Genetic algorithm optimized robust nonlinear observer for a wind turbine system based on permanent magnet synchronous generator.

This paper presents an optimal control scheme for a Permanent Magnet Synchronous Generator (PMSG) coupled to a wind turbine operating without a position sensor. This sensorless scheme includes two observers: The first observer uses the flux to estimate the speed. However, an increase in the temperature or a degradation of the permanent magnet characteristics will result in a demagnetization of the machine causing a drop in the flux. The second observer is therefore used to estimate these changes in the flux from the speed and guaranties the stability of the system. This structure leads to a better exchange of information between the two observers, eliminates the problem of encoder and compensates for the demagnetization problem. To improve the precision of the speed estimator, the gain of the non-linear observer is optimized using Genetic Algorithm (GA) and the speed is obtained from a modified Phase Locked Loop (PLL) method using an optimized Sliding Mode Controller (SMC). Furthermore, to enhance the convergence speed of this observer scheme and improve the performance of the system a Fast Super Twisting Sliding Mode Control (FSTSMC) is introduced to reinforce the SMC strategy. A series of simulations are presented to show the effectiveness and robustness of proposed observer scheme.

Robust control of series active power filters for power quality enhancement in distribution grids: Simulation and experimental validation.

This paper presents a simulation study and an experimental implementation of a single-phase Series Active Power Filter (SAPF) for the mitigation of harmonics in the load voltage. The aim is to regulate the injection voltage of the SAPF to compensate the grid voltage via the injection transformer in addition to maintaining the load voltage stable. The control strategies investigated in this work include Backstepping Sliding Mode Control (BSMC) and a neuro-fuzzy controller based on ANFIS (Adaptive Neuro-Fuzzy Inference System) l. The proposed control strategies for the single-phase SAPF are initially evaluated in simulations under MATLAB/Simulink and then validated on a laboratory-scale hardware experimental set up consisting of a source and a single-phase SAPF. A comparative study of these controllers with respect to their performance and robustness in mitigating power quality against voltage disturbances and harmonics is presented. Both simulation and experimental results have demonstrated that ANFIS-based controller was able to achieve superior performance and a lower total harmonic distortion (THD) as compared to the other control methods.

Intelligent model-based advisory system for the management of ventilated intensive care patients. Part II: Advisory system design and evaluation.

The optimisation of ventilatory support is a crucial issue for the management of respiratory failure in critically ill patients, aiming at improving gas exchange while preventing ventilator-induced dysfunction of the respiratory system. Clinicians often rely on their knowledge/experience and regular observation of the patient's response for adjusting the level of respiratory support. Using a similar data-driven decision-making methodology, an adaptive model-based advisory system has been designed for the clinical monitoring and management of mechanically ventilated patients. The hybrid blood gas patient model SOPAVent developed in Part I of this paper and validated against clinical data for a range of patients lung abnormalities is embedded into the advisory system to predict continuously and non-invasively the patient's respiratory response to changes in the ventilator settings. The choice of appropriate ventilator settings involves finding a balance among a selection of fundamentally competing therapeutic decisions. The design approach used here is based on a goal-directed multi-objective optimisation strategy to determine the optimal ventilator settings that effectively restore gas exchange and promote improved patient's clinical conditions. As an initial step to its clinical validation, the advisory system's closed-loop stability and performance have been assessed in a series of simulations scenarios reconstructed from real ICU patients data. The results show that the designed advisory system can generate good ventilator-setting advice under patient state changes and competing ventilator management targets.

Absolute electrical impedance tomography (aEIT) guided ventilation therapy in critical care patients: simulations and future trends.

Thoracic electrical impedance tomography (EIT) is a noninvasive, radiation-free monitoring technique whose aim is to reconstruct a cross-sectional image of the internal spatial distribution of conductivity from electrical measurements made by injecting small alternating currents via an electrode array placed on the surface of the thorax. The purpose of this paper is to discuss the fundamentals of EIT and demonstrate the principles of mechanical ventilation, lung recruitment, and EIT imaging on a comprehensive physiological model, which combines a model of respiratory mechanics, a model of the human lung absolute resistivity as a function of air content, and a 2-D finite-element mesh of the thorax to simulate EIT image reconstruction during mechanical ventilation. The overall model gives a good understanding of respiratory physiology and EIT monitoring techniques in mechanically ventilated patients. The model proposed here was able to reproduce consistent images of ventilation distribution in simulated acutely injured and collapsed lung conditions. A new advisory system architecture integrating a previously developed data-driven physiological model for continuous and noninvasive predictions of blood gas parameters with the regional lung function data/information generated from absolute EIT (aEIT) is proposed for monitoring and ventilator therapy management of critical care patients.

A hybrid hierarchical decision support system for cardiac surgical intensive care patients. Part II. Clinical implementation and evaluation.

OBJECTIVE: Patients emerging from cardiac surgery can display varying degrees of cardiovascular instability arising from potentially complex, multi-factorial and interlinked causes. Stabilization and control of the cardiovascular system are currently managed by healthcare experts using experiential knowledge, and, in some centers, manually inputted decision pathway algorithms. This paper describes a clinical trial undertaken to determine the basic functioning of a clinical decision support system (CDSS) designed and constructed by the authors to facilitate the control of the major cardiovascular components in the early post-operative phase. Part II follows Part I's description of the software and simulation testing of the CDSS, and describes the hardware setup of a patient monitoring and CDSS. The system is evaluated on three post-cardiac surgery intensive care patients whom had all undergone cardio-pulmonary bypass. METHODS: The study was approved by the Sheffield Teaching Hospitals National Health Service (NHS) Foundation Trust Research Ethics Committee and conducted at the North Trent cardio-thoracic surgical unit and cardiac intensive care unit (CICU), Northern General Hospital, Sheffield (UK). Patients considered as 'very likely' to require active intervention to support the cardiovascular function following routine cardiac surgery were recruited during pre-operative surgical and anesthetic assessment, giving written informed consent when admitted for their operation. These patients underwent routine induction and maintenance of anesthesia by a non-study consultant anesthetist and the operation performed. There were no restrictions placed on the types of invasive monitoring used, on the use of trans-oesophageal echocardiography, drug selection, or the anesthetic agents selected by the clinicians performing the operations. All patients had full, routine invasive and non-invasive monitoring applied, including electrocardiography, central venous and peripheral arterial catheterisation, urine outputs and central temperature. After chest closure the patients were transferred to the CICU, sedated and ventilated, and the study commenced by the study anesthetist (1st author). The patients were in a clinically stable condition when admitted to the unit, and were attended by the treating clinicians until the handover to the study anesthetist occurred. The LiDCOplus (lithium dilution cardiac output) monitor (LiDCO Limited, Flowers Building, Granta Park, Cambridge CB1 6GU, United Kingdom) was calibrated after attachment to the patient's arterial line, and the patient's beat-to-beat hemodynamic data transferred to the host laptop computer. The CDSS graphical interface displays the patient's clinical details and specific cardiovascular data and prompts the anesthetist to input the target ranges for each parameter, and select a suitable advisor frequency. This is the frequency with which the therapeutic advice is displayed on screen with an audible prompt for a control inputs from the anesthetist. In each case this was selected to be 30s. When the study anesthetist agreed with the CDSS advice (administration of fluid, commencing a drug, altering the drug infusion rate) the syringe motif on the "Advisor Infusion Rates" panel of the graphical interface was 'clicked' on and the infusion rate immediately and manually inputted to Graseby 3400 pumps. If any disagreement between the anesthetist and the computer's advice arose, the syringe motif on the "Expert Infusion Rates" panel of the preferred drug was 'clicked' on and the expert's therapeutic decision (e.g. infusion rate) was entered in the corresponding data field and then applied to the pump. During all trials, data was stored for off-line analysis. RESULTS: The CDSS successfully selected suitable drug therapies for each case and advised reasonable and appropriate infusion rates such that the study anesthetist did not have to override the suggested CDSS instructions and infusion rates. Under differing clinical conditions the system was able to maintain clinically appropriate and stable control of the cardiovascular system (CVS), with good profiles under noisy physiological measurements, and was readily able to regain control following transient deterioration of the patient hemodynamic parameters (coughing, or during blood sampling).

A hybrid hierarchical decision support system for cardiac surgical intensive care patients. Part I: Physiological modelling and decision support system design.

OBJECTIVE: To develop a clinical decision support system (CDSS) that models the different levels of the clinician's decision-making strategies when controlling post cardiac surgery patients weaned from cardio pulmonary bypass. METHODS: A clinical trial was conducted to define and elucidate an expert anesthetists' decision pathway utilised in controlling this patient population. This data and derived knowledge were used to elicit a decision-making model. The structural framework of the decision-making model is hierarchical, clearly defined, and dynamic. The decision levels are linked to five important components of the cardiovascular physiology in turn, i.e. the systolic blood pressure (SBP), central venous pressure (CVP), systemic vascular resistance (SVR), cardiac output (CO), and heart rate (HR). Progress down the hierarchy is dependent upon the normalisation of each physiological parameter to a value pre-selected by the clinician via fluid, chronotropes or inotropes. Since interventions at each and every level cause changes and disturbances in the other components, the proposed decision support model continuously refers back decision outcomes back to the SBP which is considered to be the overriding supervisory safety component in this hierarchical decision structure. The decision model was then translated into a computerised decision support system prototype and comprehensively tested on a physiological model of the human cardiovascular system. This model was able to reproduce conditions experienced by post-operative cardiac surgery patients including hypertension, hypovolemia, vasodilation and the systemic inflammatory response syndrome (SIRS). RESULTS: In all the simulated patients scenarios considered the CDSS was able to initiate similar therapeutic interventions to that of the expert, and as a result, was also able to control the hemodynamic parameters to the prescribed target values.