parameters estimation

In this paper, the process of designing and construction of three-drive laboratory Drone system with cold gas thrusters as well as modeling and estimation of its model parameters are presented. These three thrusters are designed with an angle difference of 120 degrees to control the pitch and roll angles. According to the structure of the designed platform, a three-inputs-two-outputs linear model is considered for modeling the mentioned system, and all connections between inputs and outputs are second-order linear models. The parameters of the model have been extracted based on the collection of laboratory data and the use of genetic algorithm. The results obtained from the simulation based on the extracted model and the comparison with the results of the practical tests indicate that a suitable modeling and estimation of the system has been provided. The results show that there is 16% error for modeling and estimation around the roll axis and 19% around the vertical axis, so the estimation around the roll axis is better.

In this paper, an electrical equivalent circuit model based on the photovoltaic effect has been presented with the studies done on the simulation of the solar energy system. This linear model, which consists of two diodes, shows the behavior of a solar cell to produce electricity. We have done the desired simulations using MATLAB software. Our goal in this research is to calculate the minimum error value for the unknown parameters of the circuit, which is obtained by the root-mean-square error (RMSE). In order to accurately and reliably determine the parameters of the double-diode model, an optimization method based on collective intelligence called the Cuckoo search algorithm is presented in this article. According to the desired model that we intend to study with the proposed algorithm, to obtain the minimum error value, we calculate the unknown parameters of the circuit and compare them with other methods. The results show that the RMSE value of the proposed algorithm with the initial population value of 50 and the number of iteration rounds of 1000 is equal to 3.56*10-2, which provides better results than other algorithms. The average execution time of this algorithm is 15.81 milliseconds per every iteration round.