Self-adaptative multi-kernel algorithm for switched linear systems identification

Sellami, Lamaa; Zidi, Salah; Abderrahim, Kamel

doi:10.1504/ijmic.2019.096792

Cited by 6 publications

(7 citation statements)

References 10 publications

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“…Estimation switching time “off line”: In this part, we applied the approach proposed in Sellami and Abderrahim 47,48 to estimate the discrete dynamics of the system. Figure 7 describes the evolution of the discrete state and the table below summarizes the detected switching instants (Table 3).…”

Section: Simulation Resultsmentioning

confidence: 99%

“… J j ( k ) can be chosen by various methods, since there is not a specific model which models the dynamics of the discrete state of the linear system. In Sellami and Abderrahim 47,48 J j ( k ) is the function of the square error between the output of the system y ( k ) and the first component of j eme center y j * …”

Section: Switching Time Estimation “Off-line”mentioning

confidence: 99%

“…In order to avoid this ambiguity and to apply the FTC correctly, it is necessary to develop an estimation approach to detect a mode switching and recognize the current mode in a switched system. In this section, the approach proposed in Sellami and Abderrahim, 47,48 is extended for mode detection because it showed quite encouraging. It used a data clustering approach combined with an online detection method of mode change.…”

Section: Switching Time Estimation ''Off-line''mentioning

confidence: 99%

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Fault-tolerant control and sinusoidal disturbance attenuation for a class of hybrid dynamic systems

Yahia

Bedoui

Abderrahim

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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The design of an accurate fault-tolerant control fault-tolerant control is still an active researches of switched hybrid system whose switching mechanism is unknown. In this paper, a new approach is proposed to deal with switching mode estimation and control problems for switched system subject to actuator faults and disturbances. First, the switching mode is estimated off-line using the Chiu clustering algorithm. Then, actuator fault estimation is proposed using data-based projection approach based on performance measures that allow fault estimation without relying on mathematical models and fault assumptions. Second, a novel switching control strategy is developed for actuator fault compensation and recovering the desired performances. These strategy integrate a set of controllers, corresponding to a set of partial modes to design a bank of switching control laws, compensating for the actuator fault effect and attenuating the sinusoidal disturbance. A new linear matrix inequality is proposed to compute control gains based on Lyapunov stability. This approach allows finding the optimal control gains matrices and ensure both actuator fault compensation and disturbance attenuation. Simulation results of two numerical examples shows the effectiveness of the proposed estimation and control strategies in spite of the presence of actuator faults and sinusoidal disturbance.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Switching Time Estimation “Off-line”mentioning

confidence: 99%

Section: Switching Time Estimation ''Off-line''mentioning

confidence: 99%

See 1 more Smart Citation

Fault-tolerant control and sinusoidal disturbance attenuation for a class of hybrid dynamic systems

Yahia

Bedoui

Abderrahim

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Promising and effective outcomes in solving complicated challenges, for instance, automatic system diagnostics and identification [ 22 ], fault detection in wireless system [ 23 , 24 ], cyber threat detection [ 25 ], and specific security problems in other fields have been generated using ML tools throughout the past decade [ 26 , 27 ]. To detect intrusions, ML approaches can be highly effective.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection in Vehicle Controller Area Network (CAN) Bus Using Machine Learning: A Comparative Performance Study

Bari

Yelamarthi

Ghafoor

2023

Sensors

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Electronic Control Units (ECUs) have been increasingly used in modern vehicles to control the operations of the vehicle, improve driving comfort, and safety. For the operation of the vehicle, these ECUs communicate using a Controller Area Network (CAN) protocol that has many security vulnerabilities. According to the report of Upstream 2022, more than 900 automotive cybersecurity incidents were reported in 2021 only. In addition to developing a more secure CAN protocol, intrusion detection can provide a path to mitigate cyberattacks on the vehicle. This paper proposes a machine learning-based intrusion detection system (IDS) using a Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN) and investigates the effectiveness of the IDS using multiple real-world datasets. The novelty of our developed IDS is that it has been trained and tested on multiple vehicular datasets (Kia Soul and a Chevrolet Spark) to detect and classify intrusion. Our IDS has achieved accuracy up to 99.9% with a high true positive and a low false negative rate. Finally, the comparison of our performance evaluation outcomes demonstrates that the proposed IDS outperforms the existing works in terms of its liability and efficiency to detect cyber-attacks with a minimal error rate.

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“…Over the last decade, Artificial Intelligence (AI) tools have produced interesting and effective results when solving complex problems that resemble ours, such as automatic system diagnostics and identification [11], fault detection in wireless sensor networks [12], [13], [14], [15], [16], and certain security problems in other fields. Thus, ML techniques, as the most interesting approach in the field of AI, can be very effective for the detection of intrusions.…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance Evaluation of Intrusion Detection Based on Machine Learning in In-Vehicle Controller Area Network Bus

et al. 2021

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Communication between the nodes in a vehicle is performed using many protocols. The most common of these is known as the Controller Area Network (CAN). The functionality of the CAN protocol is based on sending messages from one node to all others throughout a bus. Messages are sent without either source or destination addresses. Consequently, it is simple for an attacker to inject malicious messages. This may lead to some nodes malfunctioning or to total system failure, which can affect the safety of the driver as well as the vehicle. Detecting intrusions is a challenging problem when using a CAN bus protocol for in-vehicle communication. Most existing work focuses on the physical aspects without taking into consideration the data itself. Machine Learning (ML) tools, especially classification techniques, have been widely used to address similar problems. In this paper, we use and compare several ML techniques to deal with the problem of detecting intrusions in in-vehicle communication. An experimental study is performed using a real dataset extracted from a KIA Soul car. Compared to previous work, which focuses on detecting intrusions based on the physical aspect, in this paper, data analysis and statistical learning techniques are applied. Furthermore, the paper provides a comparative study of the most common ML techniques. The results show that the techniques proposed in this paper outperform other techniques that have been used previously.

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Self-adaptative multi-kernel algorithm for switched linear systems identification

Cited by 6 publications

References 10 publications

Fault-tolerant control and sinusoidal disturbance attenuation for a class of hybrid dynamic systems

Fault-tolerant control and sinusoidal disturbance attenuation for a class of hybrid dynamic systems

Intrusion Detection in Vehicle Controller Area Network (CAN) Bus Using Machine Learning: A Comparative Performance Study

Comparative Performance Evaluation of Intrusion Detection Based on Machine Learning in In-Vehicle Controller Area Network Bus

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