The wireless communication used by vehicles in collaborative vehicle platoons is vulnerable to cyber-attacks, which threaten their safe operation. To address this issue we propose a topology-switching coalitional model predictive control (MPC) method based on a reduced order unknown input observer which detects and isolates the cyber-attacks, so that the attacked communication links can be disabled by means of a topology switch. Also, the MPC controller is designed to guarantee robustness against undetected attacks and the increase of uncertainty derived from disabling communication links. The proposed control method also conforms to a relaxed string stability condition and is guaranteed to be safe against crashes.

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Wind energy has emerged as a prominent alternative energy source, harvesting energy through turbines to contribute sustainably to the electricity grid. Effective control of these turbines is crucial for regulating power generation, with wind farm control strategies geared toward maximizing on-demand energy generation. In this work, we propose a wind turbine regulator based on blade-pitch actuation and assess the impact of adopted turbine derating strategies on aerodynamic loading and downstream power availability in an experimental setting. By considering a derating strategy based on generator torque control law, we explore two wind farm control approaches: thrust balance and power compensation. Our findings highlight the advantages of balancing aerodynamic loads across the farm, preventing turbine saturation, and enhancing power availability by 3%-5% compared to a uniform power dispatch. Furthermore, the inclusion of power compensation results in a heightened upper limit in wind farm power tracking, indicating a 22% boost in wind farm power availability. This research underscores the potential benefits of innovative turbine regulation strategies for optimizing wind farm performance and enhancing overall energy flexibility.

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This paper develops a novel approach for online Lithium-ion (Li-ion) battery model identification and state of charge (SOC) estimation. To account for the SOC-dependent battery dynamics and the static nonlinearity between the open-circuit voltage (OCV) and SOC, we formulate a grey box nonlinear state-space model, in which elements depend on SOC in a polynomial way. For model identification, we propose an online concurrent state and parameter estimation by alternating the recursive least squares algorithm and particle filter; the SOC is computed via Coulomb counting during the modeling. The identified grey box model is then applied for SOC estimation using the particle filter. Simulation with real-world battery measurements demonstrates the effectiveness of the model structure and the estimation approach, which is reflected in accurate terminal voltage estimation and nonlinear OCV-SOC relation, and superior performance regarding SOC estimation compared to state-of-the-art approaches.

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This article addresses the computation of stationary and transient solutions of the Fokker-Planck equation for nonlinear stochastic processes. We extend a Galerkin-method, which was previously used to compute stationary solutions for nonlinear mechanical systems, by a generalized formulation of the test function space, including the original approach as a limit. The use of weighted test functions improves the performance in the stationary setting and enables the computation of transient solutions. The properties of the resulting linear system of equations are discussed, and results for stationary and transient probability density functions for nonlinear 1D-, 2D- and 4D-systems are presented.

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Identification of lithium-ion (Li-ion) battery models is essential for enhancing the operation of electrical vehicles. This paper develops a novel approach for estimating the equivalent circuit model (ECM) of Li-ion batteries and reconstructing the open-circuit voltage (OCV) and state of charge (SOC) relationship. We formulate the OCV-SOC relation as a piecewise affine (PWA) function and estimate its coefficients and the Markov parameters (impulse response) of the ECM via l₁-regularized least squares. The state space model of the ECM is derived through the Ho-Kalman algorithm. Experiments with simulated and real-life battery data demonstrate the method's effectiveness and advantages with respect to the state of the art.

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As wind turbine power capacities continue to rise, taller and more flexible tower designs are needed for support. These designs often have the tower's natural frequency in the turbine's operating regime, increasing the risk of resonance excitation and fatigue damage. Advanced load-reducing control methods are needed to enable flexible tower designs that consider the complex dynamics of flexible turbine towers during partial-load operation. This article proposes a novel modulation-demodulation control (MDC) strategy for side-side tower load reduction driven by the varying speed of the turbine. The MDC method demodulates the periodic content at the once-per-revolution (1P) frequency in the tower motion measurements into two orthogonal channels. The proposed scheme extends the conventional tower controller by augmentation of the MDC contribution to the generator torque signal. A linear analysis framework into the multivariable system in the demodulated domain reveals varying degrees of coupling at different rotational speeds and a gain sign flip. As a solution, a decoupling strategy has been developed, which simplifies the controller design process and allows for a straightforward (but highly effective) diagonal linear time-invariant (LTI) controller design. The high-fidelity OpenFAST wind turbine software evaluates the proposed controller scheme, demonstrating effective reduction of the 1P periodic loading and the tower's natural frequency excitation in the side-side tower motion.

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The Flight Control System (FCS) is one of the most important systems in all modern aircraft. For such systems it is required to have robust Fault Detection Isolation and Reconfiguration (FDIR) functionalities with high detection performance. In this work we specifically consider the Oscillatory Failure Cases (OFC), which, if not mitigated, can cause additional structural loads for which the aircraft is not designed. A Sliding Mode Observer (SMO) based detection method is proposed for fast and consistent detection of these OFC faults. A benchmark of a generic aircraft FCS equipped with OFC simulation capabilities, as well as the presented solution for detection, have previously been presented within a competition at the 2020 IFAC World Congress.

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Wind turbines may experience local weather perturbation, which is not taken into account by the commonly-used wind turbine simulation packages. Without this information, it is extremely challenging to evaluate the controller performance with regard to the effect of the variation of local atmospheric conditions. On the other side, it is too late and costly to wait until field test time. To fill this gap, in this paper, we develop a control-oriented turbine dynamic simulation framework to evaluate the controller performance considering the perturbation of local atmospheric conditions. This goal is achieved by integrating an internal wind turbine (IWT) model in the Weather Research and Forecasting (WRF) simulation tool. The proposed framework is implemented on a 5MW reference wind turbine, where the effects of the local atmospheric conditions are illustrated. The proposed WRF-IWT model are validated by comparing the results with those derived from the Fatigue, Aerodynamics, Structures, and Turbulence (FAST).

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Multiplicative watermarking (MWM) is an active diagnosis technique for the detection of highly sophisticated attacks, but is vulnerable to malicious agents that use eaves-dropped data to identify and then remove or replicate the watermark. In this work, we propose a scheme to protect the parameters of MWM, by proposing a design strategy based on piecewise affine (PWA) hybrid dynamical systems, called hybrid multiplicative watermarking (HMWM). Due to the design decision to make certain states of the HMWM systems unobservable, we show that parameter reconstruction by an eavesdropper is infeasible, from both a computational and a system-theoretic perspective, while not altering the system's closed-loop performance.

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The knowledge of the Effective wind speed (EWS) allows the designing of wind turbine controllers that regulate power production and reduce loads on turbine components. Traditional single-point measurements are known to suffer from high noise and poor correlation with the EWS. As an alternative to overcome these problems, EWS estimators can be designed. The main challenge is the high non-linearity of the wind speed influence on the drive-train dynamics. Therefore, an estimator based on the unscented Kalman filter (UKF) is proposed and compared against an extended Kalman filter (EKF) and the immersion and invariance (I&I) technique. Simulation results are provided and show the superior performances attained by the UKF. Furthermore, the usefulness of the estimated EWS is demonstrated by designing a sliding mode controller (SMC) that can track a desired power reference. In addition, the controller allows operating in sub-optimal conditions, where load reduction is attained at the expense of power maximization. The proposed estimator's and controller's performances are evaluated under wind farm wake conditions via high-fidelity simulations. The findings show that UKF can outperform the EKF and the controller can reduce loads, except under highly waked conditions.

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This work presents a coalitional model predictive controller for collaborative vehicle platoons. The overall system is modeled as a string of locally controlled vehicles that can share data through a wireless communication network. The vehicles can dynamically form disjoint groups that coordinate their actions, i.e., the so-called coalitions. The control goals are keeping a desired reference distance between all vehicles while allowing for occasional switching of the communication topology. Likewise, the presented controller promotes a string-stable evolution of the platoon system. Numerical results are provided to illustrate the proposed approach.

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Interconnections in modern systems make them vulnerable to adversarial attackers both by corrupting communication channels and compromising entire subsystems. The field of secure state estimation (SSE) aims to provide correct state estimation even when an unknown part of the measurement signals is corrupted. In this letter, we propose a solution to a novel generalized SSE problem in which full subsystems can be compromised, corrupting both the actuation and measurement signals. For a full system with p measurements, the proposed sliding mode observer (SMO)-based solution allows for up to p attack channels which can be arbitrarily distributed amongst attacks on actuation and measurement signals. This is a much larger class of attacks than considered in the existing literature. The method is demonstrated on 10 interconnected mass-spring-damper subsystems.

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For reliable and safe battery operations, accurate and robust State of Charge (SOC) and model parameters estimation is vital. However, the nonlinear dependency of the model parameters on battery states makes the problem challenging. We propose a Moving-Horizon Estimation (MHE)-based robust approach for joint state and parameters estimation. Dut to all the time scales involved in the model dynamics, a multi-rate MHE is designed to improve the estimation performance. Moreover, a parallelized structure for the observer is exploited to reduce the computational burden, combining both multi-rate and a reduced-order MHEs. Results show that the battery SOC and parameters can be effectively estimated. The proposed MHE observers are verified on a Simulink-based battery equivalent circuit model.

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Accurate prediction of battery performance under various ageing conditions is necessary for reliable and stable battery operations. Due to complex battery degradation mecha-nisms, estimating the accurate ageing level and ageing-dependent battery dynamics is difficult. This work presents a health-aware battery model that is capable of separating fast dynamics from slowly varying states of degradation and state of charge (SOC). The method is based on a sequence to sequence learning-based encoder-decoder model, where the encoder infers the slowly varying states as the latent space variables in an unsupervised way, and the decoder provides health-aware multi-step ahead prediction conditioned on slowly varying states from the encoder. The proposed approach is verified on a Lithium-ion battery ageing dataset based on real driving profiles of electric vehicles.@en

As renewable energy sources such as wind farms become dominant, new challenges emerge for operating and controlling them. Traditionally, wind farm control aims to dispatch power set-points to individual turbines to maximize energy extraction and, thus, their usage as assets. Yet, grid balance and frequency support are fundamental in presence of high renewable penetration and volatility of energy prices and demand. This requires a paradigm change, moving from power maximization to revenue maximization. In this paper, three active power control strategies pushing this shift of paradigm are investigated, namely: wake-loss compensation, thrust balancing, and load-limiting control. The findings of large eddy simulations of a reference wind farm show that wake-loss compensation indeed improves the power generation on waked wind farms, but at the price of increased structural loads on certain turbines. The addition of a thrust balancing can equalize the stresses of individual turbines and their wear in the long term, while still attaining the required power output at the farm level. Furthermore, load-limiting controllers could potentially aid by allowing maintenance to be scheduled in a single time window, thus reducing operation and maintenance costs.

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Economic model predictive control (EMPC) has received increasing attention in the wind energy community due to its ability to trade-off economic objectives with ease. However, for wind turbine applications, inherent nonlinearities, such as from aerodynamics, pose difficulties in attaining a convex optimal control problem (OCP), by which real-time deployment is not only possible but also a globally optimal solution is guaranteed. A variable transformation can be utilized to obtain a convex OCP, where nominal variables, such as rotational speed, pitch angle, and torque, are exchanged with an alternative set in terms of power and energy. The ensuing convex EMPC (CEMPC) possesses linear dynamics, convex constraints, and concave economic objectives and has been successfully employed to address power control and tower fatigue alleviation. This work focuses on extending the blade loads mitigation aspect of the CEMPC framework by exploiting its individual pitch control (IPC) capabilities, resulting in a novel CEMPC-IPC technique. This extension is made possible by reformulating static blade and rotor moments in terms of individual blade aerodynamic powers and rotational kinetic energy of the drivetrain. The effectiveness of the proposed method is showcased in a mid-fidelity wind turbine simulation environment in various wind cases, in which comparisons with a basic CEMPC without load mitigation capability and a baseline IPC are made.

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The current trend in the evolution of wind turbines is to increase their rotor size in order to capture more power. This leads to taller, slender and more flexible towers, which thus experience higher dynamical loads due to the turbine rotation and environmental factors. It is hence compelling to deploy advanced control methods that can dynamically counteract such loads, especially at tower positions that are more prone to develop cracks or corrosion damages. Still, to the best of the authors’ knowledge, little to no attention has been paid in the literature to load mitigation at multiple tower locations. Furthermore, there is a need for control schemes that can balance load reduction with optimization of power production. In this paper, we develop an Economic Model Predictive Control (eMPC) framework to address such needs. First, we develop a linear modal model to account for the tower flexural dynamics. Then we incorporate it into an eMPC framework, where the dynamics of the turbine rotation are expressed in energy terms. This allows us to obtain a convex formulation, that is computationally attractive. Our control law is designed to avoid the "turn-pike" behavior and guarantee recursive feasibility. We demonstrate the performance of the proposed controller on a 5MW reference WT model: the results illustrate that the proposed controller is able to reduce the tower loads at multiple locations, without significant effects to the generated power.@en

Wind turbines are prone to structural degradation, particularly in offshore locations. Based on the structural health condition of the tower, power de-rating strategies can be used to reduce structural loads at the cost of power losses. This paper introduces a novel closed-loop switching control architecture to constrain the thrust in individual turbines. By taking inspiration from developments in the field of reference governors, an existing demanded power tracking controller is extended by a thrust tracking controller. The latter is activated only when a user-defined constraint on fore-aft thrust force is exceeded, which can be set based on the actual damage status of the turbine. Having a down-regulation with monotonic aerodynamic load response, a simple linear thrust tracking controller is proposed. Such a scheme can reduce aerodynamic loads while incurring acceptable losses on power production which, in a wind farm setting, can be compensated for by other turbines. Large eddy simulations demonstrate the performance of the proposed scheme on satisfying thrust constraints.@en

Fully Homomorphic Encryption (FHE) has made it possible to perform addition and multiplication operations on encrypted data. Using FHE in control thus has the advantage that control effort for a plant can be calculated remotely without ever decrypting the exchanged information. FHE in its current form is however not practically applicable for real-time control as its computational load is very high compared to traditional encryption methods. In this paper a reformulation of the Gentry FHE scheme is proposed and applied on an FPGA to solve this problem. It is shown that the resulting FHE scheme can be implemented for real-time stabilization of an inverted double pendulum using discrete time control.@en