Quadcopter UAV Modeling and Automatic Flight Control Design

The mathematical model of quadcopter-unmanned aerial vehicle （UAV） is derived by using two approaches： One is the Newton-Euler approach which is formulated using classical meehanics; and other is the Euler-Lagrange approach which describes the model in terms of kinetic （translational and rotational） and potential energy. The proposed quadcopter＇s non-linear model is incorporated with aero-dynamical forces generated by air resistance, which helps aircraft to exhibits more realistic behavior while hovering. Based on the obtained model, the suitable control strategy is developed, under which two effective flight control systems are developed. Each control system is created by cascading the proportional-derivative （PD） and T-S fuzzy controllers that are equipped with six and twelve feedback signals individually respectively to ensure better tracking, stabilization, and response. Both pro- posed flight control designs are then implemented with the quadcopter model respectively and multitudinous simulations are conducted using MATLAB/Simulink to analyze the tracking performance of the quadcopter model at various reference inputs and trajectories.

Dynamic stability enhancement of interconnected multi-source power systems using hierarchical ANFIS controller-TCSC based on multi-objective PSO

Suppression of the dynamic oscillations of tie-line power exchanges and frequency in the affected interconnected power systems due to loading-condition changes has been assigned as a prominent duty of automatic generation control(AGC). To alleviate the system oscillation resulting from such load changes, implementation of flexible AC transmission systems(FACTSs) can be considered as one of the practical and effective solutions. In this paper, a thyristor-controlled series compensator(TCSC), which is one series type of the FACTS family, is used to augment the overall dynamic performance of a multi-area multi-source interconnected power system. To this end, we have used a hierarchical adaptive neuro-fuzzy inference system controller-TCSC(HANFISC-TCSC) to abate the two important issues in multi-area interconnected power systems, i.e., low-frequency oscillations and tie-line power exchange deviations. For this purpose, a multi-objective optimization technique is inevitable. Multi-objective particle swarm optimization(MOPSO) has been chosen for this optimization problem, owing to its high performance in untangling non-linear objectives. The efficiency of the suggested HANFISC-TCSC has been precisely evaluated and compared with that of the conventional MOPSO-TCSC in two different multi-area interconnected power systems, i.e., two-area hydro-thermal-diesel and three-area hydro-thermal power systems. The simulation results obtained from both power systems have transparently certified the high performance of HANFISC-TCSC compared to the conventional MOPSO-TCSC.