Real-time system identification of an unmanned quadcopter system using fully tuned radial basis function neural networks

In this paper, we present the performance analysis of a fully tuned neural network trained with the extended minimal resource allocating network (EMRAN) algorithm for real-time identification of a quadcopter. Radial basis function network (RBF) based on system identification can be utilised as an alternative technique for quadcopter modelling. To prevent the neurons and network parameters selection dilemma during trial and error approach, RBF with EMRAN training algorithm is proposed. This automatic tuning algorithm will implement the network growing and pruning method to add or eliminate neurons in the RBF. The EMRAN’s performance is compared with the minimal resource allocating network (MRAN) training for 1000 input-output pair untrained attitude data. The findings show that the EMRAN method generates a faster mean training time of roughly 4.16 ms for neuron size of up to 88 units compared to MRAN at 5.89 ms with a slight reduction in prediction accuracy

Taguchi optimisation of piezoelectric design for hybrid energy harvesting of GPS tracker device

Wireless Sensor Node (WSN) of Global Positioning System (GPS) has a disadvantage in terms of high-power consumption. Energy harvesting is a technique that collects unused light, kinetic, thermal, mechanical, chemical, wind, acoustic and hybrid, and then converts them into usable electrical energy. The main objective of the current work is to explore an energy harvesting system using piezoelectric and solar energy harvesters for a sustainable hybrid GPS sensor tracker. The Taguchi method was used to determine the optimum design of the piezoelectric transducer. The output of the piezoelectric harvester was measured by vibration (time), while solar power harvesting depended on light sensitivity (lux). The Self-Powered GPS device (SP-Tracker), was tested in the laboratory as well as at the site. The results showed that the piezoelectric energy harvesting system that analysed using the DOE Taguchi method, reflected the measurements of voltage and optimum power outputs. The optimum piezoelectric device design obtained is 3 cm and 1 g for distance and weight, respectively, with a maximum power output of 217 mW. On the other hand, the ideal size and weight of a piezoelectric device are 3 cm and 1 g. Between 108.7 and 312 mW of electricity will be generated by the hybrid energy harvesting device for both purposes. The following effort will be directed toward creating and constructing low-power Lora sensor nodes