Automated design exploration and dynamic safety analysis for optimization of mechatronic systems in safety-critical automotive applications. - In: IEEE systems journal, ISSN 1937-9234, Bd. 17 (2023), 4, S. 5357-5368
The availability of safety-critical vehicle systems is essential to ensure passengers' safety in the context of automated driving and X-by-wire systems. The resulting safety-related availability requirements aim to maintain a minimum level of functionality also in the presence of component failures. In addition to electrification and the evolution of cross-domain functionalities, they also increase overall design complexity. Therefore, automated design space exploration, embedding automated safety analysis, becomes crucial for optimal system design. This article proposes a framework for model-based optimization of safety-critical mechatronic systems facing two major challenges as follows. 1) Modeling and exploration of a large design space resulting from topology variants and associated safety measures. 2) Dynamic safety analysis of each variant considering all component failures and their effects on relevant functionalities. Due to the high computational complexity, a two-level modeling approach combining behavioral and logical modules is introduced to reduce the number of evaluation runs when automatically exploring the design space. Further, an algebraic approach is proposed to calculate relevant safety metrics with high accuracy and comparatively low calculation time. The framework is exemplified by optimizing an electric powertrain over a nested design space considering acquisition and operation cost as well as different safety-related availability requirements.
https://doi.org/10.1109/JSYST.2023.3324850
Robust adaptive tracking control of a 3D vertical motion system for nanometer precision applications. - In: IFAC-PapersOnLine, ISSN 2405-8963, Bd. 56 (2023), 2, S. 5332-5339
In this paper we address the modeling and tracking control problem of an overactuated lift and tilt vertical nanopositioning system. We derive a model based on the rigid body dynamics which adequately describes the first resonance mode of each motion axis. This model is validated with measured data in the frequency domain to illustrate that it adequately reflects the real behavior. We further device an abstract model for the derivation of advanced control strategies. By virtue of the single axis model, three SISO controllers are implemented. The control strategy is accomplished comprising a nominal feedforward and LQG-type controller plus an L1 adaptive augmentation with output feedback. The baseline (or nominal) controller features sufficiently high bandwidth for the mere stabilization, decoupling, and disturbance rejection, while the L1 adaptive component plays a central role for recovering the nominal closed-loop dynamics in the presence of parametric uncertainties w.r.t. the input gain which are quite difficult to handle in the nominal design. The effectiveness and robustness of the proposed control strategy is verified via real-time experiments featuring subnanometer and nanoradian tracking errors which seem to be fully-dominated by the measurement noise.
https://doi.org/10.1016/j.ifacol.2023.10.177
Modulating functions approach for non-asymptotic state estimation of nonlinear PDEs. - In: IFAC-PapersOnLine, ISSN 2405-8963, Bd. 56 (2023), 2, S. 9875-9880
In this paper, a non-asymptotic state estimation method for a class of nonlinear partial differential equations with general boundary conditions is presented. The method combines the modulating functions method and the Newton iterative approach. The modulating functions method is instructive to simplify the state estimation problem into solving nonlinear systems of equations, to be accomplished by Netwon's method. The performance of the proposed approach is tested through a numerical example.
https://doi.org/10.1016/j.ifacol.2023.10.410
Advanced soft-sensor systems for process monitoring, control, optimisation, and fault diagnosis. - In: IFAC-PapersOnLine, ISSN 2405-8963, Bd. 56 (2023), 2, S. 11768-11777
As processes become more complex and the need to measure each and every variable becomes more critical, the ability of physical sensors to always provide the sufficient accuracy and sampling time can be difficult. For many complex systems, such as nonideal mixtures, multiphase fluids, and solid-based systems, it may not be possible to even use a physical sensor to measure the key variables. For example, in a multiphase fluid, the concentration or density may only be able to be accurately estimated using a laboratory procedure that can only produce a limited number of samples. Similarly, the quality variables of steel may only be determinable once the final steel product has been produced, which limits the ability to effectively control the process with small time delays. In such cases, recourse has to be made to soft sensors, or mathematical models of the system that can be used to forecast the difficult-to-measure variables and allow for real-time process monitoring, control, and optimisation. Although the development of the soft-sensor model is well-established, the various applications and use cases have not been often considered and the key challenges examined. It can be seen that soft sensors have been applied to a wide range of processes from simple, chemical engineering systems to complex mining processes. In all cases, major improvements in the process operations have been observed. However, key challenges remain in updating the soft-sensor models over time, combining laboratory measurements, especially when they are infrequent or of uncertain quality, and the development of soft sensors for new conditions or processes.
https://doi.org/10.1016/j.ifacol.2023.10.565
Developing a computer programme for data quality assessment. - In: IFAC-PapersOnLine, ISSN 2405-8963, Bd. 56 (2023), 2, S. 11784-11789
With the increase in the available data, it becomes increasingly important to develop automatic methods that can extract valuable nuggets of information from the dregs of uninformative and useless information for use in system identification. This paper presents an overview and summary of this field's current state of the art. A MATLAB programme is presented that can implement data quality assessment. A brief tutorial is presented using industrial kerosene freeze-point data to partition the data set into good and bad regions for system identification. A model is developed using the partitioned data. It is shown that the resulting four models can accurately predict the kerosene freeze point in their respective regions and across the data set.
https://doi.org/10.1016/j.ifacol.2023.10.568
Concurrent monitoring and isolation of static deviations and dynamic anomalies with a sparsity constraint. - In: IFAC-PapersOnLine, ISSN 2405-8963, Bd. 56 (2023), 2, S. 11790-11795
In modern process industries, elaborate monitoring and isolation of various disturbances and faults are needed for reliable and efficient system operation. The classic process-monitoring and fault-diagnosis methods can grasp the correlation between variables, and thus, only take care of abnormal situations caused by the corruption of the correlation relationship. However, dynamics anomalies are even more noteworthy as they reflect more internal details of the system dynamic behaviour under specific situations, and more importantly, can cause severe failures and spread to a broader range of areas while evolving over time. In this paper, a monitoring-and-isolation strategy is proposed to concurrently detect and isolate faults of static deviations and dynamic anomalies. The natural sparsity of the faulty variables is used to overcome the limitations of unknown fault directions and insufficient erroneous measurements, thereby translating the isolation problem into a quadratic programming problem with a sparsity constraint and solved by the least absolute shrinkage and selection operator (LASSO). The case study shows the advantages of the proposed method in monitoring and isolating static deviations and dynamic anomalies.
https://doi.org/10.1016/j.ifacol.2023.10.570
Ruling the operational boundaries: a survey on operational design domains of autonomous driving systems. - In: IFAC-PapersOnLine, ISSN 2405-8963, Bd. 56 (2023), 2, S. 2202-2213
Automated driving systems (ADS) have the potential to offer a safe and efficient future for mobility. At the beginning of the design process of an ADS, the operational limits have to be defined using the operational design domain (ODD). Nonetheless, the field of ODD has only become popular recently, and the necessary regulations, standards, and development approaches are still emerging. Current research contributions in the domain of ODD have fragmented in recent years and have not followed a concrete direction. This survey examines a large proportion of the recent research in the domain of ODD by systematically identifying subject areas and categorising relevant publications, thereby integrating the approaches and showing an overview of the emerging topic area. Furthermore, it identifies existing gaps in the ODD research that need to be considered. Finally, the paper suggest relevant future development of ODD.
https://doi.org/10.1016/j.ifacol.2023.10.1128
A learning-based Nonlinear Model Predictive Control approach for autonomous driving. - In: IFAC-PapersOnLine, ISSN 2405-8963, Bd. 56 (2023), 2, S. 2792-2797
This paper introduces a learning-based Nonlinear Model Predictive Control (NMPC) method that combines NMPC with a Reinforcement Learning (RL) algorithm to achieve automatic parameter tuning of the NMPC optimizer, resulting in better control performance. In this study, two learning-based models were designed based on the tabular Q-learning algorithm but with different definitions of state and action spaces. To test the effectiveness of the proposed model, we conducted two kinds of experiments in which the models were applied to optimize the lane-keeping performance of an autonomous driving system. The case study results from simulations showed that the agent could match a proper parameter matrix for the NMPC within one minute. In real-world experiments, we extended the proposed control scheme to practical driving tasks using a 1:8 scale Audi model car in a specific experimental field. The model exhibited acceptable robustness in the face of relatively large deviations from the sensors and other real-time interference. These results demonstrate that the proposed learning-based NMPC method is a promising direction for solving real-time control problems.
https://doi.org/10.1016/j.ifacol.2023.10.1388
A reduced-order model-based design of event-triggered sliding-mode control. - In: Sliding-mode control and variable-structure systems, (2023), S. 417-435
Event-triggered sliding-mode control (SMC) is an effective tool for stabilizing networked systems under external perturbations. In this chapter, a reduced-order model-based event-triggered controller is presented, unlike the case in the traditional full-order-based design. Besides its inherent advantage of reduced computations, this technique also offers many benefits to the network-based implementation. Particularly in the event-triggering scenario, the use of a reduced-order state vector shows an increase in the sampling interval (also called the inter-event time), leading to a sparse sampling sequence. This is the primary goal of almost all event-triggered controllers. The second outcome of this design is the transmission of a reduced-order vector over the network. Consequently, the transmission cost associated with the controller implementation can be reduced. This chapter exploits the aggregation technique to obtain a reduced-order model for the plant. The design of SMC and the event condition are carried out using this reduced-order model. The analysis of the closed-loop system is discussed using the reduced-order model without transforming it into a regular form. At the end, a practical example is considered to illustrate the benefit of the proposed technique.
https://doi.org/10.1007/978-3-031-37089-2_16
Differential privacy for protecting private patterns in data streams. - In: 2023 IEEE 39th International Conference on Data Engineering workshops, (2023), S. 118-124
Complex event processing (CEP) is a powerful and increasingly more important tool to analyse data streams for Internet of Things (IoT) applications. These data streams often contain private information that requires proper protection. However, privacy protection in CEP systems is still in its infancy, and most existing privacy-preserving mechanisms (PPMs) are adopted from those designed for data streams. Such approaches undermine the quality of the entire data stream and limit the performance of IoT applications. In this paper, we attempt to break the limitation and establish a new foundation for PPMs of CEP by proposing a novel pattern-level differential privacy (DP) guarantee. We introduce two PPMs that guarantee pattern-level DP. They operate only on data that correlate with private patterns rather than on the entire data stream, leading to higher data quality. One of the PPMs provides adaptive privacy protection and brings more granularity and generalization. We evaluate the performance of the proposed PPMs with two experiments on a real-world dataset and on a synthetic dataset. The results of the experiments indicate that our proposed privacy guarantee and its PPMs can deliver better data quality under equally strong privacy guarantees, compared to multiple well-known PPMs designed for data streams.
https://doi.org/10.1109/ICDEW58674.2023.00025