The aim of the present thesis is to develop a novel heuristic filter by utilizing Firefly Optimization Algorithm for state estimation of nonlinear, non-Gaussian systems. The proposed filter formulates the estimation problem as a dynamic, stochastic one. The swarm intelligence of the fireflies enables the filter to find and track the best estimation. To estimate the states of a system, the model of the system is required. Hence, an 8-DoF quadrotor with slung payload system, as a case study, is modeled by the tensor method. In this case, as a highly nonlinear system, in order not to rely on extra sensors for monitoring swing-angle, the estimation of payload states is needed. In this regard,...
The aim of the present thesis is to develop a novel heuristic filter by utilizing Firefly Optimization Algorithm for state estimation of nonlinear, non-Gaussian systems. The proposed filter formulates the estimation problem as a dynamic, stochastic one. The swarm intelligence of the fireflies enables the filter to find and track the best estimation. To estimate the states of a system, the model of the system is required. Hence, an 8-DoF quadrotor with slung payload system, as a case study, is modeled by the tensor method. In this case, as a highly nonlinear system, in order not to rely on extra sensors for monitoring swing-angle, the estimation of payload states is needed. In this regard,...