{"title":"A Robust Human-Autonomy Collaboration Framework With Experimental Validation","authors":"M. Yusuf Uzun;Emirhan Inanc;Yildiray Yildiz","doi":"10.1109/LCSYS.2024.3467188","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3467188","url":null,"abstract":"In this letter, we introduce a robust human-autonomy collaboration framework focusing on flight control applications. The objective is to optimize performance by always keeping the human operator in control of the vehicle while compensating for human limitations. A significant aspect of this framework is its robustness to human intent estimation errors. This is achieved by precisely modulating the automation assistance to prevent undesired interference with the human operator. We provide human-in-the-loop experimental results, demonstrating significant performance improvements when intent estimation is accurate. Experiments also validate that the pilots maintain vehicle control even when the estimation is faulty.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383485","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 System Fault Diagnosis Method Based on Labeled Time Petri Net With Data","authors":"Jian Song;Guanjun Liu","doi":"10.1109/LCSYS.2024.3466515","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3466515","url":null,"abstract":"Regarding the fault diagnosis issue in real-time discrete event systems, existing detection methods typically involve modeling using labeled time Petri net (LTPN), followed by using the generated modified state class graph (MSCG) from LTPN to detect faults in the system. The diagnosis results are classified into normal, uncertain, and faulty. However, existing fault detection methods are based on time sequences for diagnosis; they ignore the changes in data flow in the system, making it impossible to determine whether there is a fault in the system when the detection result is uncertain. To address this issue, this letter proposes a modeling method of LTPN with data (LTPND), which binds corresponding data element operations on the transitions of LTPN to simulate the data flow in the system. Subsequently, the MSCG of LTPND is constructed, and a corresponding fault diagnosis algorithm is proposed based on MSCG. By traversing all the paths that satisfy the fire time and sequence information of observable transitions, faults in the system are detected from the perspectives of time constraints and data element changes, thus accurately judging the specific situations of uncertain states. Finally, the feasibility and effectiveness of the proposed method are validated through case analysis.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397395","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":"Novel Sampled-Output Feedback Controller Guaranteeing Lyapunov Stability for Continuous-Time Uncertain Nonautonomous Nonlinear Systems","authors":"Jang-Hyun Park","doi":"10.1109/LCSYS.2024.3464331","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3464331","url":null,"abstract":"A novel output-feedback digital controller designed for continuous-time completely unknown nonlinear systems with inherent uncertainties is proposed in this letter. It addresses a broad class of general time-varying nonlinear systems characterized by significant unstructured uncertainties. Unlike previous methods, this controller does not require discretization of the controller or the controlled plant and is not limited to specific systems like Lur’e-type systems or upper-triangular nonlinear plants. Leveraging a discrete differentiator, it is rigorously demonstrated that the proposed digital control input, processed through a zero-order hold, ensures Lyapunov stability of the hybrid closed-loop system.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324234","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}
Grigoris Michos;George C. Konstantopoulos;Paul A. Trodden
{"title":"Dynamic Tube Control for DC Microgrids","authors":"Grigoris Michos;George C. Konstantopoulos;Paul A. Trodden","doi":"10.1109/LCSYS.2024.3464329","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3464329","url":null,"abstract":"This letter proposes a dynamic tube control approach for DC Microgrids (MGs) connected to constant power loads (CPL) that guarantees boundedness of the system dynamics, satisfaction of the desired operational constraints and closed-loop stability. Contrary to many approaches in the literature, we consider an explicit model of the dynamics to investigate the geometric effect of the load demand perturbations on the behaviour of the closed loop system. Combined with the use of nominal dynamics, i.e., dynamics parametrized by a constant load demand, this allows us to formulate, for the first time, necessary conditions for the existence of a tube around a nominal solution that bounds all possible uncertain trajectories stemming from perturbations of the load demand. Furthermore, we show that the computation of the tube follows a fully decentralized approach and its size is dependent on the nominal dynamics, which we use in the regulation of the nominal solution to reduce the conservativeness of the controller. The effectiveness of the proposed control architecture is illustrated in a simulated scenario.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383362","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":"On a Discrete-Time Networked Epidemic Model With Time-Varying Heterogeneous Delays","authors":"Fangzhou Liu;Lei Shi;Jinliang Shao;Qingchen Liu","doi":"10.1109/LCSYS.2024.3463499","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3463499","url":null,"abstract":"Delays caused by the incubation period of an infectious disease are inevitable in modeling the spreading of a real epidemic. With this in mind, our note proposes a novel discrete-time networked susceptible-infected-susceptible (SIS) epidemic model with delays. In this model, the independent edge-based time delay in the network is time-varying and heterogeneous. To prove the asymptotic stability of the model, a super-stochastic matrix based method is proposed to analyze the convergence of an infinite product. By using this method, a sufficient algebraic condition for the convergence of the model to the disease-free equilibrium point is established. The theoretical results obtained are verified by numerical simulations.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324249","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":"Byzantine Attack Resilient Bounded Consensus","authors":"Jinxin Miao;Zhenqian Wang;Xiwang Dong;Zhiqiang Zuo;Jinhu Lü","doi":"10.1109/LCSYS.2024.3457851","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3457851","url":null,"abstract":"Average consensus is a cornerstone of distributed systems, facilitating essential functionalities such as distributed information fusion, decision-making, and decentralized control. However, achieving the exact average consensus is challenging when partial nodes are compromised and act as Byzantine attackers by transmitting malicious messages using judiciously crafted patterns. Existing resilient consensus results can only guarantee that the consensus value under Byzantine attacks remains within the range defined by the maximum and minimum initial state values of all legitimate nodes. In addition, the consensus value is highly uncertain when attack strategies change. In this letter, we propose a new resilient consensus algorithm that ensures the consensus value falls within a much tighter bound. The bound contains the exact average consensus value and is solely determined by the initial states of legitimate nodes, regardless of the attack strategies employed by the Byzantine attackers. More interestingly, we demonstrate that the bound is the tightest achievable under our resilient consensus algorithm when the number of Byzantine attackers reaches the maximum threshold our algorithm can handle. Numerical simulations are given to validate the theoretical results.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274931","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":"Functional Diagnosability of Possibly Uncertain Systems","authors":"Carine Jauberthie;Nathalie Verdière;Louise Travé-Massuyès","doi":"10.1109/LCSYS.2024.3456230","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3456230","url":null,"abstract":"Diagnosability is a crucial attribute of systems and their instrumentation, ensuring that specified faults can be uniquely identified using the available sensors. In a model-based context, diagnosability is evaluated through analytical redundancy relations derived from the model by eliminating unknown variables. These relations, evaluated from sensor data, yield residuals, which indicate the system’s normal or faulty state. Ideally, residuals exhibit distinct values for different faults, generating unique fault signatures that facilitate fault discrimination and affirm system diagnosability. This letter presents a sufficient condition for the functional diagnosability of nonlinear dynamical systems, based on the functional linear independence of fault signatures. Unlike conventional diagnosability analysis, which focuses on residuals evaluated in a binary manner, 0 when not sensitive to a fault and 1 otherwise, functional diagnosability emphasizes the system’s behavior by evaluating the functional expressions of residuals defined as functional fault signatures. Evaluated from sensor data, functional signatures allow for an analysis of the whole residual trajectories. This advantageously increases the discriminating power. This approach leverages the symbolic framework of differential algebra, accommodating both deterministic and bounded uncertain systems without the need for a set-membership framework.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274930","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":"Multi-Sensor Marginalized Particle Filtering for Dynamic Source Estimation","authors":"Nicola Forti;Giorgio Battistelli;Luigi Chisci","doi":"10.1109/LCSYS.2024.3455248","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3455248","url":null,"abstract":"This letter presents a marginalized particle filtering method for localizing, from sparse measurements, a moving source emitting a spatio-temporal field governed by a partial differential equation (PDE). We explicitly consider the full space-time dynamics of the field using a finite-element (FE) approximation for the spatial discretization of the governing PDE system. We propose a marginalized (or Rao-Blackwellized) formulation of the joint field and source estimation problem that leverages the conditionally linear-Gaussian structure of the system with respect to the source position and intensity. This formulation enables the estimation of field variables conditioned on each source position particle using the optimal Kalman filter. We apply this marginalized formulation to both centralized and distributed multi-sensor architectures with remarkable results in terms of monitoring performance and computational efficiency.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666858","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316413","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}
{"title":"Piecewise Affine Relaxation of Discrete Value Functions in Learning Model Predictive Control With Application to Autonomous Racing","authors":"Eunhyek Joa;Changhee Kim;Donghoon Shin;Seunghoon Woo","doi":"10.1109/LCSYS.2024.3455174","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3455174","url":null,"abstract":"Learning Model Predictive Control (LMPC) is a data-driven approach to MPC that enhances closed-loop performance by leveraging data from successive task iterations to approximate the solution of optimal control problems. The value function in LMPC is pivotal for performance enhancement, but its discrete nature-where each point corresponds to a data point-renders the LMPC problem computationally intensive due to its mixed-integer nature. This letter introduces a novel method to construct the LMPC value function. The proposed value function is a piecewise affine approximation that interpolates the discrete data points of the original value function, resulting in a nonlinear relaxation of the mixed-integer LMPC problem. By connecting the discrete data points with piecewise affine segments, the essential characteristics of the original value function are preserved. The proposed algorithm’s effectiveness is demonstrated through numerical simulations in autonomous racing.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274989","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":"Prescribed Performance-Guaranteed Tracking Control: A Switched Positive System Perspective","authors":"Xiyu Gu;Zongyi Guo;Xinming Wang;David Henry;Jérôme Cieslak","doi":"10.1109/LCSYS.2024.3453088","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3453088","url":null,"abstract":"This letter presents a new prescribed performance-guaranteed control (PPC) for a class of linear systems based on positive system theory. Different from standard PPC structure, our attention focuses on the positivity of distance between constrained state and associated performance boundaries. The original constrained system is mapped to a switched positive system involved in distance. This novel framework mitigates the vulnerability/sensitivity of the controller, as it eliminates the necessity for high-gain mechanisms to generate sufficient control input against constraint violations. It develops the performance boundary to a great extent, where various predetermined performance behaviors could be achieved without auxiliary structure. Both theoretical analysis and numerical simulation clarify the effectiveness of the proposed approach.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165100","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}