Adaptive poleplacement: the division by zero problem

We re-examine the division by zero problem which occurs in certainty equivalence based indirect adaptive control algorithms applied to linear systems. By exploiting a parametrization for linear systems induced by the continued fraction description of its transfer function, the division by zero problem obtains a very simple geometric representation that can be used to virtually eliminate the problem in the adaptive algorith

Limitations of robust adaptive pole placement control for first order systems

The limitations of pole placement control when there is an adaptive disturbance whose size depends on the previous input and output signals are investigated. It is shown that the control objective of robust stabilization restricts the set of systems that can be dealt with. An adaptive algorithm applicable to systems in this set is proposed and analyzed

Epileptic Seizures and the EEG

A study of epilepsy from an engineering perspective, this volume begins by summarizing the physiology and the fundamental ideas behind the measurement, analysis and modeling of the epileptic brain. It introduces the EEG and provides an explanation of the type of brain activity likely to register in EEG measurements, offering an overview of how these EEG records are and have been analyzed in the past. The book focuses on the problem of seizure detection and surveys the physiologically based dynamic models of brain activity. Finally, it addresses the fundamental question: can seizures be predicted? Based on the authors' extensive research, the book concludes by exploring a range of future possibilities in seizure prediction

The pole zero cancellation problem in adaptive pole-placement revisited

We revisit the pole-zero cancellation problem in adaptive pole-placement. It is well known that an on-line identified model in an adaptive pole-placement algorithm need not be controllable. Since the controller design usually requires that the model is controllable, this results in an ill defined control step. By exploiting the properties of the closed loop unfalsified models set, we derive an algorithm which does not suffer this difficulty The analysis is presented in the case that the system belongs to the model class

Obfuscated memory malware detection in resource-constrained iot devices for smart city applications

Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection. Their multiclass versions consider a few families only and, thereby, fail to detect much existing and emerging malware. Moreover, their large memory size makes them unsuitable to be executed in resource-constrained embedded/IoT devices. To address this problem, in this paper, we propose a multiclass but lightweight malware detection method capable of identifying recent malware and is suitable to execute in embedded devices. For this, the method considers a hybrid model by combining the feature-learning capabilities of convolutional neural networks with the temporal modeling advantage of bidirectional long short-term memory. The proposed architecture exhibits compact size and fast processing speed, making it suitable for deployment in IoT devices that constitute the major components of smart city systems. Extensive experiments with the recent CIC-Malmem-2022 OMM dataset demonstrate that our method outperforms other machine learning-based models proposed in the literature in both detecting OMM and identifying specific attack types. Our proposed method thus offers a robust yet compact model executable in IoT devices for defending against obfuscated malware

Indirect Field Oriented Control of Induction Motors is Robustly Globally Stable

Field orientation, in one of its many forms, is an established control method for high dynamic performance AC drives. In particular, for induction motors, indirect fieldoriented control is a simple and highly reliable scheme which has become the de facto industry standard. In spite of its widespread popularity no rigorous stability proof for this controller was available in the literature. In a recent paper (Ortega et al, 1995) [Ortega, R., D. Taoutaou, R. Rabinovici and J. P. Vilain (1995). On field oriented and passivity-based control of induction motors: downward compatibility. In Proc. IFAC NOLCOS Conf., Tahoe City, CA.] we have shown that, in speed regulation tasks with constant load torque and current-fed machines, indirect field-oriented control is globally asymptotically stable provided the motor rotor resistance is exactly known. It is well known that this parameter is subject to significant changes during the machine operation, hence the question of the robustness of this stability result remained to be established. In this paper we provide some answers to this question. First, we use basic input-output theory to derive sufficient conditions on the motor and controller parameters for global boundedness of all solutions. Then, we give necessary and sufficient conditions for the uniqueness of the equilibrium point of the (nonlinear) closed loop, which interestingly enough allows for a 200% error in the rotor resistance estimate. Finally, we give conditions on the motor and controller parameters, and the speed and rotor flux norm reference values that insure (global or local) asymptotic stability or instability of the equilibrium. This analysis is based on a nonlinear change of coordinates and classical Lyapunov stability theory