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Heating, ventilation, and air conditioning (HVAC) systems are used to condition the indoor environment in buildings. They can be subjected to malfunctioning since they are the most extensively operated buildings' components that account alone for almost half of the total building energy usage. Therefore, fault diagnosis (FD) of the HVAC system is important to maintain the system's reliability and efficiency and provide preventive maintenance. This article presents a supervised FD strategy for single actuator faults in HVAC systems given that actuators, such as dampers and valves, are mostly prone to faults resulting in thermal discomfort and energy inefficiency in buildings. The proposed approach is based on 2-D convolutional neural networks (CNNs) using an efficient 1-D-to-2-D data transformation performed on the time-series signals acquired from the HVAC system. The performance of the CNNs is ensured by an optimal tuning of its significant hyperparameters using the Bayesian optimization algorithm toward maximizing the classification accuracy. The proposed 1-D-to-2-D data transformation approach is computationally efficient and eliminates the use of advanced signals preprocessing. It is performed in two schemes: the static and dynamic schemes to analyze the correlation between the system's variables and consider the temporal effects of the time-series signals without compromising the detection time. The proposed approach is developed and validated using simulation data collected from a three-zone HVAC system simulator using Transient System Simulation Tool (TRNSYS). It demonstrates improved performance compared to the 1-D CNN-based approach and the other standard data-driven approaches for actuator FD in HVAC systems. Scopus

In this paper, the problem of distributed event-based control of large scale power systems is addressed. Towards this end, a Direct Current (DC) microgrid that is composed of multiple interconnected Distributed Generation Units (DGUs) is considered. Voltage stability is guaranteed by utilizing decentralized local controllers for each DGU. A distributed discrete-time event-triggered (ET) consensus-based control strategy is then designed for current sharing in the DGUs. In this mechanism, the transmissions occur while a specified event is triggered to prevent unessential utilization of communication resources. The asymptotic stability of the ET-based controller is shown formally by using Lyapunov stability via linear matrix inequality (LMI) conditions. The effectiveness of the proposed methodology is demonstrated and substantiated in simulation case study.

The exponential growth of renewable electricity generation has transformed significantly the operating environment of gas turbines. Nowadays, gas turbines operate under transient conditions to support their renewable partners. Gas turbine dynamic performance simulation provides the means for assessing the engine behavior and designing engine controllers that will enable them to fulfill their new operating role. This paper presents a novel gas turbine performance system for representing the nonlinear behavior of a two-shaft gas turbine engine. The developed gas turbine engine model in MATLAB/Simulink environment is capable of simulating both steady state and transient operating conditions, and to facilitate the design of controllers for stable engine operation. Validation of the developed engine model with a gas turbine simulation package confirmed the excellent agreement among all the simulated measurements at transient conditions. The simulated behavior of a hybrid gas/wind power plant enabled the development of an optimized controller for empowering the gas turbine to support the intermittent wind turbines. The time responses of the main parameters of the hybrid gas/wind power plant demonstrated the significant amount of transient conditions that a gas turbine experiences for fulfilling the energy gap imposed by the wind turbines. Finally, the performance comparison of the hybrid power plant to a twin gas turbine power plant highlighted the effective reduction in NO x emissions.

Despite many advancements in extracorporeal membrane oxygenation (ECMO), the procedure is still correlated with a high risk of patient complications. Simulation-based training provides the opportunity for ECMO staff to practice on real-life scenarios without exposing ECMO patients to medical errors while practicing. At Hamad Medical Corporation (HMC) in Qatar, there is a critical need of expert ECMO staff. Thus, a modular ECMO simulator is being developed to enhance the training process in a cost-effective manner. This ECMO simulator gives the instructor the ability to control the simulation modules and run common simulation scenarios through a tablet application. The core modules of the simulation system are placed in the patient unit. The unit is designed modularly such that more modules can be added throughout the simulation sessions to increase the realism of the simulation sessions. The new approach is to enclose the patient unit in a trolley, which is custom-designed and made to include all the components in a modular fashion. Each module is enclosed in a separate box and then mounted to the main blood simulation loop box using screws, quick connect/disconnect liquid fittings, and electrical plugs. This method allows fast upgrade and maintenance for each module separately as well as upgrading modules easily without modifying the trolley’s design. The prototype patient unit has been developed for portability, maintenance, and extensibility. After implementation and testing, the prototype has proven to successfully simulate the main visual and audio cues of the real emergency scenarios, while keeping costs to a minimum Qatar University Scopus

With the rapid advancement in the industrial control technologies and the increased deployment of the industrial Internet of Things (IoT) in the buildings sector, this work presents an analysis of the security of the Heating, Ventilation, and Air Conditioning (HVAC) system which is a major component of the Building Management System (BMS), has become critical. This paper presents a Transient System Simulation Tool (TRNSYS) model of a 12-zone HVAC system that allows assessing the cybersecurity aspect of HVAC systems. The thermal comfort model and the estimated total power usage are used to assess the magnitude of the malicious actions launched against the HVAC system. Simulation data are collected and used to develop and validate a semi-supervised, data-driven attack detection strategy using Isolation Forest (IF) for the system under study. Three schemes of the proposed approach are investigated, which are: using raw data, using Principal Component Analysis (PCA) for feature extraction, and using 1D Convolutional Neural Network (CNN)-based encoder for temporal feature extraction. The proposed approach is compared with standard machine-learning approaches, and it demonstrates a promising capability in attack detection for a range of attack scenarios with high reliability and low computational cost. Qatar Foundation; Qatar National Research Fund Scopus

Extracorporeal membrane oxygenation (ECMO) is a modified form of CPB that supports intensive care patients' vital functions during recovery from cardiac or pulmonary trauma. ECMO, although lifesaving, is vulnerable to a plethora of mechanical complications which can cause mortality. This is why developing advanced training systems is of crucial importance. In this paper, as part of an ECMO simulator for training management, a novel thermochromic heater-cooler system is presented. The need of such contribution arises from the lack of high-realism blood simulation methodologies Hence, developed upon thermochromic ink, cost-effective blood simulation is achieved by temperature adjustment, simulating oxygenation and hypoxemia. The system has been developed as a prototype with successful and reversible transitions between dark and bright red blood color. After addressing the limitations, the heater-cooler will be integrated with the ECMO simulator, allowing unpreceded cost-efficient simulation possibilities. Qatar University Scopus

Novel drugs and treatment methods are introduced for the management of cancer at a very fast pace. Given the complexity and heterogeneity of the disease, decision making regarding the right choice of the drug, dose, drug combination, and treatment schedule has become a perplexing task. Cancer scientists and oncologists worldwide are looking at using the available treatment options in a judicious and more effective manner so as to improve the overall treatment outcome and life quality of the patients. Towards this end, optimal drug dose, treatment schedules, and treatment combinations should be identified. Hence, in this chapter, the control of various cancer therapies discussed in Chaps. 3- 9 using classical control strategies such as optimal and adaptive methods and heuristic methods such as ANN-, EA-, RL-, and FL-based methods are reviewed.

Experimental and clinical evidence suggest that many single agent-based therapeutic approaches that are discussed in Chaps. 3– 8 show an improved potency (additive or synergistic effects) when used in combination [1, 2, 3, 4, 5]. Such combination therapy approaches can bring together the positive aspects of each therapy method involved to improve the overall therapeutic efficacy and patient outcome. Hence, in this chapter, mathematical models of chemotherapy and immunotherapy, chemotherapy and oncolytic virotherapy, chemotherapy and HER2 targeted therapy, and immunotherapy and anti-angiogenic therapy are reviewed.

Anti-angiogenic therapy is used to interrupt the development of blood vessels towards and into the tumors from the existing blood vessels that surround the tumor micro-environment [1]. Anti-angiogenic therapy models primarily involves the dynamics of tumor cells, endothelial cells, angiogenesis inhibitory agents, and angiogenesis stimulatory agents. In this chapter, two mathematical models are discussed that depict different aspects of cancer dynamics under anti-angiogenic therapy.

The purpose of this work is to design a covert agent structure that can create a non-zero frequency deviation in the load frequency control (LFC) system and illustrate the necessity of a secure communication network for the load frequency control. LFC systems are one of the most time-consuming control loops of power systems that are vulnerable to cyber-attacks and disturbances. Cyber-attacks like covert attacks can affect the frequency performance of the LFC system without being detected, as the covert adversaries cancel out the influence of the deceptively added actuation signal by deducting the corresponding effect from the measurement signal. In this paper, a covert agent with feedback structure is formulated and it is shown that the covert adversary can take control of LFC system through the covert reference signal and can keep the relative offset between actual frequency deviation of LFC system and the measurement signal received by the integral controller within safe limits, such that the attacker remains undetected. Qatar Foundation; Qatar National Research Fund; Qatar University Scopus

Breast cancer is one of the major causes of mortality in women worldwide. The most aggressive breast cancer subtypes are human epidermal growth factor receptor-positive (HER2+) and triple-negative breast cancers. Therapies targeting HER2 receptors have significantly improved HER2+ breast cancer patient outcomes. However, several recent studies have pointed out the deficiency of existing treatment protocols in combatting disease relapse and improving response rates to treatment. Overriding the inherent actions of the immune system to detect and annihilate cancer via the immune checkpoint pathways is one of the important hallmarks of cancer. Thus, restoration of these pathways by various means of immunomodulation has shown beneficial effects in the management of various types of cancers, including breast. We herein review the recent progress in the management of HER2+ breast cancer via HER2-targeted therapies, and its association with the programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) axis. In order to link research in the areas of medicine and mathematics and point out specific opportunities for providing efficient theoretical analysis related to HER2+ breast cancer management, we also review mathematical models pertaining to the dynamics of HER2+ breast cancer and immune checkpoint inhibitors.

Industrial Control System (ICS) is a general term that includes supervisory control & data acquisition (SCADA) systems, distributed control systems (DCS), and other control system configurations such as programmable logic controllers (PLC). ICSs are often found in the industrial sectors and critical infrastructures, such as nuclear and thermal plants, water treatment facilities, power generation, heavy industries, and distribution systems. Though ICSs were kept isolated from the Internet for so long, significant achievable business benefits are driving a convergence between ICSs and the Internet as well as information technology (IT) environments, such as cloud computing. As a result, ICSs have been exposed to the attack vectors used in the majority of cyber-attacks. However, ICS devices are inherently much less secure against such advanced attack scenarios. A compromise to ICS can lead to enormous physical damage and danger to human lives. In this work, we have a close look at the shift of the ICS from stand-alone systems to cloud-based environments. Then we discuss the major works, from industry and academia towards the development of the secure ICSs, especially applicability of the machine learning techniques for the ICS cyber-security. The work may help to address the challenges of securing industrial processes, particularly while migrating them to the cloud environments.

This paper presents a novel supervised on-line fault diagnosis strategy in Heating, Ventilation, and Air conditioning (HVAC) systems for actuator faults using 2D Convolutional Neural Networks. It is based on an efficient 1-Dimensional to 2-Dimensional data transformation that eliminates the need for advanced signals pre-processing. The proposed approach aims to address the limitations found in the previous works in terms of the diagnosis accuracy by adopting the recently evolving topology of the Convolutional Neural Networks. It is developed and validated using simulation data collected for a 3-zone HVAC system simulator using Transient System Simulation Tool (TRNSYS). The proposed approach demonstrates an improved performance when compared to the other data-driven approaches for actuator fault diagnosis in HVAC systems. Scopus

The problem of distributed fast fault detection for a direct current (dc) microgrid that is composed of multiple interconnected distributed generation units (DGUs) is addressed in this article. A local fault detector is designed for each DGU based on the measured local state of the subsystem as well as the transmitted variables of neighboring measurements, which forms the fault detection network. Indeed, each DGU not only can detect its own fault, but also is capable of detecting its neighbor’s faults. Toward this end, a multiobjective optimization problem formulation based on the $H_\infty /H_-/$regional pole placement performance criteria is presented and it is solved using linear matrix inequality approach to obtain the parameters of the distributed fault detectors and the optimal amount of the performance indices. The convergence rate of the fault detection observer is improved with the proposed method which leads to fast fault detection in the dc microgrid system. The effectiveness and capabilities of the proposed methodology are demonstrated through a simulation case study. Scopus

Cyber security for Industrial Control Systems (ICS) is increasingly becoming an area of research as advance network technology continues to evolve providing extended connectivity between cyber world and control system hardware in a plant. In this paper, we present attack detection and isolation technique for sensor attacks on a binary distillation column as an important ICS field which can be targeted by attackers. At first, we present a hybrid model of a DC where the DC has been designed in Aspen Plus Dynamics and the control system has been implemented using Simulink. Then, the mathematical model of various sensor attacks which have been considered for the study are presented. Following that an attack detection and isolation technique based on state estimation using Luenberger observer has been presented. Finally we present the results illustrating effect of sensor attacks on DC performance along with validating the effectiveness of the proposed attack detection and isolation method. Qatar National Research Fund Scopus

In this paper, a dual estimation methodology is developed for both time-varying parameters and states of a nonlinear stochastic system based on the particle filtering scheme. Our developed methodology is based on a concurrent implementation of state and parameter estimation filters as opposed to using a single filter to simultaneously estimate the augmented states and parameters. The convergence and stability properties of our proposed dual estimation strategy are shown formally to be guaranteed under certain conditions. The advantage of our developed dual estimation method is justified by handling simultaneously and efficiently both the state and time-varying parameters of a nonlinear system. This is accomplished in the context of a health monitoring scheme that employs a unified approach to fault detection (FD), isolation, and identification in a single framework. The performance capabilities of our proposed FD methodology is demonstrated and evaluated by its application to a gas turbine engine through providing state and parameter estimation objectives under simultaneous and concurrent component fault scenarios. Extensive simulation results are provided to substantiate and justify the superiority of our proposed FD methodology when compared with another well-known alternative diagnostic technique that is available in the literature. ? 1993-2012 IEEE. Qatar National Research Fund, Qatar Foundation Scopus

In this paper, sensor fault detection and isolation of time-varying nonlinear dynamical systems is studied by utilizing an adaptive kernel principal component analysis (KPCA) solution as a useful method to overcome the weaknesses of conventional KPCA approach in dealing with time-varying dynamical processes. Toward this goal, adaptive Hotelling's T2 is used with KPCA to tackle the time-varying behavior of nonlinear systems. Moreover, for fault isolation, partial adaptive KPCA (AKPCA) is proposed where a set of residual signals is generated based on the structured residual set framework. The simulation studies demonstrate that using the proposed methodology, the occurrence of sensor faults in the nonlinear dynamic model of an aeroderivative gas turbine can be effectively detected and isolated in the presence of component degradation. - 2018 Elsevier Ltd This publication was supported by NPRP grant No. 4-195-2-065 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the re-sponsibility of the authors. Scopus

In this paper, a fault detection and isolation (FDI) methodology based on an immune system (IS) inspired mechanism known as the dendritic cell algorithm (DCA) is developed and implemented. Our proposed DCA-based FDI methodology is then applied to a well-known wind turbine test model. The proposed DCA-based scheme performs both detection as well as isolation of sensor faults given dual sensor redundancy, unlike other works in the literature that only address the fault detection problem and rely on analytical redundancy approach for accomplishing the fault isolation task. A nonparametric statistical comparison test is also performed to compare the performance of the DCA-based FDI scheme with another IS-based scheme known as the negative selection algorithm. Through extensive simulation case study scenarios the capabilities and performance of our proposed methodologies have been fully demonstrated and justified. Scopus

The increasing threat of cancer to human life and the improvement in survival rate of this disease due to effective treatment has promoted research in various related fields. This research has shaped clinical trials and emphasized the necessity to properly schedule cancer chemotherapy to ensure effective and safe treatment. Most of the control methodologies proposed for cancer chemotherapy scheduling treatment are model-based. In this paper, a reinforcement learning (RL)-based, model-free method is proposed for the closed-loop control of cancer chemotherapy drug dosing. Specifically, the Q-learning algorithm is used to develop an optimal controller for cancer chemotherapy drug dosing. Numerical examples are presented using simulated patients to illustrate the performance of the proposed RL-based controller.

The domination of gas turbines in the energy arena is facing many challenges from environmental regulations and the plethora of renewable energy sources. The gas turbine has to operate under demand-driven modes and its components consume their useful life faster than the engines of the base-load operation era. As a result the diagnostics and prognostics tools should be further developed to cope with the above operation modes and improve the condition based maintenance (CBM). In this study, we present a derivative-driven diagnostic pattern analysis method for estimating the performance of gas turbines under dynamic conditions. A real time model-based tuner is implemented through a dynamic engine model built in Matlab/Simulink for diagnostics. The nonlinear diagnostic pattern is then partitioned into data-windows. These are the outcome of a data analysis based on the second order derivative which corresponds to the acceleration of degradation. Linear regression is implemented to locally fit the detected deviations and predict the engine behavior. The accuracy of the proposed method is assessed through comparison between the predicted and actual degradation by the remaining useful life (RUL) metric. The results demonstrate and illustrate an improved accuracy of our proposed methodology for prognostics of gas turbines under dynamic modes. 2017 Elsevier Ltd Qatar Foundation; Qatar National Research Fund Scopus

Connecting high-power Renewable Energy Sources (RESs) to the utility grid has to be secured considering international standards to guarantee the efficiency, reliability, and stability of the power system. Redundancy, modularity, and scalability of the Modular Multilevel Converter (MMC) make it a proper option for high-power RESs such as offshore wind energy and PV systems. In this paper, the control of grid-connected MMC is investigated in order to comply with low voltage ride-through requirements. The modeling of MMC is achieved in this study using the Average Value Model (AVM). Grid integration through LCL-filter is explored since an LCL-filter may be required for Quasi two-level (Q2L) PWM operation of MMC and/or MMC with a relatively low-level order. A switching-gain integral state feedback with full-order observer controller is designed, evaluated, and verified for LCL-filter connecting MMC to the utility grid. Low Voltage Ride Through (LRVT) capability using Reactive Power Compensation (RPC) technique for grid voltage sag based on international codes such as German code is considered with the designed controller.

This paper presents a data-driven sensor fault diagnosis algorithm for multi-zone Heating, Ventilation, and Air conditioning (HVAC) systems based on an Auto-Associative Neural Networks (AANNs) framework. The proposed method can be used for both single and multiple sensor faults diagnosis by comparing the input of the network with the output and then identifying and isolating the fault based on the generated residuals. The implementation of the proposed method and the evaluation results are presented and demonstrated thoroughly in the paper for a simple 2-zone HVAC system using the transient systems simulation program (TRNSYS) considering single and multiple bias and drift sensor faults. The performance of the AANN-based approach is compared with a Principle Component Analysis (PCA)-based method and the results show a significant improvement in terms of the diagnosis accuracy. - 2019 IEEE. Scopus

Background and objectives: In recent decades, cancer has become one of the most fatal and destructive diseases which is threatening humans life. Accordingly, different types of cancer treatment are studied with the main aim to have the best treatment with minimum side effects. Anti-angiogenic is a molecular targeted therapy which can be coupled with chemotherapy and radiotherapy. Although this method does not eliminate the whole tumor, but it can keep the tumor size in a given state by preventing the formation of new blood vessels. In this paper, a novel model-free method based on reinforcement learning (RL) framework is used to design a closed-loop control of anti-angiogenic drug dosing administration. Methods: A Q-learning algorithm is developed for the drug dosing closed-loop control. This controller is designed using two different values of the maximum drug dosage to reduce the tumor volume up to a desired value. The mathematical model of tumor growth under anti-angiogenic inhibitor is used to simulate a real patient. Results: The effectiveness of the proposed method is shown through in silico simulation and its robustness to patient parameters variation is demonstrated. It is demonstrated that the tumor reaches its minimal volume in 84 days with maximum drug inlet of 30 mg/kg/day. Also, it is shown that the designed controller is robust with respect to ± 20% of tumor growth parameters changes. Conclusion: The proposed closed-loop reinforcement learning-based controller for cancer treatment using anti-angiogenic inhibitor provides an effective and novel result such that with a clinically valid and safe dosage of drug, the volume reduces up to 1mm3 in a reasonable short period compared to the literature.

This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank&rsquo, s control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Na&iuml, ve Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments.

In this paper, a novel robust sensor fault detection and isolation (FDI) strategy using the multiple model-based (MM) approach is proposed that remains robust with respect to both time-varying parameter uncertainties and process and measurement noise in all the channels. The scheme is composed of robust Kalman filters (RKF) that are constructed for multiple piecewise linear (PWL) models that are constructed at various operating points of an uncertain nonlinear system. The parameter uncertainty is modeled by using a time-varying norm bounded admissible structure that affects all the PWL state space matrices. The robust Kalman filter gain matrices are designed by solving two algebraic Riccati equations (AREs) that are expressed as two linear matrix inequality (LMI) feasibility conditions. The proposed multiple RKF-based FDI scheme is simulated for a single spool gas turbine engine to diagnose various sensor faults despite the presence of parameter uncertainties, process and measurement noise. Our comparative studies confirm the superiority of our proposed FDI method when compared to the methods that are available in the literature. 2016 Elsevier Ltd. All rights reserved. Scopus

In this paper, a novel sensor fault detection, isolation, and identification (FDII) strategy is proposed using the multiple-model (MM) approach. The scheme is based on multiple hybrid Kalman filters (MHKFs), which represents an integration of a nonlinear mathematical model of the system with a number of piecewise linear (PWL) models. The proposed fault detection and isolation (FDI) scheme is capable of detecting and isolating sensor faults during the entire operational regime of the system by interpolating the PWL models using a Bayesian approach. Moreover, the proposed MHKF-based FDI scheme is extended to identify the magnitude of a sensor fault using a modified generalized likelihood ratio method that relies on the healthy operational mode of the system. To illustrate the capabilities of our proposed FDII methodology, extensive simulation studies are conducted for a nonlinear gas turbine engine. Various single and concurrent sensor fault scenarios are considered to demonstrate the effectiveness of our proposed online hierarchical MHKF-based FDII scheme under different flight modes. Finally, our proposed hybrid Kalman filter (HKF)-based FDI approach is compared with various filtering methods such as the linear, extended, unscented, and cubature Kalman filters corresponding to both interacting and noninteracting MM-based schemes. Our comparative studies confirm the superiority of our proposed HKF method in terms of promptness of the fault detection, lower false alarm rates, as well as robustness with respect to the engine health parameter degradations. 2015 IEEE. Scopus

In this paper, linear parameter-varying (LPV) control is considered for a solution copolymerization reactor, which takes into account the time-varying nature of the parameters of the process. The nonlinear model of the process is first converted to an exact LPV model representation in the state-space form that has a large number of scheduling variables and hence is not appropriate for control design purposes due to the complexity of the LPV control synthesis problem. To reduce such complexity, two approaches are proposed in this paper. First, an approximate LPV representation with only one scheduling variable is obtained by means of a parameter set mapping (PSM). The second approach is based on reformulating the nonlinear model so that it provides an LPV model with a fewer number of scheduling parameters but preserves the same input–output behavior. Moreover, in the implementation of the LPV controllers synthesized with the derived models, the unmeasurable scheduling variables are estimated by an extended Kalman filter. Simulation results using the nonlinear model of the copolymerization reactor are provided in order to illustrate the performance of the proposed controllers in reducing the convergence time and the control effort.

The Heating, Ventilation, and Air conditioning (HVAC) system is a major system in buildings for conditioning the indoor environment. Sensor data validation and fault diagnosis for HVAC systems are essentially important to secure a reliable and efficient operation since sensor measurements are vital for the HVAC closed-loop control system. The aim of this work is to address this matter by developing a data-driven approach using the system's normal operation data and without the need for the knowledge of the mathematical model of the system. It is based on an Auto-Associative Neural Network (AANN) that is structured and trained to construct an input-output mapping model based on data dimensionality reduction that is capable of validating sensor measurements in terms of sensor error correction, missing data replacement, noise filtering, and inaccuracy correction. It can be used for both single and multiple sensor faults diagnosis by monitoring the consistency between the actual and the AANN-estimated sensor reading. The validation of the proposed method is demonstrated on data obtained from a 3-zone HVAC system simulated in TRNSYS. The evaluation results show the effectiveness of the proposed approach and an improvement in terms of data validation and diagnostic accuracy when compared with a PCA-based method.

Modular multilevel converter is becoming a great candidate for high power applications due to its valuable features. However, its higher performance comes at the expenses of higher complexity, which increases the risks of failures. Power switches can be accounted among the most vulnerable components in this converter and their failure can lead to the regular functioning destabilization and eventual shutdowns. Failures in power switches can be induced by either open or short circuits faults. This paper presents the principle component analysis, which is a data-driven approach, to tackle the open circuit faults detection in modular multilevel converters. The results were compared to the performance of a model-based technique, the sliding mode observer, and showed a better performance of the adopted approach. Scopus

The problem of simultaneous fault detection, isolation and tracking (SFDIT) control design for linear systems subject to both bounded energy and bounded peak disturbances is considered in this work. A dynamic observer is proposed and implemented by using the H?/H?/L1 formulation of the SFDIT problem. A single dynamic observer module is designed that generates the residuals as well as the control signals. The objective of the SFDIT module is to ensure that simultaneously the effects of disturbances and control signals on the residual signals are minimised (in order to accomplish the fault detection goal) subject to the constraint that the transfer matrix from the faults to the residuals is equal to a pre-assigned diagonal transfer matrix (in order to accomplish the fault isolation goal), while the effects of disturbances, reference inputs and faults on the specified control outputs are minimised (in order to accomplish the fault-tolerant and tracking control goals). A set of linear matrix inequality (LMI) feasibility conditions are derived to ensure solvability of the problem. In order to illustrate and demonstrate the effectiveness of our proposed design methodology, the developed and proposed schemes are applied to an autonomous unmanned underwater vehicle (AUV). - 2017 Informa UK Limited, trading as Taylor & Francis Group. This publication was supported in part by NPRP [grant number NPRP 5 - 045 - 2 - 017] from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

This paper demonstrates the application of the linear parameter-varying (LPV) framework to control a copolymerization reactor. An LPV model representation is first developed for a nonlinear model of the process. The LPV model complexity in terms of the model order and the number of scheduling variables is then reduced by truncating those system states that have insignificant direct influence on the input-output behavior of the system and do not directly appear in the output equations of the model. It is important to note that these truncated states are still preserved in the reduced model by affecting the scheduling parameters and hence enabling the representation of the input-output map. Using the derived model, a linear fractional transformation (LFT) based LPV controller synthesis approach is used to synthesize a controller for the process. Simulation based studies of the closed-loop behavior of the system regulated by the designed LPV controller demonstrate that the LPV controller solution outperforms a model predicative control designed previously for this system in terms of the achieved control performance and the online computational effort. 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Qatar National Research Fund Scopus

Most buildings nowadays are equipped with Heating, Ventilation, and Air-Conditioning (HVAC) systems dedicated for processing indoor air in terms of different parameters: temperature, humidity, pressure, and quality. Recent studies show that developed countries consume up to 40% of the energy on commercial buildings and half of that is used for air-conditioning purposes, more precisely in air cooling and heating. For that, it is important to develop efficient HVAC control systems that minimize energy usage and achieve occupants' comfort. Buildings are composed of multiple interconnected zones and the simple HVAC control approach is to control zones temperatures locally based on the thermal loads of individual zones. However, it is realistic to account for the thermal interaction between the zones in the controller design to analyze the extent of its effects on the HVAC control system. This paper discusses the design of two-zone HVAC control system using feedback linearization considering the presence of zones interaction. The zones interaction is modeled using physics and thermodynamics laws. The effect of the interaction on control effort and tracking error is analyzed using simulation results. Scopus

In this paper, a fault tolerant control (FTC) scheme for multi-agent systems subject to loss of effectiveness actuator faults is investigated. The main objective of the proposed FTC approach is to keep the performance of a multi-agent system after the occurrence of actuator faults similar to the healthy one. For this purpose, an adaptive virtual actuator is designed for each agent without a need to have a separate fault detection, isolation and identification unit and it is shown that the difference between the states of each agent before and after the occurrence of actuator faults converges to zero. The proposed FTC scheme is independent of the mission of the multi-agent systems. Simulation results corresponding to a team of F-8 aircrafts demonstrate and illustrate the effectiveness of our proposed FTC scheme. 2017 American Automatic Control Council (AACC). Scopus

This paper is concerned with the distributed event-triggered based consensus problem of multiple underactuated surface vessels. The considered vehicles are underactuated where the number of the vehicles actuators is less than the vehicles degrees of freedom. Moreover, information exchange among the vehicles is event-based and vehicles do not need to broadcast their information continuously. Unactuated and actuated states of considered vehicles are strongly coupled, hence a state transformation is utilized to represent the vehicle model in a cascade nonlinear system format, and consequently switching controllers are designed to make the vehicles to reach a consensus. Furthermore, agents broadcast their information based on predefined event triggering conditions and the amount of data exchange among the surface vessels is significantly decreased. To illustrate the performance of the proposed approach, simulation results are provided. 1 2017 IEEE. Scopus

In this paper, using the state-dependent Riccati equation (SDRE) technique, a suboptimal control law is designed to solve the tracking problem for networked nonlinear discrete-time systems. Based on the proposed method, an event-triggered technique is also developed to reduce the information exchange between the controller and the actuator in a networked control system. It is shown that the obtained closed-loop system is asymptotically stable under mild conditions. The proposed method is applied to a nonlinear benchmark (Vander Pol's oscillator system) and the simulation results demonstrate the effectiveness of the proposed approach for solving the tracking problem of a nonlinear system in a networked control framework. Keywords: Nonlinear system, Suboptimal tracking controller, State-dependent Riccati equation (SDRE), Networked control system, Event-triggered. 1 2017 IEEE. Scopus

The focus of several clinical trials and research in the area of clinical pharmacology is to fine tune the drug dosing in the phase of additive, antagonistic, and synergistic drug interactive effects. It is important to consider the interactive effects of the drugs to restrict the drug usage to the optimal level required to achieve certain therapeutic effects. Such optimal drug dosing methods will minimize the adverse drug effects and cost associated with the treatment. In this paper, we discuss the use of a reinforcement learning (RL)-based controller to fine tune the drug titration while different drugs with interactive effects are administrated simultaneously. We demonstrate the efficacy of the method by using 25 simulated patients for the simultaneous infusion of a sedative and analgesic drug which has synergistic interactive effect. 1 2017 IEEE. This publication was made possible by the GSRA grant No. GSRA1-1-1128-13016 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors. Scopus

An actuator fault detection and isolation (FDI) scheme is proposed in this paper for differential drive mobile robots based on the concept of multiple model approach. The nonlinear kinematic model of the mobile robot is discretized and a bank of Extended Kalman filters (EKF) is designed to detect and isolate faults. Experimental results are presented to demonstrate the efficiency of the proposed FDI approach. 1 2017 European Union. Scopus

High operational and maintenance costs represent as major economic constraints in the wind turbine (WT) industry. These concerns have made investigation into fault diagnosis of WT systems an extremely important and active area of research. In this paper, an immune system (IS) inspired methodology for performing fault detection and isolation (FDI) of a WT system is proposed and developed. The proposed scheme is based on a self nonself discrimination paradigm of a biological IS. Specifically, the negative selection mechanism [negative selection algorithm (NSA)] of the human body is utilized. In this paper, a hierarchical bank of NSAs are designed to detect and isolate both individual as well as simultaneously occurring faults common to the WTs. A smoothing moving window filter is then utilized to further improve the reliability and performance of the FDI scheme. Moreover, the performance of our proposed scheme is compared with another state-of-the-art data-driven technique, namely the support vector machines (SVMs) to demonstrate and illustrate the superiority and advantages of our proposed NSA-based FDI scheme. Finally, a nonparametric statistical comparison test is implemented to evaluate our proposed methodology with that of the SVM under various fault severities. 1 2013 IEEE. Scopus

This paper studies the derivation of kinematic and dynamic models of a newly developed active ankle foot orthosis (AFO) that was designated for testing gait-assistive compliance control algorithms. The AFO is characterized with a closed-loop chain structure and powered by a linear ServoTube motor making it very fast and responsive. The paper first derives kinematic and dynamic models for the AFO. Then, a torque controller is developed based on the dynamic model of the AFO and it is shown that the AFO is able to track the position and velocity of the desired trajectory with a satisfactory accuracy. 1 2017 IEEE. Scopus

In this paper, we present a new approach for the design of fault detection and isolation (FDI) filters for a class of linear parameter-varying (LPV) multi-agent systems, where the state-space representations of the agents are not identical. The developed formulation for the fault detection and isolation offers a distributed filter design method, in which each agent uses sensor measurements both locally and from the neighboring agents. Each FDI filter is in the 'unknown input observer' form which are designed so that their outputs, i.e., residual signals, are: (i) robust with respect to the external disturbance inputs, and (ii) sensitive with respect to the fault signals. Finally, a numerical example is given to illustrate the efficacy of the main results of the paper. Scopus

This paper presents an electromyographic (EMG)based digital processing system for estimating foot direction of motion and force exerted from muscles of the human lower leg. This information can be used in the control frameworks of ankle-foot orthoses (AFOs). In essence, it is usually necessary to estimate the ankle's muscles voluntary contraction (VC) and user's intention of motion in order to control an AFO in a reliable and effective way. Consequently, a data processing system is developed that can designate these parameters from EMG signals. The voluntary contraction is calculated based on the root mean square (RMS) of EMG signals, and the direction of motion is estimated by using a feature-based classifier. The experiments are performed on five healthy, sitting subjects and the methodology consists of selecting discriminative features and using basic classifiers. The results of this work show that the direction of motion can be estimated in real-time with high accuracy. 1 2017 IEEE. This publication was made possible by NPRP grant No. 6-463-2-189 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

In this paper, distributed control reconfiguration strategies for directed switching topology networked multi-agent systems are developed and investigated. The proposed control strategies are invoked when the agents are subject to actuator faults and while the available fault detection and isolation (FDI) modules provide inaccurate and unreliable information on the estimation of faults severities. Our proposed strategies will ensure that the agents reach a consensus while an upper bound on the team performance index is ensured and satisfied. Three types of actuator faults are considered, namely: the loss of effectiveness fault, the outage fault, and the stuck fault. By utilizing quadratic and convex hull (composite) Lyapunov functions, two cooperative and distributed recovery strategies are designed and provided to select the gains of the proposed control laws such that the team objectives are guaranteed. Our proposed reconfigurable control laws are applied to a team of autonomous underwater vehicles (AUVs) under directed switching topologies and subject to simultaneous actuator faults. Simulation results demonstrate the effectiveness of our proposed distributed reconfiguration control laws in compensating for the effects of sudden actuator faults and subject to fault diagnosis module uncertainties and unreliabilities. 1 2017 ISA Scopus

This study develops a novel event-triggered control design methodology for discrete-time dynamic systems characterised by linear parameter-varying (LPV) models. To this end, an event-triggering mechanism is first introduced for LPV systems aiming at reducing the data transmission of states, scheduling variables and controller outputs, i.e. at both the sensor and the controller nodes. Then, an event-based LPV state feedback controller is proposed to achieve a reference tracking objective. Sufficient conditions for simultaneous design of the controller parameters and event-triggering conditions are provided in terms of linear matrix inequality conditions to guarantee the stability of the closed-loop system and to track a desired step reference signal. The experimental results are finally presented to demonstrate and validate the properties and performance of the proposed control design approach on a laboratory tank system. 1 The Institution of Engineering and Technology 2017. Scopus

This paper presents design, modeling and control of a small autonomous boat (SAB) where an Remote Control (RC) commercial boat is augmented with an embedded system and the required sensors in order to conduct simple maneuvers autonomously. The detailed kinematic and dynamic equations of the SAB are presented and simulation and experimental results for the boat heading control demonstrate the efficacy of SAB. 1 2017 IEEE. This publication was made possible by NPRP grants No. NPRP 5 - 045 - 2 - 017 and No. NPRP 6-463-2-189 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

In this paper, a novel hybrid architecture is proposed for developing a prognosis and health monitoring methodology for nonlinear systems through integration of model-based and computationally intelligent-based techniques. In our proposed framework, the well-known particle filters (PFs) method is utilized to estimate the states as well as the health parameters of the system. Simultaneously, the system observations are predicted through an observation forecasting scheme that is developed based on neural networks (NNs) paradigms. The objective is to construct observation profiles that are to be used in future time horizons. Our proposed online training that is utilized for observation forecasting enables the NNs models to track nonergodic changes in the profiles that are present due to presence of hidden damage affecting the system health parameters. The forecasted observations are then utilized in the PFs to predict the evolution of the system states as well as the health parameters (which are considered to be time-varying due to effects of degradation and damage) into future time horizons. Our proposed hybrid architecture enables one to select health signatures for determining the remaining useful life of the system or its components not only based on the system observations but also by taking into account the system health parameters that are not physically measurable. Our proposed hybrid health monitoring methodology is constructed and developed by invoking a special framework where implementation of the observation forecasting scheme is not dependent on the structure of the utilized NNs model. In other words, changing the network structure will not significantly affect the prediction accuracy associated with the entire health prediction scheme. To verify and validate the above results and as a case study, our proposed hybrid approach is applied to predict the health condition of a gas turbine engine when it is affected by and subjected to fouling and erosion degradation and fault damages. Manuscript received June 1, 2016; accepted July 27, 2016. Date of publication August 15, 2016; date of current version July 17, 2017. This work was supported by the Qatar National Research Fund (a member of Qatar Foundation) under NPRP Grant 4-195-2-065. This paper was recommended by Associate Editor G. Provan. Scopus

In this study, the authors present a new approach for the design of distributed state estimation and fault detection and isolation (FDI) filters for a class of linear parameter-varying multi-agent systems, where the state-space representations of the agents are not identical. The developed formulation for the FDI offers a distributed filter design method, in which each agent uses sensor measurements both locally and from the neighbouring agents. Each FDI filter is in the 'unknown input observer' form which are designed so that their outputs, i.e. residual signals, are: (i) robust with respect to the external disturbance inputs and (ii) sensitive with respect to the fault signals. Moreover, it is shown that using the proposed methodology each agent is able to estimate not only its own states, but also states of its nearest neighbours in the presence of external disturbances and faults. Finally, a numerical example is given to illustrate the efficacy of the main results of the paper. Scopus

In this paper, design and development of fault tolerant control (FTC) of linear systems subject to loss of effectiveness and time-varying additive actuator faults as well as an external disturbance using the fault-hiding approach is investigated. The main idea of this approach is to keep the nominal controller and to design a virtual actuator that is inserted between the faulty plant and the nominal controller in order to hide actuator faults from the nominal controller while no information about the nominal controller is needed and consequently the performance of the system before and after the occurrence of actuator faults are kept to be the same. The proposed state feedback adaptive virtual actuator does not require a separate fault detection, isolation and identification (FDII) unit. An illustrative example is given to demonstrate the effectiveness of the proposed adaptive virtual actuator. Qatar National Research Fund,QNRF,Qatar Foundation, QF Scopus

In this paper, sensor fault detection and isolation of nonlinear time-varying dynamical systems is investigated based on a fast partial block-wise adaptive Kernel Principal Component Analysis (KPCA) scheme. Using the proposed partial adaptive KPCA, faults are diagnosed perfectly and it is possible to prevail the shortcomings of the conventional KPCA and PCA methods. It is shown through simulation studies that the occurrence of sensor faults in the nonlinear dynamical model of an aeroderivative gas turbine can be detected and isolated effectively using the proposed approach. 1 2017 IEEE. This publication was made possible by NPRP grant No.5 - 574 - 2 -233 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

In the area of robust control, fault diagnosis, and fault tolerant control of linear systems, many fundamental problems can be recast as $H_\infty$ , $l_1$ and generalized $H_2$ control frameworks leading to the so-called mixed norm or multiobjective optimization problems. This paper develops a new linear matrix inequality (LMI) approach to the problems of event-triggered multiobjective synthesis of feedback controllers and fault diagnosis filters through a unified framework. Toward this end, at first a general problem known as event-triggered integrated fault detection, isolation and control (E-IFDIC) is defined. By utilizing a filter to represent, characterize, and specify the E-IFDIC module, a multiobjective formulation of the problem is developed based on $H_\infty$ , $H_{-}$ , $l_1$ and generalized $H_2$ performance criteria. It is shown that when an event-triggered strategy is applied to both the sensor and E-IFDIC module, the amount of data that is sent through the sensor-to-E-IFDIC module and E-IFDIC module-to-actuator channels are dramatically reduced. A set of LMI feasibility conditions is derived to ensure the solvability of the problem, as well as to simultaneously obtain the E-IFDIC module parameters and the event-triggered conditions. Finally, it is shown that certain existing problems in the fields of time and event-triggered control and fault diagnosis can be considered as special cases of our proposed methodology. Two industrial case studies are also provided to illustrate and demonstrate the effectiveness of our proposed design methodology when compared with available work in the literature.

The critical task of anesthesia administration demands optimal drug titration by considering patient-specific features and sedation requirements. Though there exist several sedation assessment monitors, physicians still largely rely on subjective sedation scoring systems. This paper presents an optimal infusion controller for the continuous infusion of midazolam along with the analgesic fentanyl. Specifically, we extend the convex optimization-based optimal drug dosing regimen for propofol to midazolam. We additionally propose an alternative optimization approach using the Kullback-Leibler divergence measure of two probability distributions. It is shown that by using the new proposed approach one can achieve the desired sedation level by using less drug infusion in comparison with previous methods. A nonlinear two-compartment model is used to describe the pharmacokinetics of midazolam and a stochastic pharmacodynamic model is used that utilizes the modified Ramsay sedation scoring method to link the plasma drug concentration with the effective probabilistic sedation level.