PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.Comment: This paper has been accepted for publication in Information Science Journal 201

An Efficient Microcontroller Based Sun Tracker Control for Solar Cell Systems

The solar energy is fast becoming a different means of electricity resource. Now in world Fossil fuels are seriously depleting thus the need for another energy source is a necessity. To create effective utilization of its solar, energy efficiency must be maximized. An attainable way to deal with amplifying the power output of sun-powered exhibit is by sun tracking. This paper presents the control system for a solar cell orientation device which follows the sun in real time during daytime

Intelligent Controller Based on Artificial Neural Network and INC Based MPPT for Grid Integrated Solar PV System

Solar photovoltaic (PV) systems have become an integral part of today's advanced energy infrastructure due to its low kinetic energy, its abundance availability, and its freedom from human interference. Solar PV systems have the potential to greatly reduce our reliance on fossil fuels, but their intermittent nature means they cannot provide a constant source of electricity. The system's security should be well thought out, and it should be able to withstand a lot of abuse. The current energy system faces a significant difficulty in ensuring continuous supply. In this study, a three-phase, two-stage photovoltaic system that is managed by artificial neural networks (ANN). A DC-DC boost converter with maximum power point tracking (MPPT) based on the incremental conductance (INC) method is incorporated in the first stage. In the next step, an ANN-based controller optimizes the performance of a three-phase switching PWM inverter that is connected to the grid by controlling currents along the d-q axis. Comprehensive simulations were carried out using MATLAB or Simulink to evaluate the system's performance under various illumination and temperature conditions. Results show that the suggested approach outperforms the baseline in a number of areas. Better dynamic reactions, accurate tracking of reference currents within permissible bounds, and quick settling periods after startup are all displayed by it. These findings show that our method has the potential to greatly improve the efficiency and dependability of solar PV systems. The results of this study have implications for renewable energy in general and present a viable path toward enhancing the resilience and sustainability of energy infrastructure

Advancing automation and robotics technology for the space station and for the US economy: Submitted to the United States Congress October 1, 1987

In April 1985, as required by Public Law 98-371, the NASA Advanced Technology Advisory Committee (ATAC) reported to Congress the results of its studies on advanced automation and robotics technology for use on the space station. This material was documented in the initial report (NASA Technical Memorandum 87566). A further requirement of the Law was that ATAC follow NASA's progress in this area and report to Congress semiannually. This report is the fifth in a series of progress updates and covers the period between 16 May 1987 and 30 September 1987. NASA has accepted the basic recommendations of ATAC for its space station efforts. ATAC and NASA agree that the mandate of Congress is that an advanced automation and robotics technology be built to support an evolutionary space station program and serve as a highly visible stimulator affecting the long-term U.S. economy

A Review of Control Techniques Future Trends in Wind Energy Turbine Systems with Doubly Fed Induction Generators (DFIG)

تعتبر طاقة الرياح حاليا واحدة من أكثر مصادر الطاقة الخضراء النظيفة الملاءمة على نطاق واسع في العالم. تم تطوير العديد من مبادئ توربينات الرياح بستخدام  المولدات المختلفة لتحويل طاقة الرياح المتاحة إلى طاقة كهربائية. يعد نظام المولد الحثي ذي التغذية المزدوجة DFIG لتوربينات الرياح ذات السرعة المتغيرة نسبيا (VSWT) هو الأكثر ملاءمة لطاقة توربينات الرياح بسبب فوائده العديدة مقارنة بتوربينات الرياح ذات السرعة الثابتة نسبيا (FSWT). تقدم هذه الورقة مراجعة و مقارنة عن طاقة توربينات الرياح المختلفة وملخصًا قيمًا للعمل الأخير المتعلقة بأنظمة طاقة الرياح المختلفة (WECS) لنمذجة DFIG وأقصى نقطة طاقة MPP وأحدث نظام تحكم للتشغيل. ومن ناحية أخرى تم في الدراسة الحالية تقديم مقارنات ومناقشات بين توربينات الرياح المختلفة لتكون مفيدة للدراسات البحثية.Wind energy is currently widely regarded as one of the most favorable green clean sources of energy. Several wind turbine principles with various generator architectures have been evolved to exchange the available wind energy into electric power. The DFIG partial Variable-Speed Wind Turbine (VSWT) system is most proper for wind turbine energy because of its numerous benefits over Fixed-Speed Wind Turbines (FSWT). This paper introduces a comparative review of the different wind turbine conversion energy and a valuable summary of the recent work in the literature on different Wind Energy Conversion Systems (WECS) of a DFIG modeling, Maximum Power Point (MPP), and the latest control system for operation. On the other side, comparisons and discussions between different wind turbines have been presented in the current study to be beneficial for research studies