{"title":"Geometric Perspectives on Preference Alignment: From Replicator Dynamics to Mirror Descent","authors":"Bowen Su","doi":"10.1109/LCSYS.2026.3689885","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3689885","url":null,"abstract":"Direct Preference Optimization (DPO) has revolutionized the alignment of large language models by implicitly optimizing a reward function via pairwise comparisons. While its empirical efficacy is well-documented, its theoretical convergence properties remain under-explored from a control-theoretic perspective. This letter casts DPO as a Riemannian gradient flow on the probability simplex equipped with the Fisher–Rao metric. Within this finite-dimensional policy-space model, we analyze the continuous-time dynamics through the Fisher–Rao Riemannian geometry of the probability simplex and the discrete-time update through the entropic mirror geometry induced by the Kullback–Leibler divergence. In particular, we prove that the associated continuous-time gradient flow coincides with a generalized replicator dynamics. We then establish a local geometric characterization of the objective by proving local strong geodesic convexity near a critical point under suitable structural assumptions, which in turn yields a local exponential stability result for the corresponding gradient flow. On the discrete-time side, we show that the induced policy-space update is exactly a KL-Mirror Descent step in the tabular setting and derive a descent guarantee under a relative smoothness assumption. These results provide a geometric interpretation of DPO in an idealized finite-outcome model and clarify the role of information geometry in preference-based optimization.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"367-372"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003570","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}
{"title":"Exact and Approximate Convex Reformulation of Linear Stochastic Optimal Control With Chance Constraints","authors":"Tanmay Dokania;Yashwanth Kumar Nakka","doi":"10.1109/LCSYS.2026.3701076","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3701076","url":null,"abstract":"In this letter, we present an equivalent convex optimization formulation for discrete-time stochastic linear systems subject to linear chance constraints, alongside two tight convex relaxations for quadratic chance constraints. By lifting the state vector to explicitly encode moment information, the formulation captures linear chance constraints on states and controls across multiple time steps exactly, without conservatism, yielding strict improvements in both feasibility and optimality. For quadratic chance constraints, we derive convex approximations that are provably less conservative than existing methods. We validate the framework for minimum-snap trajectory generation in a quadrotor, demonstrating that the proposed approach achieves cost reductions of <inline-formula> <tex-math>${41}%$ </tex-math></inline-formula>-<inline-formula> <tex-math>${67}%$ </tex-math></inline-formula> and remains feasible at noise levels an order of magnitude beyond the operating range of prior formulations.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"787-792"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442363","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}
{"title":"Combinatorial Admissibility in Control-Affine Networks","authors":"Daniel Zelazo;Louis Theran","doi":"10.1109/LCSYS.2026.3702047","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3702047","url":null,"abstract":"We study synchronization of heterogeneous control-affine nonlinear agents interconnected through diffusive (relative-output) measurements. We separate the design into an edge-space step—specifying a stabilizing model evolution for relative outputs—and a lift step—realizing the prescribed edge motion using the agents’ allowable input directions, constrained by the control-affine geometry of the agents. We introduce an admissibility notion that characterizes when an edge-driven diffusive design is feasible. We derive checkable combinatorial certificates that connect graph topology and actuation limits directly to admissibility, so that feasible edge dynamics can be verified in a practical and transparent way. The results are illustrated on synchronization of nonlinear oscillators.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"757-762"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442740","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}
{"title":"PID-Compatible Compensation for Containerized Wireless Industrial Control Under Timing Uncertainty","authors":"Luyiyang Gong;Rui Wang","doi":"10.1109/LCSYS.2026.3727663","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3727663","url":null,"abstract":"Timing uncertainty in containerized wireless industrial control arises from the combined effects of wireless communication, protocol processing, and container virtualization. This letter proposes a reinforcement learning (RL)-assisted proportional–integral–derivative (PID)-compatible compensation framework for such uncertainty. A nonlinear controller-level model represents bounded delay and correlated read/write losses while distinguishing candidate from plant-applied inputs. A closed-form, safety-projected additive compensator retains the PID structure, while a cloud-trained RL policy schedules the controller parameters. Sufficient conditions are derived for uniform ultimate boundedness of the tracking error on a compact validation region under intermittent communication failures. Quadruple-tank results show that the proposed method achieves the lowest mean squared error, integral absolute error, and worst-case settling time among all evaluated methods, including fixed and robust controllers.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"2287-2292"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925225","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}
{"title":"Agent Utilities Over Generalized Voronoi Regions and Their Gradients","authors":"Andre N. Costa;Petter Ögren;Carlos H. C. Ribeiro","doi":"10.1109/LCSYS.2026.3729004","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3729004","url":null,"abstract":"Voronoi partitions are widely used to model spatial dominance in multi-agent systems, but Euclidean distance can be a limited proxy when agents differ in initial velocity or dynamics (e.g., drag). This letter studies Cost-Induced Voronoi (CIV) regions, where spatial partitions are induced by transfer costs depending on the full agent state and dynamics. As a closed-form special case, we consider infinite-horizon Linear Quadratic Regulator costs for second-order agents with linear drag, yielding LQR-CIV regions. When a scalar utility is defined over a space, we can define agent utility as the integral over their CIV regions. To enable agents to maximize their spatial utility, we derive a closed-form boundary-integral expression for the utility gradient using the Reynolds Transport Theorem. This closed-form gradient bypasses the need for computationally expensive finite-difference approximations. In a soccer-inspired case study, we demonstrate that our analytical gradient reduces computational cost by a factor of 25 compared to finite-difference alternatives.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"2341-2346"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925379","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}
{"title":"Bearing Invariance and Robust Disturbance Thresholds for Hybrid Camera-Based Unicycle Stabilization","authors":"Labid Bin Bashar","doi":"10.1109/LCSYS.2026.3732181","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3732181","url":null,"abstract":"A recent hybrid camera-based stabilizer regulates a unicycle robot by switching between front and rear onboard camera-coordinate representations. We analyze whether the active-camera bearing can reach the field-of-view (FOV) boundary under nominal closed-loop dynamics and additive yaw-rate disturbances. Using a range-independent two-state bearing model, we first recover the known result that the bearing remains within <inline-formula> <tex-math>$|theta |le pi /2$ </tex-math></inline-formula>. We then derive a gain condition that keeps the bearing strictly inside the FOV, an initial-condition safe set valid for every initial range, and the largest persistent yaw-rate disturbance bound that preserves <inline-formula> <tex-math>$|theta |le pi /2$ </tex-math></inline-formula>. We also show that any larger bound can violate this property and give explicit error and sampling-age limits that prevent false camera switching. Numerical sweeps support the analytical results and illustrate their disturbance limits.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"2383-2388"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985665","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}
{"title":"Federated Behavioral Identification via Grassmann Manifold Aggregation","authors":"Shiang Cao;YangQuan Chen","doi":"10.1109/LCSYS.2026.3698430","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3698430","url":null,"abstract":"This letter studies the identification of linear systems using behaviors generated by multiple similar systems. Instead of estimating a parametric model, we adopt a framework rooted in behavioral systems theory, where restricted behaviors correspond to fixed-dimensional linear subspaces represented as points on the Grassmann manifold. Motivated by federated learning, we develop a manifold-based aggregation mechanism that combines information from multiple systems while preserving data privacy. The method aggregates locally identified behavior subspaces via geometric operations on the Grassmann manifold, enabling collaborative system identification without sharing raw data. We show that, under mild assumptions, the aggregated identification is stable in the sense that its error is bounded by a constant multiple of the local identification error. Under stronger conditions, we further establish that the identification accuracy improves as the number of participating systems increases. Numerical simulations demonstrate that the proposed approach accurately reconstructs the system dynamics using data from multiple similar systems.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"517-522"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236332","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}
{"title":"Meta-Federated Learning for Robust Adaptive Control in Non-Stationary Environments: A Lyapunov-Guided Approach","authors":"Vaibhav Sharma;Kamal Raj Pardasani","doi":"10.1109/LCSYS.2026.3676121","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3676121","url":null,"abstract":"Traditional Federated Learning approaches often fail in control settings due to the instability of the average and lack of rigorous safety guarantees. This letter introduces Lyapunov-Guided Meta-Federated Optimization (LGM-FO), a framework synergizing Model-Agnostic Meta-Learning with Lyapunov control theory to address heterogeneity and non-stationarity in Cyber-Physical Systems. Our framework formulates the problem as meta-learning a robust control initialization integrated with a neural Lyapunov certificate to facilitate rapid adaptation while maintaining verifiable stability guarantees. To mitigate communication overhead, we employ an event-triggered aggregation protocol. Theoretically, we provide convergence proofs for the bilevel optimization, derive dynamic regret bounds of <inline-formula> <tex-math>$mathcal {O}(sqrt {T})$ </tex-math></inline-formula> for time-varying environments and establish asymptotic stability under asynchronous delays using the Razumikhin theorem. The framework demonstrates superior robustness and efficiency compared to standard federated baselines.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"175-180"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557803","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}
{"title":"A Priority-Ordered Swapping Algorithm for Submodular Maximization Problems","authors":"Xianlun Peng;Xiangyu Meng;Fangfei Li","doi":"10.1109/LCSYS.2026.3670932","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3670932","url":null,"abstract":"In this letter, we delve into the monotone submodular maximization problem with a cardinality constraint. We propose a priority-ordered swapping algorithm that iteratively improves the solution by swapping the lowest priority elements in the current solution set with elements from the ground set. This algorithm can effectively complement the greedy approach, especially when starting from the greedy solution. Moreover, we quantify the performance gap between our algorithm’s solution and the optimal solution, providing a theoretical guarantee for its effectiveness. We also identify conditions under which our algorithm outperforms standard greedy approaches in terms of approximation quality. In addition, the effectiveness of the proposed algorithm is further demonstrated by applying it to the multi-agent coverage problem.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"133-138"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557704","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}
{"title":"PFC-Based Output-Feedback Adaptive Control for Heterogeneous Nonlinear Multi-Agent Systems","authors":"Takao Sato;Ryuto Kobayashi","doi":"10.1109/LCSYS.2026.3712002","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3712002","url":null,"abstract":"This letter proposes an output-feedback adaptive control scheme for heterogeneous nonlinear multi-agent systems (MAS) based on the output feedback strictly passive (OFSP) property. For a network consisting of a leader with a known trajectory and followers with unknown dynamics, a parallel feedforward compensator (PFC) is introduced to each follower to ensure the OFSP property of the augmented system. A distributed adaptive control law is then constructed based on the relative output errors among agents. By using mathematical induction, it is theoretically proven that all signals in the closed-loop system remain bounded and the tracking errors of the augmented outputs are uniformly bounded. Numerical simulations involving agents with diverse system orders and nonlinearities validate the effectiveness of the proposed approach.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"1849-1854"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148481072","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}