Trajectory tracking of Unmanned Aerial Vehicles (UAVs) is a very challenging and complicated field of research due to their nonlinear and underactuacted dynamics. In this paper, a real time trajectory tracking controller is developed for a quadrotor. A state feedback with integral action control scheme is designed for the position controller to ensure that the quadrotor can track the reference position rapidly. The quadrotor dynamic model is established using Newton-Euler formalism taking into account various physical phenomena that can effect its dynamic behavior. NI-LabVIEW based simulation results show that the proposed controller can make the quadrotor tracks the desired trajectory quickly and smoothly with ensuring the stability for roll and pitch angle.
In Egypt, the current water quality monitoring program involves thorough physio-chemical and biological analyses. However, there is still a lack of real-time water quality data that influences the urgent decision making process. The field acquisition of such data has still been costly, lengthy and laborious. Therefore, this paper aims to present a low-cost and labor-saving Early Warning Framework (EWF) for water quality monitoring of the River Nile based on the Internet of Things (IOT). A newly developed Prototype was introduced to monitor the in-situ water quality parameters; pH, turbidity and temperature at a pilot location along the River Nile within Egypt. The same parameters were also monitored using the current state-of-the-art multi-probe EXO.Then, both sets of data measurements were sent to a real-time monitoring control center for comparison and calibration. The comparison results revealed that there is no significant difference between the two measurements according to a statistical analysis done using the Minitab 16 statistical model. The Root Mean Squared Error (RMSE) values showed that the error percentages were accepted for the three monitored parameters (0.19 for pH, 0.056 for temperature, and 0.52 for turbidity). Moreover, the overall cost of the developed Prototype including sensors, raspberry Pi and all other expenses was found to be only 197 $ as compared to 11,130 $ when using EXO. Accordingly, it could be concluded that the developed Prototype can provide a low-cost early warning system for water quality monitoring. Finally, it is strongly recommended to install developed real-time water quality monitoring stations as economic wireless hotspots at a number of strategic sites along the River Nile within Egypt.
In this paper, novel virtual instrumentation based systems for real-time collision-free path planning and tracking control of mobile robots are proposed. The developed virtual instruments are computationally simple and efficient in comparison to other approaches, which act as a new soft-computing platform to implement a biologically-inspired neural network. This neural network is topologically arranged with only local lateral connections among neurons. The dynamics of each neuron is described by a shunting equation with both excitatory and inhibitory connections. The neural network requires no off-line training or on-line learning, which is capable of planning a comfortable trajectory to the target without suffering from neither the too close nor the too far problems. LabVIEW is chosen as the software platform to build the proposed virtual instrumentation systems, as it is one of the most important industrial platforms. We take the initiative to develop the first neuro-dynamic application in LabVIEW. The developed virtual instruments could be easily used as educational and research tools for studying various robot path planning and tracking situations that could be easily understood and analyzed step by step. The effectiveness and efficiency of the developed virtual instruments are demonstrated through simulation and comparison studies.
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