Output Feedback Controller Design for a Class of MIMO Nonlinear Systems Using High-Order Sliding-Mode Differentiators With Application to a Laboratory 3-D Crane

This paper addresses the problem of output feedback control design for a class of multi-input-multi-output (MIMO) nonlinear systems where the number of inputs is less than that of outputs. There are two difficulties in this design problem: 1) too few control inputs will not generally allow independent control over all outputs and 2) the state of the system is not available for measurements, and only the outputs are available through measurements. To address the first issue, a practical output feedback control problem is formulated, aiming to regulate only part of the outputs, and a controller structure with two design components in all or some chosen control inputs is proposed. To cope with the second difficulty, the recently developed high-order sliding mode differentiators (HOSMDs) are used to estimate the derivatives of the outputs needed in the controller design. With the derivatives estimated using HOSMDs, an output feedback controller is designed using the backstepping approach. Stability results are established for the designed controller under certain conditions. In order to test the applicability of the proposed output feedback controller in practical industrial problems, experiments are carried out though implementing the controller on a laboratory-scale 3-D crane. The experimental results are presented and reveal the advantage of the proposed controller structure, as well as the effect of controller gain and sampling periods

An improved droop-based control strategy for MT-HVDC systems

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This paper presents an improved droop-based control strategy for the active and reactive power-sharing on the large-scale Multi-Terminal High Voltage Direct Current (MT-HVDC) systems. As droop parameters enforce the stability of the DC grid, and allow the MT-HVDC systems to participate in the AC voltage and frequency regulation of the different AC systems interconnected by the DC grids, a communication-free control method to optimally select the droop parameters, consisting of AC voltage-droop, DC voltage-droop, and frequency-droop parameters, is investigated to balance the power in MT-HVDC systems and minimize AC voltage, DC voltage, and frequency deviations. A five-terminal Voltage-Sourced Converter (VSC)-HVDC system is modeled and analyzed in EMTDC/PSCAD and MATLAB software. Different scenarios are investigated to check the performance of the proposed droop-based control strategy. The simulation results show that the proposed droop-based control strategy is capable of sharing the active and reactive power, as well as regulating the AC voltage, DC voltage, and frequency of AC/DC grids in case of sudden changes, without the need for communication infrastructure. The simulation results confirm the robustness and effectiveness of the proposed droop-based control strategy

Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms

The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.Comment: Accepted for publication in edited book titled "Federated and Transfer Learning", Springer, Cha

A bidirectional power charging control strategy for Plug-in Hybrid Electric Vehicles

© 2019 by the authors. Plug-in Hybrid Electric Vehicles (PHEVs) have the potential of providing frequency regulation due to the adjustment of power charging. Based on the stochastic nature of the daily mileage and the arrival and departure time of Electric Vehicles (EVs), a precise bidirectional charging control strategy of plug-in hybrid electric vehicles by considering the State of Charge (SoC) of the batteries and simultaneous voltage and frequency regulation is presented in this paper. The proposed strategy can control the batteries charge which are connected to the grid, and simultaneously regulate the voltage and frequency of the power grid during the charging time based on the available power when different events occur over a 24-h period. The simulation results prove the validity of the proposed control strategy in coordinating plug-in hybrid electric vehicles aggregations and its significant contribution to the peak reduction, as well as power quality improvement. The case study in this paper consists of detailed models of Distributed Energy Resources (DERs), diesel generator and wind farm, a generic aggregation of EVs with various charging profiles, and different loads. The test system is simulated and analyzed in MATLAB/SIMULINK software

An improved mixed AC/DC power flow algorithm in hybrid AC/DC grids with MT-HVDC systems

© 2019 by the authors. One of the major challenges on large-scale Multi-Terminal High Voltage Direct Current (MT-HVDC) systems is the steady-state interaction of the hybrid AC/DC grids to achieve an accurate Power Flow (PF) solution. In PF control of MT-HVDC systems, different operational constraints, such as the voltage range, voltage operating region, Total Transfer Capability (TTC), transmission reliability margin, converter station power rating, etc. should be considered. Moreover, due to the nonlinear behavior of MT-HVDC systems, any changes (contingencies and/or faults) in the operating conditions lead to a significant change in the stability margin of the entire or several areas of the hybrid AC/DC grids. As a result, the system should continue operating within the acceptable limits and deliver power to the non-faulted sections. In order to analyze the steady-state interaction of the large-scale MT-HVDC systems, an improved mixed AC/DC PF algorithm for hybrid AC/DC grids with MT-HVDC systems considering the operational constraints is developed in this paper. To demonstrate the performance of the mixed AC/DC PF algorithm, a five-bus AC grid with a three-bus MT-HVDC system and the modified IEEE 39-bus test system with two four-bus MT-HVDC systems (in two different areas) are simulated in MATLAB software and different cases are investigated. The obtained results show the accuracy, robustness, and effectiveness of the improved mixed AC/DC PF algorithm for operation and planning studies of the hybrid A/DC grids

A new topology of a fast proactive hybrid DC Circuit Breaker for MT-HVDC grids

© 2019 by the authors. One of the major challenges toward the reliable and safe operation of the Multi-Terminal HVDC (MT-HVDC) grids arises from the need for a very fast DC-side protection system to detect, identify, and interrupt the DC faults. Utilizing DC Circuit Breakers (CBs) to isolate the faulty line and using a converter topology to interrupt the DC fault current are the two practical ways to clear the DC fault without causing a large loss of power infeed. This paper presents a new topology of a fast proactive Hybrid DC Circuit Breaker (HDCCB) to isolate the DC faults in MT-HVDC grids in case of fault current interruption, along with lowering the conduction losses and lowering the interruption time. The proposed topology is based on the inverse current injection technique using a diode and a capacitor to enforce the fault current to zero. Also, in case of bidirectional fault current interruption, the diode and capacitor prevent changing their polarities after identifying the direction of fault current, and this can be used to reduce the interruption time accordingly. Different modes of operation of the proposed topology are presented in detail and tested in a simulation-based system. Compared to the conventional DC CB, the proposed topology has increased the breaking current capability, and reduced the interruption time, as well as lowering the on-state switching power losses. To check and verify the performance and efficiency of the proposed topology, a DC-link representing a DC-pole of an MT-HVDC system is simulated and analyzed in the PSCAD/EMTDC environment. The simulation results verify the robustness and effectiveness of the proposed HDCCB in improving the overall performance of MT-HVDC systems and increasing the reliability of the DC grids

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

For over a century, induction furnaces have been used in the core of foundries for metal melting and heating. They provide high melting/heating rates with optimal efficiency. The occurrence of faults not only imposes safety risks but also reduces productivity due to unscheduled shutdowns. The problem of diagnosing faults in induction furnaces has not yet been studied, and this work is the first to propose a data-driven framework for diagnosing faults in this application. This paper presents a deep neural network framework for diagnosing electrical faults by measuring real-time electrical parameters at the supply side. Experimental and sensory measurements are collected from multiple energy analyzer devices installed in the foundry. Next, a semi-supervised learning approach, known as the local outlier factor, has been used to discriminate normal and faulty samples from each other and label the data samples. Then, a deep neural network is trained with the collected labeled samples. The performance of the developed model is compared with several state-of-the-art techniques in terms of various performance metrics. The results demonstrate the superior performance of the selected deep neural network model over other classifiers, with an average F-measure of 0.9187. Due to the black box nature of the constructed neural network, the model predictions are interpreted by Shapley additive explanations and local interpretable model-agnostic explanations. The interpretability analysis reveals that classified faults are closely linked to variations in odd voltage/current harmonics of order 3, 11, 13, and 17, highlighting the critical impact of these parameters on the model’s prediction

A dual approach for positive T–S fuzzy controller design and its application to cancer treatment under immunotherapy and chemotherapy

This study proposes an effective positive control design strategy for cancer treatment by resorting to the combination of immunotherapy and chemotherapy. The treatment objective is to transfer the initial number of tumor cells and immune–competent cells from the malignant region into the region of benign growth where the immune system can inhibit tumor growth. In order to achieve this goal, a new modeling strategy is used that is based on Takagi–Sugeno. A Takagi-Sugeno fuzzy model is derived based on the Stepanova nonlinear model that enables a systematic design of the controller. Then, a positive Parallel Distributed Compensation controller is proposed based on a linear co-positive Lyapunov Function so that the tumor volume and administration of the chemotherapeutic and immunotherapeutic drugs is reduced, while the density of the immune-competent cells is reached to an acceptable level. Thanks to the proposed strategy, the entire control design is formulated as a Linear Programming problem. Finally, the simulation results show the effectiveness of the proposed control approach for the cancer treatment

Generative adversarial network-based scheme for diagnosing faults in cyber-physical power systems

This paper presents a novel diagnostic framework for distributed power systems that is based on using generative adversarial networks for generating artificial knockoffs in the power grid. The proposed framework makes use of the raw data measurements including voltage, frequency, and phase-angle that are collected from each bus in the cyber-physical power systems. The collected measurements are firstly fed into a feature selection module, where multiple state-of-the-art techniques have been used to extract the most informative features from the initial set of available features. The selected features are inputs to a knockoff generation module, where the generative adversarial networks are employed to generate the corresponding knockoffs of the selected features. The generated knockoffs are then fed into a classification module, in which two different classification models are used for the sake of fault diagnosis. Multiple experiments have been designed to investigate the effect of noise, fault resistance value, and sampling rate on the performance of the proposed framework. The effectiveness of the proposed framework is validated through a comprehensive study on the IEEE 118-bus system