This paper proposes a low-complexity State-of-Charge (SoC) estimation strategy for lithium-ion batteries under aging. The approach reformulates a second-order equivalent circuit model into a higher-order linear time-varying structure, enabling the use of a classical Kalman filter with linear output (KFLO). This avoids both the linearization errors of the EKF and the computational overhead of the UKF. A hybrid identification scheme updates internal resistance online via event-driven current variations while recursively correcting cycle efficiency. Experimental validation on progressively aged cells shows that the estimator maintains robust accuracy, achieving RMSE below 2.5% and MAE below 1.3% even for aged batteries, outperforming EKF and UKF benchmarks. Results demonstrate the method’s suitability for resource-constrained BMS applications requiring long-term reliability.

This paper considers the general problem of the design of boundary controllers for distributed parameter systems. The control objectives are the asymptotic stability of the closed-loop system, as well as the disturbance attenuation of exogenous disturbances affecting the measurements and boundary conditions. More specifically, this work studies input-to-state stability (ISS) and stabilization for systems governed by coupled linear ordinary differential equations (ODEs) and homogeneous linear hyperbolic partial differential equations (PDEs), where actuation, sensing, and disturbance inputs are located at the system boundaries. First, we extend a previously established Lyapunov-based ISS condition for a similar class of systems to the H_1 setting under mild assumptions on the disturbance inputs. This extension not only ensures that the H_1-norm of the system states driven by non-vanishing disturbances and compatible initial conditions are bounded, but also provides an upper bound on the system H_1-norm. Subsequently, these theoretical results are applied to derive stability analysis and control design conditions expressed in terms of linear matrix inequalities. Numerical examples are provided to illustrate the effectiveness and potential of the proposed approach.

This paper proposes a novel robust nonlinear observer (RNO) with learning capacity (LC) for state of charge estimation in lithium-ion batteries. The observer is designed via a convex optimization formulation that guarantees the input-to-state stability of the estimation error dynamics under exogenous disturbances. An auxiliary correction term, generated by a machine learning scheme, is incorporated to enhance the estimation performance. The learning mechanism employs a feedforward neural network that processes delayed measurements; however, the proposed structure can accommodate more complex learning mechanisms provided that the correction signal remains magnitude-bounded. The proposed scheme is validated through comprehensive simulations and practical experiments, with its performance benchmarked against well-established observers. Both numerical and experimental results demonstrate that the proposed observer outperforms the RNO without LC as well as other well-known observers.

This paper focuses on an islanded wind turbine-battery microgrid that supplies an electrolyzer load for offshore hydrogen production. Small-signal stability is assessed while accounting for operating point changes due to wind variability as well as battery aging. To this end, a polynomial parameter varying descriptor model of the microgrid is determined and it is validated by comparison with a PSCAD microgrid simulation. Robust stability of the descriptor model is analyzed by checking sufficient linear matrix inequality conditions accounting for the time variation of the parameters. The benefit of the approach is discussed with regard to the classical root locus method. This stability analysis also results in the determination of the minimal battery rated power ensuring robust stability.

The use of lithium-ion batteries requires careful monitoring, especially regarding the estimation of key internal parameters. Among these, the state of charge (SoC) is particularly important since it cannot be measured directly, requiring the development of reliable estimators. Model-based approaches are widely used for this purpose, equivalent circuit models (ECMs) being especially popular due to their ability to intuitively represent dynamics using electrical analogs. Despite their low complexity, ECMs typically exhibit nonlinear output tions that must be properly handled to ensure accurate estimation. In this context, the present work proposes SoC estimator based on a second-order ECM with a linearized output. The proposed formulation is derived an immersion-based transformation that increases the system order, yielding in a model with a linear and state-affine dynamics containing a current-dependent parameter. The main difference in this approach that the basis transformation exactly converts the originally non-linear system into a linear parameter varying (LPV) model. This allows the use of low-complexity estimators for estimating the SoC, such as the classic Kalman filter in this work. An observability analysis is conducted to guarantee estimation convergence, and a Kalman Filter (KF) is designed for SoC estimation. Results using real data obtained in an electromobility use case demonstrate that the proposed approach achieves a root mean square error (RMSE) below 1.2% under different conditions.

This work presents the design of a boundary state observer for one-dimensional heterodirectional semilinear hyperbolic partial differential equations. The observer equations are formulated using two correction signals derived from boundary measurements, with one being applied to the source term and another one at the boundaries. To ensure the stability and convergence of the estimation error, sufficient design conditions are derived using Lyapunov theory. These conditions guarantee the exponential stability of the estimation error in the & Laplacetrf;2 sense, and they are expressed in the form of linear matrix inequalities providing a computational framework for the observer design. A numerical example is presented to validate the proposed methodology.

This paper investigates an intrusion-tolerant control problem for human-in-the-loop multi-agent systems (HILMASs) subjected to external disturbances and unconstrained actuator and sensor false data injection attacks (FDIAs) under directed graph. It is critical to emphasize that once a hacker gets into the control loop of the HILMAS, he can do whatever damage he wants, which means that the attack signal should be free of any constraints. Two main unconstrained attacks, i.e., unbounded FDIAs and variable-frequency FDIAs, are hard to accurately estimate and defend against since the widely adopted constraints on the existing FDIAs such as the bounded or/and the bounded first-order derivative have been removed. To tackle this challenging obstacle, a novel unknown-input-proportional-differential observer (UIPDO) is developed to not only reconstruct the follower agents' states as well as unconstrained actuator and sensor FDIAs simultaneously, but also avoid the decrease of estimation accuracy caused by measurement deviation. It should be noted that this measurement deviation may be extremely large as it is caused by unconstrained sensor FDIAs, which renders the traditional observer ineffective in providing reliable and accurate estimates of the system states and unconstrained actuator and sensor FDIAs. Then, a novel UIPDO-based intrusion-tolerant control strategy without requiring boundedness of the first-order derivatives of the FDIAs as in existing literature is proposed. Furthermore, an adaptive Zeno-free event-triggered mechanism (ETM) solely relying local state information is developed to reduce the communication burden. Finally, the numerical simulation is provided to verify the merits and effectiveness of the developed methodology. Note to Practitioners-In HILMASs, security is of paramount importance. However, these systems face increasing vulnerability to external disturbances and unconstrained FDIAs under directed graph. Unconstrained attacks, such as unbounded and variable-frequency FDIAs, pose significant challenges due to their lack of traditional signal constraints, complicating their estimation and defense. This paper introduces a novel unknown-input-proportional-differential observer (UIPDO) based on an augmented descriptor system to reconstruct agent states and estimate these attacks, maintaining accuracy even with large measurement deviations. Furthermore, we propose an UIPDO-based intrusion-tolerant control strategy that does not assume boundedness of FDIA first-order derivatives, allowing tolerance of rapid and significant attack changes. Additionally, an adaptive Zeno-free ETM using local real-time state information optimizes communication resources. Our research provides a robust solution to enhance the security, resilience, and practicality of HILMASs against unconstrained FDIAs and external disturbances.

In this paper, the problem of pre-specified performance fault-tolerant cluster consensus control and fault direction identification is solved for the human-in-the-loop (HIL) swarm unmanned aerial vehicles (UAVs) in the presence of possible non-identical and unknown direction faults (NUDFs) in the yaw chan-nel. The control strategy begins with the design of a pre-specified performance event-triggered observer for each individual UAV. These observers estimate the outputs of the human controlled UAVs, and simultaneously achieve the distributed design of actual control signals as well as cluster consensus of the observer output. It is worth mentioning that these observers require neither the high-order derivatives of the human controlled UAVs' output nor a priori knowledge of the initial conditions. The fault-tolerant controller realizes the pre-specified performance output regulation through error transformation and the Nussbaum function. It should be pointed out that there are no chattering caused by the jump of the Nussbaum function when a reverse fault occurs. In addition, to provide a basis for further solving the problem of physical malfunctions, a fault direction identification algorithm is proposed to accurately identify whether a reverse fault has occurred. Simulation results verify the effectiveness of the proposed control and fault direction identification strategies when the reverse faults occur.

This paper is concerned with the switched observer design for a class of systems subject to locally Lipschitz non-linearities. By performing a suitable description of the estimation error dynamics into a linear parameter varying (LPV) system representation, sufficient conditions for the existence of a switching output injection gain are proposed such that the asymptotic stability of the estimation error is guaranteed. These conditions can be conveniently expressed by means of linear matrix inequalities (LMIs), which are easily computationally tractable. A numerical example is provided to show the favorable performance achieved by the proposed observer, which can be applied to a large class of non-linear systems.

This paper addresses the problem of input-to-state stability (ISS) and stabilization of linear ordinary differential equations (ODEs) coupled with a system of homogeneous linear hyperbolic partial differential equations (PDEs) through the boundaries. First, a Lyapunov result characterizing the ISS property for finite-dimensional systems is extended to deal with coupled ODE and PDE systems. The proposed ISS condition is then applied to derive stability and stabilization conditions in terms of linear matrix inequality constraints assuming magnitude bounded disturbances at the boundaries. Two convex optimization problems are also proposed in order to obtain either an optimized reachable set estimate or a boundary controller that minimizes the disturbance effects on the L2xRnorm of the system states. Numerical examples illustrate the potential of the proposed approach.

This paper investigates a preset-time and -accuracy human-in-the-loop cluster consensus control approach for nonlinear multi-agent systems (MASs) on a directed graph under stochastic actuation attacks. First, a new preset-time and -accuracy Zeno-free event-triggered observer that requires neither each agent to access the human agent information nor the first $(n-1)$ derivatives of the human agent output is designed by using a preset-time and -accuracy convergence performance function. Based on this observer, a preset-time and -accuracy intrusion-tolerant controller is put forward for every follower agent, wherein the preset-time convergence and steady-state cluster consensus accuracy are assigned a priori . It is worth mentioning that the control strategy is designed in the form of a transformed error, which not only avoids the infinite gain at the preset-time instant, but also operates continuously even beyond the preset-time interval. Meanwhile, the preset time and accuracy are not affected by the initial conditions, so that the performance function can be preset arbitrarily. To confirm the effectiveness and advantages of the theoretical results, the proposed strategy for cluster consensus control with preset-time and -accuracy is applied to a single link manipulator system.

This paper studies cooperative control problem for multiple Euler-Lagrange systems with unknown harmonic disturbances, where the disturbances are the superposition of sinusoidal components with unknown amplitude, phase, and frequency. A novel adaptive disturbance observer without requiring the disturbance frequency information is designed to estimate the disturbances. Over the directed network topology, a cooperative control scheme is proposed based on backstepping techniques, so that the tracking error is ultimately uniformly bounded. The scheme developed can be used to reject the harmonic disturbances in a uniform way, which means the characteristics of the disturbances can be described as a fixed parameter vector. Finally, an application is presented to demonstrate the effectiveness of the proposed control strategy in the context of a two-link manipulator system.

The nonlinear state estimation for a series/parallel arrangement of lithium-ion (Li-ion) battery cells is addressed in this article assuming limited information. First, a unified model for series/parallel arrangements in terms of a nonlinear descriptor model is proposed in order to account for Kirchhoff’s laws characterizing the interconnection. It relies on a simplified electrochemical model of the individual cells, the so-called equivalent-hydraulic model. Then, assuming that the system and output equation nonlinearities locally satisfy Lipschitz-like constraints, a linear matrix inequality (LMI)-based technique is proposed for designing a nonlinear state observer, which estimates the state of each individual cell (such as the state-of-charge and inner temperature) as well as the algebraic variables (currents or voltages for respectively parallel or series configurations). The proposed design conditions also provide an estimate of the region of guaranteed convergence while ensuring a peak-to-peak performance with respect to exogenous inputs and model uncertainties. The effectiveness of the approach is demonstrated on a detailed battery electrochemical simulator (based on the Doyle–Fuller–Newman model) for a series arrangement of two cells. Comparisons with a standard extended Kalman filter demonstrate the superior performance of the proposed approach.

This paper proposes a control strategy consisting of a robust controller and an Echo State Network (ESN) based control law for stabilizing a class of uncertain nonlinear discrete-time systems subject to persistent disturbances. Firstly, the robust controller is designed to ensure that the closed-loop system is Input-to-State Stable (ISS) with a guaranteed stability region regardless of the ESN control action and exogenous disturbances. Then, the ESN-based controller is trained in order to mitigate the effects of disturbances on the system output. A numerical example demonstrates the potential of the proposed control design method.

B A. Baddabedalah M. Baglietto E. W. Bai G. J. Balas M. J. Balas F. Balduzzi J. Ball B. Bamieh A. Barabanov N. Barabanov J. Barbot B. Barmish G. Barret Y. Bar-Shalom P. Bartlett R. R. Barton G. K. Basak A. E. Bashirov S. Battilotti P. Bauer D. Bayard N. G. Bean C. Beck O. Beldiman G. Belforte A. Bemporad A. Bentsman J. Bentsman A. Benzaouia P. Bernhard D. Bernstein J. Bernussou S. Bhat S. Bhatnagar S. Bhattacharyya A. Bicchi J. Birge M. Bisiacco M. Blachuta F. Blanchini A. Bloch V. Blondel P. Bolzern D. Bonvin V. Borkar F. Borrelli N. K. Bose E.-K. Boukas O. J. Boxma R. Braatz M. S. Branicky M. Brdys T. Brinsmead P. M. T. Broersen B. Brogliato A. Budhiraja F. Bullo I. Burkov T. Burton

The estimation of the internal state in each individual cell of a battery pack made of an arrangement of cells in series or parallel is considered. A unified modelling framework resorting to a descriptor model in order to account for Kirchhoff’s laws characterizing the interconnection is proposed. Next, an LMI-based method is presented to design a robust state observer providing state estimates of each individual cell, notably including state-of-charge and inner temperature, as well as estimates of the unmeasured currents (voltages) for parallel (series) configurations. The effectiveness of the approach is demonstrated in simulation on a parallel arrangement of two cells.

The Multi-View Stereo (MVS) is a key process in the photogrammetry workflow. It is responsible for taking the camera's views and finding the maximum number of matches between the images yielding a dense point cloud of the observed scene. Since this process is based on the matching between images it greatly depends on the ability of features matching throughout different images. To improve the matching performance several researchers have proposed the use of Convolutional Neural Networks (CNNs) to solve the MVS problem. Despite the progress in the MVS problem with the usage of CNNs, the Video RAM (VRAM) consumption within these approaches is usually far greater than classical methods, that rely more on RAM, which is cheaper to expand than VRAM. This work then follows the progress made in CasMVSNet in the reduction of GPU memory usage, and further study the changes in the feature extraction process. The Average Group-wise Correlation is used in the cost volume generation, to reduce the number of channels in the cost volume, yielding a reduction in GPU memory usage without noticeable penalties in the result. The deformable convolutions are applied in the feature extraction network to augment the spatial sampling locations with learning offsets, without additional supervision, to further improve the network's ability to model transformations. The impact of these changes is measured using quantitative and qualitative tests using the DTU and the Tanks and Temples datasets. The modifications reduced the GPU memory usage by 32% and improved the completeness by 9% with a penalty of 6.6% in accuracy on the DTU dataset.

This paper deals with the observer design problem for a class of one-dimensional multi-species transport-reaction systems satisfying sector bounded nonlinearities and considering measurements distributed over the spatial domain. A design method is proposed based on a reduced-order model and a Lyapunov function, which provides sufficient conditions in terms of standard linear matrix inequalities (LMIs) to ensure the exponential convergence of the estimation error with a prescribed decay rate. The observer performance is further improved through an offline optimal sensor placement algorithm considering a parameterized reduced-order output matrix. A nonisothermal tubular reactor is presented to demonstrate the observer performance as well as the advantages of the proposed sensor placement optimization scheme.

The real-time prediction and estimation of the spread of diseases, such as COVID-19 is of paramount importance as evidenced by the recent pandemic. This work is concerned with the distributed parameter estimation of the time-space propagation of such diseases using a diffusion-reaction epidemiological model of the susceptible-exposed-infected-recovered (SEIR) type. State estimation is based on continuous measurements of the number of infections and deaths per unit of time and of the host spatial domain. The observer design method is based on positive definite matrices to parameterize a class of Lyapunov functionals, in order to stabilize the estimation error dynamics. Thus, the stability conditions can be expressed as a set of matrix inequality constraints which can be solved numerically using sum of squares (SOS) and standard semi-definite programming (SDP) tools. The observer performance is analyzed based on a simplified case study corresponding to the situation in France in March 2020 and shows promising results.