Repetitive control (RC) has shown to achieve superior rejection of periodic disturbances. Many nonlinear systems are subject to repeating disturbances. The aim of this article is to develop a continuous-time RC design with stability guarantees for nonlinear Lur'e-type systems. Approximate output tracking is achieved by combining an internal model, consisting of a finite number of linear oscillators with frequencies at the reference frequency and at its multiples, with a stabilizer that guarantees a convergence property of the closed-loop system. The developed RC approach is applied to a nonlinear mechanical ventilation system for intensive care units (ICUs), which can be modeled as a Lur'e-type system. The experimental study confirms that the RC scheme is able to successfully follow the desired target pressure profile to properly support the ventilation needs of an adult patient. @en

In this article, we propose a tractable nonlinear fault estimation filter along with explicit performance bounds for a class of linear dynamical systems in the presence of both additive and nonlinear multiplicative faults. We consider the case, where both faults may occur simultaneously and through an identical dynamical relationship, a setting that is relevant to several application domains, including automotive driving, aviation, and chemical plants. The proposed filter architecture combines tools from model-based approaches in the control literature and regression techniques from machine learning. To this end, we view the regression operator through a system-theoretic perspective to develop operator bounds that are then utilized to derive performance bounds for the proposed estimation filter. In the case of constant, simultaneously, and identically acting additive and multiplicative faults, it can be shown that the estimation error converges to zero with an exponential rate. The performance of the proposed estimation filter in the presence of incipient faults is validated through an application on the lateral safety systems of SAE level 4 automated vehicles. The numerical results show that the theoretical bounds of this study are indeed close to the actual estimation error.@en

This paper addresses the problem of robust process and sensor fault reconstruction for nonlinear systems. The proposed method augments the system dynamics with an approximated internal linear model of the combined contribution of known nonlinearities and unknown faults - leading to an approximated linear model in the augmented state. We exploit the broad modeling power of ultra-local models to characterize this internal dynamics. We use a linear filter to reconstruct the augmented state (simultaneously estimating the state of the original system and the sum of nonlinearities and faults). Having this combined estimate, we can simply subtract the analytic expression of nonlinearities from that of the corresponding estimate to reconstruct the fault vector. Because the nonlinearity does not play a role in the filter dynamics (it is only used as a static nonlinear output to estimate the fault), we can avoid standard restrictive assumptions like globally (one-sided) Lipschitz nonlinearities and/or the need for Lipschitz constants to carry out the filter design. The filter synthesis is posed as a mixed H2/H∞ optimization problem where the effect of disturbances and model mismatches is minimized in the H∞ sense, for an acceptable H2 performance with respect to measurement noise.@en

Lateral control in the absence of lane markings is an essential safety fallback for an autonomous vehicle in cooperative driving applications. Point following control is one such solutions for lateral control. However, it suffers from corner cutting and severe disturbance amplification throughout the platoon. In this paper, a new model for controller synthesis is proposed which supports including the error induced by the road curvature in the communication between two vehicles. This enables the trailing vehicle to deduce the actual road error states, which negates steady-state corner cutting if these errors are controlled to zero. To demonstrate the benefits of this new control model, an control framework is used to design a lateral controller which minimizes the lateral overshoot of the vehicles during transient maneuvering. The proposed approach has been evaluated using numerical simulations. Simulation results show that the lateral overshoot can be reduced by a factor 10 with respect to existing lateral control solutions. @en

This paper presents a novel approach to (controlled) synchronization of networked nonlinear systems. For classes of identical single-input–single-output nonlinear systems and networks, including oscillator networks, we propose a systematic design procedure (with generic as well as constructive conditions) for specifying nonlinear coupling functions that guarantee global asymptotic synchronization of the systems’ (oscillatory) states. The proposed coupling laws are in the form of a definite integral of a nonlinear “coupling gain” function. It can be fit to the system's nonlinearities and, thus, can avoid cancelling nonlinearities by feedback or high-gain arguments commonly needed for linear (diffusive) coupling laws. As demonstrated by two examples, including a network of FitzHugh–Nagumo oscillators, this design can result in much lower synchronizing coupling gains than for the common case of linear couplings, therewith increasing energy efficiency of the coupling laws and reducing output-noise sensitivity. The resulting coupling structure can be of a varying type, when couplings are activated/deactivated depending on the systems’ outputs without undermining overall synchronization. The approach is based on a novel notion of incremental feedback passivity with a nonlinear gain. In addition to the design contribution, these results provide a new insight into potential synchronization mechanisms in natural and artificial nonlinearly coupled systems.@en

In this paper, we present a methodology for actuator and sensor fault estimation in nonlinear systems. The method consists of augmenting the system dynamics with an approximated ultra-local model (a finite chain of integrators) for the fault vector and constructing a Nonlinear Unknown Input Observer (NUIO) for the augmented dynamics. Then, fault reconstruction is reformulated as a robust state estimation problem in the augmented state (true state plus fault-related state). We provide sufficient conditions that guarantee the existence of the observer and stability of the estimation error dynamics (asymptotic stability of the origin in the absence of faults and ISS guarantees in the faulty case). Then, we cast the synthesis of observer gains as a semidefinite program where we minimize the ℒ 2 -gain from the model mismatch induced by the approximated fault model to the fault estimation error. Finally, simulations are given to illustrate the performance of the proposed methodology.@en

Estimating and detecting faults is crucial in ensuring safe and efficient automated systems. In the presence of disturbances, noise, or varying system dynamics, such estimation is even more challenging. To address this challenge, this letter proposes a novel filter to estimate multiple fault signals for a class of discrete-time linear parameter-varying (LPV) systems. The design of such a filter is formulated as an optimization problem and is solved recursively, while the system dynamics may vary over time. Conditions for the existence and detectability of the fault are introduced and the problem is formulated and solved using the quadratic programming framework. We further propose an approximate scheme that can be arbitrarily precise while it enjoys an analytical solution, which supports real-time implementation. The method is illustrated and validated on an automated vehicle's lateral dynamics, which is a practically relevant example for LPV systems. The results show that the estimation filter can decouple unknown disturbances and known or measurable parameter variations in the dynamics while estimating the unknown fault.@en

Patient-ventilator asynchrony is one of the largest challenges in mechanical ventilation and is associated with prolonged ICU stay and increased mortality. The aim of this paper is to automatically detect and classify the different types of patient-ventilator asynchronies during a patient's breath using the typically available data on commercially available ventilators. This is achieved by a detection and classification framework using an objective definition of asynchrony and a supervised learning approach. The achieved detection performance of the near-real time framework on a clinical dataset is a significant improvement over current clinical practice, therewith and, this framework has the potential to significantly improve the patient comfort and treatment outcomes.@en

Estimation of relevant lung parameters and the breathing effort of a ventilated patient is essential to keep track of the patient's clinical condition. The aim of this paper is to investigate the major challenges of estimating the patient's condition with parametric models. The main method is a linear regression framework, where identifiability and persistence of excitation aspects are clearly unraveled. Different approaches for improving estimation accuracy are outlined. As an illustration, one of the solution strategies is implemented, which leads to accurate estimates of the breathing effort and relevant lung parameters.@en