Design, Simulation, Analysis and Optimization of PID and Fuzzy Based Control Systems for a Quadcopter

From MDPI via Jisc Publications RouterHistory: accepted 2021-09-07, pub-electronic 2021-09-10Publication status: PublishedUnmanned aerial vehicles or drones are becoming one of the key machines/tools of the modern world, particularly in military applications. Numerous research works are underway to explore the possibility of using these machines in other applications such as parcel delivery, construction work, hurricane hunting, 3D mapping, protecting wildlife, agricultural activities, search and rescue, etc. Since these machines are unmanned vehicles, their functionality is completely dependent upon the performance of their control system. This paper presents a comprehensive approach for dynamic modeling, control system design, simulation and optimization of a quadcopter. The main objective is to study the behavior of different controllers when the model is working under linear and/or non-linear conditions, and therefore, to define the possible limitations of the controllers. Five different control systems are proposed to improve the control performance, mainly the stability of the system. Additionally, a path simulator was also developed with the intention of describing the vehicle’s movements and hence to detect faults intuitively. The proposed PID and Fuzzy-PD control systems showed promising responses to the tests carried out. The results indicated the limits of the PID controller over non-linear conditions and the effectiveness of the controllers was enhanced by the implementation of a genetic algorithm to autotune the controllers in order to adapt to changing conditions

The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: a critical review

With the predicted depletion of natural resources and alarming environmental issues, sustainable development has become a popular as well as a much-needed concept in modern process industries. Hence, manufacturers are quite keen on adopting novel process monitoring techniques to enhance product quality and process efficiency while minimizing possible adverse environmental impacts. Hardware sensors are employed in process industries to aid process monitoring and control, but they are associated with many limitations such as disturbances to the process flow, measurement delays, frequent need for maintenance, and high capital costs. As a result, soft sensors have become an attractive alternative for predicting quality-related parameters that are ‘hard-to-measure’ using hardware sensors. Due to their promising features over hardware counterparts, they have been employed across different process industries. This article attempts to explore the state-of-the-art artificial intelligence (Al)-driven soft sensors designed for process industries and their role in achieving the goal of sustainable development. First, a general introduction is given to soft sensors, their applications in different process industries, and their significance in achieving sustainable development goals. AI-based soft sensing algorithms are then introduced. Next, a discussion on how AI-driven soft sensors contribute toward different sustainable manufacturing strategies of process industries is provided. This is followed by a critical review of the most recent state-of-the-art AI-based soft sensors reported in the literature. Here, the use of powerful AI-based algorithms for addressing the limitations of traditional algorithms, that restrict the soft sensor performance is discussed. Finally, the challenges and limitations associated with the current soft sensor design, application, and maintenance aspects are discussed with possible future directions for designing more intelligent and smart soft sensing technologies to cater the future industrial needs

Design, simulation, analysis and optimization of pid and fuzzy based control systems for a quadcopter

Unmanned aerial vehicles or drones are becoming one of the key machines/tools of the modern world, particularly in military applications. Numerous research works are underway to explore the possibility of using these machines in other applications such as parcel delivery, construction work, hurricane hunting, 3D mapping, protecting wildlife, agricultural activities, search and rescue, etc. Since these machines are unmanned vehicles, their functionality is completely dependent upon the performance of their control system. This paper presents a comprehensive approach for dynamic modeling, control system design, simulation and optimization of a quadcopter. The main objective is to study the behavior of different controllers when the model is working under linear and/or non‐linear conditions, and therefore, to define the possible limitations of the control-lers. Five different control systems are proposed to improve the control performance, mainly the stability of the system. Additionally, a path simulator was also developed with the intention of de-scribing the vehicle’s movements and hence to detect faults intuitively. The proposed PID and Fuzzy‐PD control systems showed promising responses to the tests carried out. The results indi-cated the limits of the PID controller over non‐linear conditions and the effectiveness of the controllers was enhanced by the implementation of a genetic algorithm to autotune the controllers in order to adapt to changing conditions.

Investigating the effects of renewable energy utilization towards the economic growth of Sri Lanka: A structural equation modelling approach

With the rapid depletion of natural resources and increased environmental pollution, nations across the world are in a desperate need of achieving the common goal of sustainable development. Combustion of fossil fuels along with other human activities over the past few decades have resulted in increased greenhouse gas emissions leading to the climate change and the multi-scale pollution of land, air and water that the world is experiencing today. Hence, the focus has now been shifted towards the sustainable and renewable energy sources while prompting a circular economy. Being one of the island nations of South Asia, Sri Lanka has also aligned the country's economic strategies with the global trends and aims to be a 100% sustainable energy dependent nation by 2050. Consequently, there has been a gradual rise in the renewable energy establishments in the country over the past two decades. This study aims to analyze the influence of these establishments on the economic growth of the country, using a structural equation modelling approach. A conceptual model was formulated to represent the relationships between the renewable energy consumption and the key economic indicators, and also a path analysis was carried out to explore the relationships among the variables. The fit of the conceptual model to the secondary data collected related to the economic and energy indicators was established using a number of statistical fit indices such as the Chi-square, absolute fit indices and the root mean square error of approximation. The results indicate that there is no significant direct effect of the renewable energy consumption on the GDP of Sri Lanka yet, but there is an indirect positive effect through capital formation and also an indirect negative effect through trade balance. Hence, it is clear that renewable energy establishments have to be promoted through incentives/policies that lead to capital accumulation via increased renewable energy consumption. Moreover, the analysis and the findings presented in this study can be related and are applicable to many of the emerging economies across the globe