency issues in overactuated nonlinear systems, sNDI does not experience this problem. In addition to the comparison, the paper demonstrates that both methods lead to identical results if restoring is applied in the control allocation of INDI. Even though sNDI and INDI with restoring can lead to limit cycles for theoretical non-linear overactuated systems, the practical applicability of this approach to transition electrical vertical take-off and landing vehicles (eVTOL) is demonstrated in flight tests of the Variable Skew Quad Plane.
{"title":"On the Equivalence of Sensory and Incremental Nonlinear Dynamic Inversion","authors":"S. Hafner;T. De Ponti;E. Smeur","doi":"10.1109/OJCSYS.2026.3669038","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3669038","url":null,"abstract":"In the aerospace control domain, Nonlinear Dynamic Inversion (NDI)-based control laws are widely spread. As a variation to Incremental Nonlinear Dynamic Inversion (INDI), the sensory Nonlinear Dynamic Inversion (sNDI) method was recently developed. Both methods rely on replacing model knowledge with sensor measurements. However, the methods differ in how the pseudo-controls are allocated: INDI allocates them incrementally, while sNDI allocates them globally, with corresponding advantages and disadvantages. While INDI requires a restoring mechanism in the control allocation due to path dependency issues in overactuated nonlinear systems, sNDI does not experience this problem. In addition to the comparison, the paper demonstrates that both methods lead to identical results if restoring is applied in the control allocation of INDI. Even though sNDI and INDI with restoring can lead to limit cycles for theoretical non-linear overactuated systems, the practical applicability of this approach to transition electrical vertical take-off and landing vehicles (eVTOL) is demonstrated in flight tests of the Variable Skew Quad Plane.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"136-148"},"PeriodicalIF":0.0,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11417726","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440621","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}
Existing closed-loop controllers for functional electrical stimulation are prone to exceeding subject-specific stimulation limits, thereby limiting performance and also accelerating stimulation-induced muscle fatigue. In view of these challenges, this paper develops a Lyapunov-based model predictive control method to control knee flexion and extension during input-delayed stimulation. The method incorporates a contractive constraint under an electromechanical delay (EMD) compensation control law that achieves system stability despite an unknown constant input delay, bounded control constraints, and imperfectly estimated model parameters. A Lyapunov stability analysis proves that the Lyapunov constraint renders the closed-loop error ultimately bounded, and gain conditions are provided to guarantee recursive feasibility. LMPC's performance is explored in simulation and experiments and compared against an analytical proportional derivative-dynamic surface controller (PD-DSC) and a proportional-derivative-delay compensation (PD-DC) controller. In simulation, LMPC improved tracking root-mean-square error by 75.57% and 71.71%, on average, compared to PD-DSC and PD-DC, respectively. We observed that incorporating a slackening term often improved LMPC's tracking performance, although strict enforcement of the Lyapunov constraint was superior when there was greater EMD estimation error. Additionally, unlike PD-DSC and PD-DC, LMPC was not destabilized when EMD was overestimated or underestimated, nor did it violate input constraints. In knee extension experiments, LMPC respected input constraints, which PD-DSC did not. The LMPC was also validated in overground walking experiments to test its ability to produce both knee flexion and extension in participants with and without spinal cord injury.
{"title":"Lyapunov-Based Nonlinear Model Predictive Control of Input-Delayed Functional Electrical Stimulation: Investigative Simulations and Experiments","authors":"Krysten Lambeth;Ziyue Sun;Ashwin Iyer;Vidisha Ganesh;Nitin Sharma","doi":"10.1109/OJCSYS.2026.3666636","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3666636","url":null,"abstract":"Existing closed-loop controllers for functional electrical stimulation are prone to exceeding subject-specific stimulation limits, thereby limiting performance and also accelerating stimulation-induced muscle fatigue. In view of these challenges, this paper develops a Lyapunov-based model predictive control method to control knee flexion and extension during input-delayed stimulation. The method incorporates a contractive constraint under an electromechanical delay (EMD) compensation control law that achieves system stability despite an unknown constant input delay, bounded control constraints, and imperfectly estimated model parameters. A Lyapunov stability analysis proves that the Lyapunov constraint renders the closed-loop error ultimately bounded, and gain conditions are provided to guarantee recursive feasibility. LMPC's performance is explored in simulation and experiments and compared against an analytical proportional derivative-dynamic surface controller (PD-DSC) and a proportional-derivative-delay compensation (PD-DC) controller. In simulation, LMPC improved tracking root-mean-square error by 75.57% and 71.71%, on average, compared to PD-DSC and PD-DC, respectively. We observed that incorporating a slackening term often improved LMPC's tracking performance, although strict enforcement of the Lyapunov constraint was superior when there was greater EMD estimation error. Additionally, unlike PD-DSC and PD-DC, LMPC was not destabilized when EMD was overestimated or underestimated, nor did it violate input constraints. In knee extension experiments, LMPC respected input constraints, which PD-DSC did not. The LMPC was also validated in overground walking experiments to test its ability to produce both knee flexion and extension in participants with and without spinal cord injury.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"121-135"},"PeriodicalIF":0.0,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11404180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362500","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 : 2026-02-13DOI: 10.1109/OJCSYS.2026.3662069
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Pub Date : 2026-02-13DOI: 10.1109/OJCSYS.2026.3662073
{"title":"IEEE Control Systems Society Information","authors":"","doi":"10.1109/OJCSYS.2026.3662073","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3662073","url":null,"abstract":"","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11396398","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175975","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 : 2026-01-28DOI: 10.1109/OJCSYS.2026.3657987
Zhonggang Li;Raj Thilak Rajan
Affine formation control (AFC) is a subset of formation control methods that enables coordinated multiagent movement while preserving affine relationships, and has recently gained increasing popularity due to its utility across diverse applications. AFC is inherently distributed, where each agent's local controller relies on the relative displacements of neighboring agents. The unavailability of these measurements in practice, due to node or communication failures, leads to a change in the underlying graph topology and subsequently causes instability or sub-optimal performance. In this work, each edge in the graph is modeled using a state-space framework, allowing the corresponding edge-states to be estimated with or without up-to-date measurements. We then propose a Kalman-based estimation framework where we fuse both temporal information from agents' dynamics and spatial information, which is derived from the geometry of the affine formations. We give convergence guarantees and optimality analysis on the proposed algorithm, and numerical validations show the enhanced robustness of AFC against these topology changes in several practical scenarios.
{"title":"Geometry-Aware Edge-State Tracking for Robust Affine Formation Control","authors":"Zhonggang Li;Raj Thilak Rajan","doi":"10.1109/OJCSYS.2026.3657987","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3657987","url":null,"abstract":"Affine formation control (AFC) is a subset of formation control methods that enables coordinated multiagent movement while preserving affine relationships, and has recently gained increasing popularity due to its utility across diverse applications. AFC is inherently distributed, where each agent's local controller relies on the relative displacements of neighboring agents. The unavailability of these measurements in practice, due to node or communication failures, leads to a change in the underlying graph topology and subsequently causes instability or sub-optimal performance. In this work, each edge in the graph is modeled using a state-space framework, allowing the corresponding edge-states to be estimated with or without up-to-date measurements. We then propose a Kalman-based estimation framework where we fuse both temporal information from agents' dynamics and spatial information, which is derived from the geometry of the affine formations. We give convergence guarantees and optimality analysis on the proposed algorithm, and numerical validations show the enhanced robustness of AFC against these topology changes in several practical scenarios.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"107-120"},"PeriodicalIF":0.0,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11364073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175974","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 : 2026-01-26DOI: 10.1109/OJCSYS.2026.3657701
Vipul K. Sharma;S. Sivaranjani
We address the problem of safe adaptive control for a class of nonlinear systems with dynamical uncertainties, while satisfying control barrier function (CBF) type safety constraints with user-defined risk tolerances at all times. We develop a model reference adaptive control framework that provably guarantees safety in two stages. In the first stage, we design a safe reference model to generate reference trajectories that satisfy CBF-based safety conditions. However, asymptotically tracking a safe reference trajectory does not automatically guarantee safety at every time step. Therefore, in the second stage, we formulate a chance-constrained optimization problem for the nonlinear system with dynamical uncertainties to track the reference model, while provably guaranteeing CBF-based safety constraint satisfaction at each time step up to a user-defined risk bound. We then provide a risk-tunable sampling-based scenario design approach to tune parameterized controllers that solve this optimization problem. In addition, for the special case of linear dynamics, we provide conditions on the uncertainty samples for the existence of controller parameters that can guarantee safe tracking. We illustrate the performance of our framework on a quadcopter navigation problem with obstacle avoidance constraints.
{"title":"Risk-Tunable Safe Adaptive Control for Nonlinear Systems Under Dynamical Uncertainties","authors":"Vipul K. Sharma;S. Sivaranjani","doi":"10.1109/OJCSYS.2026.3657701","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3657701","url":null,"abstract":"We address the problem of safe adaptive control for a class of nonlinear systems with dynamical uncertainties, while satisfying control barrier function (CBF) type safety constraints with user-defined risk tolerances at all times. We develop a model reference adaptive control framework that provably guarantees safety in two stages. In the first stage, we design a safe reference model to generate reference trajectories that satisfy CBF-based safety conditions. However, asymptotically tracking a safe reference trajectory does not automatically guarantee safety at every time step. Therefore, in the second stage, we formulate a chance-constrained optimization problem for the nonlinear system with dynamical uncertainties to track the reference model, while provably guaranteeing CBF-based safety constraint satisfaction at each time step up to a user-defined risk bound. We then provide a risk-tunable sampling-based scenario design approach to tune parameterized controllers that solve this optimization problem. In addition, for the special case of linear dynamics, we provide conditions on the uncertainty samples for the existence of controller parameters that can guarantee safe tracking. We illustrate the performance of our framework on a quadcopter navigation problem with obstacle avoidance constraints.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"91-106"},"PeriodicalIF":0.0,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11363681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175979","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 : 2026-01-14DOI: 10.1109/OJCSYS.2026.3654320
{"title":"2025 Index IEEE Open Journal of Control Systems","authors":"","doi":"10.1109/OJCSYS.2026.3654320","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3654320","url":null,"abstract":"","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"629-639"},"PeriodicalIF":0.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11353212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982218","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 : 2026-01-12DOI: 10.1109/OJCSYS.2026.3653649
Ilia Shilov;Ezzat Elokda;Sophie Hall;Heinrich H. Nax;Saverio Bolognani
Many multi-agent socio-technical systems rely on aggregating heterogeneous agents’ costs into a social cost function (SCF) to coordinate resource allocation in domains such as energy grids, water allocation, or traffic management. The choice of SCF often entails implicit assumptions and may lead to undesirable outcomes if not rigorously justified. In this paper, we demonstrate that what determines which SCF ought to be used is the degree to which individual costs can be compared across agents and which axioms the aggregation shall fulfill. Drawing on the results from social choice theory, we provide guidance on how this process can be used in control applications. We demonstrate which assumptions about interpersonal utility comparability - ranging from ordinal level comparability to full cardinal comparability - together with a choice of desirable axioms, inform the selection of a correct SCF, be it the classical utilitarian sum, the Nash SCF, or maximin. Thus, fixing comparability level first, then choosing an objective from the compatible class, and reporting both as part of the specification, makes the fairness and efficiency consequences transparent. We demonstrate how the proposed framework can be applied for principled allocations of water, transportation, and energy resources.
{"title":"Welfare and Cost Aggregation for Multi-Agent Control: When to Choose Which Social Cost Function, and Why?","authors":"Ilia Shilov;Ezzat Elokda;Sophie Hall;Heinrich H. Nax;Saverio Bolognani","doi":"10.1109/OJCSYS.2026.3653649","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3653649","url":null,"abstract":"Many multi-agent socio-technical systems rely on aggregating heterogeneous agents’ costs into a social cost function (SCF) to coordinate resource allocation in domains such as energy grids, water allocation, or traffic management. The choice of SCF often entails implicit assumptions and may lead to undesirable outcomes if not rigorously justified. In this paper, we demonstrate that what determines which SCF ought to be used is the degree to which individual costs can be compared across agents and which axioms the aggregation shall fulfill. Drawing on the results from social choice theory, we provide guidance on how this process can be used in control applications. We demonstrate which assumptions about interpersonal utility comparability - ranging from ordinal level comparability to full cardinal comparability - together with a choice of desirable axioms, inform the selection of a correct SCF, be it the classical utilitarian sum, the Nash SCF, or maximin. Thus, fixing comparability level first, then choosing an objective from the compatible class, and reporting both as part of the specification, makes the fairness and efficiency consequences transparent. We demonstrate how the proposed framework can be applied for principled allocations of water, transportation, and energy resources.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"80-90"},"PeriodicalIF":0.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11347468","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175980","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 : 2026-01-12DOI: 10.1109/OJCSYS.2026.3651197
Ainur Zhaikhan;Malek Khammassi;Ali H. Sayed
This work leverages adaptive social learning to estimate partially observable global states in multi-agent reinforcement learning (MARL) problems. Unlike existing methods, the proposed approach enables the concurrent operation of social learning and reinforcement learning. Specifically, it alternates between a single step of social learning and a single step of MARL, eliminating the need for the time- and computation-intensive two-timescale learning frameworks. Theoretical guarantees are provided to support the effectiveness of the proposed method. Simulation results verify that the performance of the proposed methodology can approach that of reinforcement learning when the true state is known.
{"title":"Policy Optimization in Multi-Agent Settings Under Partially Observable Environments","authors":"Ainur Zhaikhan;Malek Khammassi;Ali H. Sayed","doi":"10.1109/OJCSYS.2026.3651197","DOIUrl":"https://doi.org/10.1109/OJCSYS.2026.3651197","url":null,"abstract":"This work leverages adaptive social learning to estimate partially observable global states in multi-agent reinforcement learning (MARL) problems. Unlike existing methods, the proposed approach enables the concurrent operation of social learning and reinforcement learning. Specifically, it alternates between a single step of social learning and a single step of MARL, eliminating the need for the time- and computation-intensive two-timescale learning frameworks. Theoretical guarantees are provided to support the effectiveness of the proposed method. Simulation results verify that the performance of the proposed methodology can approach that of reinforcement learning when the true state is known.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"5 ","pages":"64-79"},"PeriodicalIF":0.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11329162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175978","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 : 2025-12-17DOI: 10.1109/OJCSYS.2025.3628513
{"title":"IEEE Control Systems Society Publication Information","authors":"","doi":"10.1109/OJCSYS.2025.3628513","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3628513","url":null,"abstract":"","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11303147","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778370","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}
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