Intelligent Control of Switched Reluctance Motor for Electrical Vehicle Applications with Different Controller

تستخدم محركات المعاوقي المفتاحي لإنتاج الكثير من  عزم الدوران والتي تعمل عند التشبع المغناطيسي العالي. وبالنظر إلى التشبع المغناطيسي العالي، فإن العلاقة بين تيار الطور، وموقع الدوار هي علاقة غير خطية. لذلك فان  الضجيج، الاضطرابات، وعزم القصور الذاتي  عند  التحميل يمكن أن يكون لها جميعا تأثير سلبي على أداء المحرك المعاوقي المفتاحي. في هذه الدراسة تم تطوير وحدة التحكم الانزلاقي. وقد استخدم وحدة التحكم الانزلاقي في تنظيم السرع على مدى واسع  بما في ذلك المحرك المعاوقي المفتاحي في السرع العالية والسرع الواطئة وتقارن هذه الدراسة وحدة التحكم الانزلاقي مع وحدة التحكم التناسبي المتكامل التفاضلي في المحرك المعاوقي المفتاحي ذو 4/6 اقطاب باستعمال  الطرق الامثل للتحكم . ومقارنة  سرعة الجزء الدوار مع السرعة المضبوطة .فان وحدة التحكم الانزلاقي المتسارع هو الافضل من حيث الاداء والمتانة في  تطبيق السيارات الكهربائية  تبعا لنظام السيمولنك المستخدم Switched reluctance motors (SRM) are used to produce a lot of torque when they are operating at high magnetic saturation. Due to the high magnetic saturation, the relationship between phase current, rotor position, and the flux linkage of SRM is nonlinear. Noise, disturbances, and inertia of load torque can all have a negative impact on the SRM driver system's speed controller performance. In this study, the SRM driver system's sliding mode controller was developed .The sliding mode controller( SMC) speed controller was used to regulate speeds of the SRM throughout a wide range speeds, including high and low speeds. This study compares (SMC) with a modified reaching law and a Proportional Integral Divertive Control (PID) controller for a 6/4 pole SRM using an optimization technique for switching controllers. Furthermore, the rotor speed was simulated and compared to the reference speed. The Exponential Sliding Mode Controller (ExpSMC) is the best in terms of performance and robustness for an electric vehicle application, depending on a simulation of an established test bench using the two controllers

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population