Detecting sensor faults in automotive systems is vital to ensure the safe operation and reliability of vehicle controllers. The aim is to detect faults reliably and quickly while avoiding false positives. In particular, the road parameters in the form of banking angle and friction coefficient strongly influence the fault estimation. This work presents a framework for the reliable detection of sensor faults in the vehicle's Inertial Measurement Unit (IMU) up to the driving dynamics limit range. The unknown road parameters are estimated in parallel and enable the use of the proposed method under varying environmental conditions. The estimation of the vehicle states, the coefficient of friction and the sensor faults is carried out using a Two-Stage Unscented Kalman Filter with a nonlinear vehicle model. The estimation of the road inclination and banking angle is carried out using a separate Extended Kalman Filter. Measurements with an Audi SQ8 e-tron under high and low friction condition are presented and show a fault detection time of 100 ms while avoiding false-positive detections for quasi steady state friction conditions. Rapid variations in the coefficient of friction lead to a slight degradation in fault estimation performance.
In the control and operation of power inverters, proportional-resonant (PR) controllers are used to track references and reject disturbances that can be described by a sum of sinusoidal signals of known frequency. The relevance of these controllers is increasing due to the increase in distortion and volatility of three-phase signals in the grid. However, as reported in the literature, the tuning of PR controllers is far from trivial due to their large number of parameters and the presence of pure imaginary poles in the associated transfer function. To address these issues, a time-domain framework based on linear matrix inequalities (LMIs) is presented for the tuning of PR controllers with applications to voltage and current tracking. The advantage of this approach is the straightforward combination with other techniques such as H-infinity control. The effectiveness of the approach is illustrated through numerical simulations, where the injection of a constant active power is achieved in the presence of distorted voltages.
This article tackles the problem of designing ratelimiting elements, commonly found in feedback loops and used to filter signals before passing them to actuators. Under certain circumstances, these elements can introduce a phase delay, which can ultimately cause instability. For this reason, techniques to understand, analyse, and predict instability due to their phase loss have been proposed. Techniques to limit the rate of change of a signal without introducing the significant phase loss of a standard rate limiter have also been studied. However, said schemes have one or multiple of the following problems: the introduction of bias, the necessity to know the internal signals of actuators, difficult parameter tuning/interpretation, and the need to solve online optimisation problems. This study provides an alternative rate-limiting element that is competitive with state-of-the-art methods in phase-matching performance but avoids the mentioned drawbacks. The newly introduced phase anticipation scheme has been tested in step, sinusoidal, and mixed regimes. It proved able to recover up to 65 % of the lost phase without introducing any significant downside. To illustrate its performance in a concrete application, we show its effectiveness in avoiding phase loss-induced limit cycles in an aircraft ground handling task in a high-fidelity simulator.
This paper studies the resilient consensus of a multi-agent system in the presence of an external source and misbehaving agents. A dynamic model is considered that integrates an external signal from the source to the weighted-mean-subsequence-reduced (W-MSR) based algorithm. Graphtheoretic conditions for resilient consensus are derived based on a new notion of tier-$k$ conformity, ensuring that the consensus value remains independent of the misbehaving agents and is determined by the external signal. For the special case where all agents receive the external signal, exponential convergence to the consensus value is proven. Numerical examples are included to illustrate and validate the theoretical findings.
This paper presents a partially model-free adaptive optimal tracking control method for power systems, specifically targeting a synchronous generator connected through a reactive transmission line. By integrating the tracking error dynamics with reference trajectory dynamics, an augmented system is created. A discounted performance function is introduced to address the nonlinear tracking problem optimally. Unlike traditional methods that compute feedforward and feedback terms separately, the proposed approach calculates both simultaneously by minimizing the discounted performance function. The discrete-time tracking Bellman and Hamilton-Jacobi-Bellman (HJB) equations are derived, and a reinforcement learning (RL)-based technique is employed to solve the optimal policy online without requiring prior knowledge of system drift dynamics. Finally, the proposed method is validated through a real-time digital simulator (RTDS) with a standard power system representation.
State estimation and control of quantum systems present significant challenges due to the inherent noise, uncertainty, and non-linearity of quantum dynamics. In this paper, we propose the application of Sliding Mode Observers (SMOs) as a robust and efficient solution for the state estimation and control of finite dimensional quantum systems whose state is described by a density matrix rho. We demonstrate how these observers can be used to track quantum states accurately, despite the presence of measurement noise and system dynamics uncertainties. Furthermore, we introduce a control strategy that leverages the state estimates to stabilize the quantum system, ensuring desirable performance even under challenging conditions. Numerical simulations are provided to illustrate the effectiveness of the proposed methodology, showcasing improvements in system stability, accuracy of state estimation, and robustness to perturbations. The results highlight the potential of sliding mode observers as a viable tool for quantum control applications, including quantum information processing, quantum computing, and other emerging quantum technologies.
This work investigates relay-based methods to reveal the frequency domain characteristics of BlueROV2 underwater vehicle. Two relay-based approaches are examined: the Modified Relay Feedback Test (MRFT) and a two-relay (twisting-type) controller. The distinguishing feature of these relay tests is their ability to capture process frequency-response data at a desired phase angle in the Nyquist plot. Approximate equivalence of these tests is also discussed. Influence of additional dynamics on oscillations for these tests is discussed and it is argued that these relay tests have capability to excite both actuator and plant dynamics simultaneously, hence provide a mean to perform full system identification and precise controller tuning. Experiment and simulations of oscillation tests are also presented.
This paper presents a method for parameterizing a linear quadratic (LQ) motion planning algorithm for automated vehicles. The presented method approximates the behavior of a planning algorithm that optimizes for a multi-objective optimization (MOO) objective function. The MOO objective function captures the Pareto-conflicting objectives of mitigating motion sickness and reducing travel time. Both objectives cannot be considered directly in the LQ motion planning algorithm due to the complex formulation of the motion sickness objective and the limited prediction horizon, which prevents the algorithm from considering total travel time explicitly. Nevertheless, we use an LQ approach because it allows for online planning. Our method uses Bayesian optimization to tune the parameters of the LQ objective function so that the resulting state trajectory is optimal with respect to the MOO objective function. Additionally, we employ a normalized weighted-sum method to assign varying importance to the MOO objectives, resulting in a convex Pareto front of the MOO objectives. The presented results demonstrate that the LQ motion planning algorithm is online capable and can be effectively tuned to balance the trade-off between motion sickness and travel time according to passenger susceptibility.
An important function of an Automatic Train Operation (ATO) system is to control braking to bring a train to a standstill at a specific location in a smooth and safe manner. To minimise journey times and to improve throughput on a rail network, the time required for braking needs to be minimised. This paper considers braking as an optimal, minimum time control problem and applies the solution as a feedforward braking traction. A feedback controller is then used to ensure that the train speed follows the optimal solution. The feedback is based on a PID controller to align with the existing structure of the ATO system. By considering the controller as a Lur'e system, the Popov criterion provides a limit on the allowable slope of the braking curve and the optimal profile is modified to ensure that this constraint is satisfied. The performance of the controller is evaluated on an industry supplied simulation of the train dynamics.
High performance demands on accuracy and system throughput are common in many industrial applications, making well-designed control laws and reference trajectories essential. Yet, while system performance depends on the joint effects of feedforward control, feedback control, and the design of the reference, these different elements of the control system are typically designed sequentially rather than simultaneously. Such a sequential design approach potentially leads to a loss in obtainable performance. In this work, we present a novel unified data-driven co-design framework for feedforward, feedback, and/or reference design. This framework aims at minimizing the duration of the transient phase in setpoint control: the time required to execute a point-to-point task and reach a desired level of accuracy. The efficacy of the proposed approach in minimizing this duration is demonstrated in a case study of an industrial wire bonder system, in which reductions of up to 54 % in the duration of the transient phase were obtained compared to the current industrial state-of-practice.
Irrigation channels operating under practical decentralized controllers can exhibit string instabilities in the form of undesirable amplification of flow transients as they propagate spatially. To limit the propagation of such instability, the application of an existing decentralized water-level balancing control scheme along a mid section of multiple pools is considered. The main design trade-offs are illustrated by numerical example.
This paper considers the problem of complexity reduction of large-scale interconnected structural-dynamics models. On the one hand, traditional CMS-based reduction methods for such problems often fail to sufficiently reduce the order of these models. On the other hand, the large-scale nature of these models obstructs direct application of more effective balanced reduction methods. To address this challenge, we propose a synergetic approach that combines several structurepreserving balanced truncation methods with various efficient Gramian approximation techniques. A comparative study of the effectiveness and computational efficiency of the resulting methods is performed by using those to reduce a structuraldynamics model from the lithography industry.
Bariatric surgery, particularly Roux-en-Y Gastric Bypass (RYGB), significantly alters postprandial physiology, including enhanced gastric emptying and increased glucose absorption. These changes can contribute to the development of post-bariatric hypoglycemia (PBH), and its associated a clinically relevant complication. As demonstrated in type 1 diabetes, the development of mathematical models and their use in simulation environments can greatly accelerate the development and testing of novel clinical approaches. This work aims to identify the most appropriate meal absorption model structure from existing models in the literature by incorporating parameters that reflect physiological alterations following RYGB surgery. Glucose rate of appearance (Ra) mean data from 12 subjects both pre- and post-surgery is fitted. The best-fitting model was selected using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The selected model was able to accurately capture the exaggerated postprandial glycemic excursions observed in the data of RYGB subjects (RMSE=0.382 g/kg/min). Such a model can be further integrated into larger glucose metabolism simulators, potentially providing a valuable simulation tool for the study of PBH management.
Maintaining grid balance represents a significant challenge in light of the growing proportion of renewable energy sources in power generation. A viable solution to enhance grid balance is the implementation of Battery Energy Storage Systems (BESS), which can be charged during low-demand periods and discharged during peak load times. This paper investigates the feasibility of deploying a BESS within a district comprising a population of 100,000. The proposed optimization algorithm is formulated as a mixed-integer linear programming problem. While this algorithm applies a linear battery model, the results are compared to those obtained from a second-order equivalent circuit model. The findings indicate that the dynamic operation of the BESS can effectively reduce the standard deviation of the residual load by as much as 3.5 MWh.
Autonomous mobile robots are actively applied to execute complex tasks, such as package delivery, autonomous taxiing, and search-and-rescue. Signal Temporal Logic (STL) offers a powerful formalism for such complex tasks. However, designing plans (trajectories) that satisfy tasks formalized by STL grammar, particularly for nonholonomic systems such as a car-like robot or a fixed-wing aircraft, is a challenging problem. This paper proposes a method to generate trajectories for a multi-robot system with car-like robots to perform complex tasks specified with STL grammar. The proposed method solves a nonlinear program (NLP) to construct trajectories with several constant curvature curves that satisfy the specification. In doing so, it also guarantees the kinematic feasibility of the solution trajectories. Extensive simulation studies show that the proposed method finds satisfying solutions 4x faster than a model predictive control baseline. Additionally, it is able to construct trajectories for complex STL specifications that the baseline fails to satisfy.
This study proposes a general forecast and control framework for constrained stochastic nonlinear optimal control of isolated gas-renewable energy systems with energy storage. Typically, these systems require control strategies that can handle significant uncertainties in produced renewable energy due to forecast uncertainty from meteorological forecasts. To address the uncertainty in meteorological forecasts, data-driven stochastic grey-box models of the renewable energy source are modelled and probabilistically forecast (PF) with stochastic differential equations (SDEs). The PF scheme improves upon the meteorological forecasts by forming a probability distribution in time with the meteorological forecasts and past data as input. Based on these distributions, a multi-stage (MS) nonlinear model predictive control (NMPC) formulation is utilised, resulting in a tractable control formulation. The proposed framework is validated in simulation with real-life data for a hybrid gas-wind energy system, which shows that the proposed method is real-time capable despite using standard solvers and outperforms standard methods, such as certaintyequivalent NMPC when relying on meteorological forecasts. Though motivated by the energy sector, the proposed method can be extended to any stochastic system since SDEs are a general class of stochastic processes.
Battery health diagnostics are critical for enabling aging-aware control strategies and optimizing operational planning in energy storage systems, yet electrode-level state estimation remains challenging due to time-intensive methods and specialized equipment requirements. This study introduces a rapid diagnostic framework that estimates electrode-level states using only a 2-minute transient response. By integrating resistance-capacitance parameters-extracted from the voltage response to a step current-into an ensemble learning model, battery capacity, electrode-level capacities, and lithium inventory are estimated. The framework is validated with experimental data from 36 commercial electric vehicle batteries across all state-of-charge (SOC) levels, achieving an average diagnostic accuracy of 98.3% for cell capacity, 95.5% for negative electrode capacity, 98.5% for positive electrode capacity, and 97.9% for lithium inventory. Moreover, the framework remains effective even when the cell's SOC is uncertain, offering valuable insights for adaptive control and aging-aware battery management.
A variety of control functions are used in modern vehicles to stabilize the vehicle dynamics. These can be improved with precise information of time-varying parameters. The vehicle mass, center of gravity height and the roll moment of inertia are significant for vehicle roll dynamics as they characterize the static and dynamic behavior of the roll motion. These parameters not only affect the driving behavior but can also increase the risk of rollover. Since the roll moment of inertia is particularly significant for transient roll dynamics, the main contribution of this paper is to develop a model-based estimation algorithm to estimate the roll moment of inertia. This paper therefore presents an Unscented Kalman Filter for a simultaneous state and parameter estimation. By using a nonlinear vehicle model which represents the roll, pitch and vertical dynamics, the effects of the center of gravity height and additional masses on the inertia are taken into account. In order to improve the estimation results, an activation condition based on a linear single track model and an underlying observability analysis is presented. Based on that, a precise parameter estimation with a deviation of less than 5 % to the nominal parameter is achieved.
Wave energy is a promising renewable resource, yet traditional wave energy conversion (WEC) systems suffer from limitations due to their stationary deployment. In particular, stationary WECs require several months or even years for permitting and installation, and they cannot be relocated to suit evolving demands after their installation. While these limitations are not necessarily an issue for longterm deployments, they are problematic for applications such as disaster recovery or temporary power supplementation in island communities, which require rapid deployability. This work examines a mobile wave glider system that combines the principles of a rapidly deployable wave glider with an auxiliary power take-off (PTO) system that utilizes a fraction of the available wave power to charge an on-board battery through an active damper. The contribution of this work lies in a real-time power optimization scheme that combines a sea-state-driven, model-based lookup table (based on a customized model developed by the authors) with extremum seeking control (ESC) to learn a correction to the lookup table. This approach is based on the observation that the optimal corrections will exhibit limited, slow variation relative to the variations in the sea state itself. Simulations, driven by wave data from the island nation of Palau and a scaled model of the system, demonstrate the effectiveness of the proposed control strategy in achieving near-optimal energy harvesting.
Pedestrian confidence region prediction is crucial for ensuring safety in autonomous driving, particularly in urban scenarios. Recent work models pedestrian dynamics via the Social Force Model (SFM), providing deterministic trajectory predictions. However, these approaches often fail to account for the inherent uncertainty in pedestrian behavior and biases across datasets. We formulate a Distributionally Robust Optimization (DRO) problem that integrates parameter and distributional uncertainty into the SFM to estimate pedestrian confidence regions at a future time. To solve this, we apply a sample-based approximation that transforms probabilistic constraints into a tractable deterministic form, with uniform convergence guarantees. A case study on pedestrian-vehicle interaction demonstrates improved prediction accuracy and robustness under uncertainty.
