This article presents a control approach to obtain the better stabilization in attitude and altitude of quad-rotor under different disturbance conditions. In the standard Quad-rotor rotor type UAV, controlling of attitude and altitude is one of the most critical tasks and appropriate controller for stabilization of UAV is essential and necessary. These two controls under various conditions of disturbances was a field of research stimulating for the researchers. The controller proposed is contingent on the PID feedback structure with Extended Kalman Filter (EKF). From Lyapunov Stability Theorem, it is proved that quad-rotor proposed altitude control system is asymptotic as well exponentially stability. Extended Kalman Filter (EKF) is used to filter out the sensors and system noises. Finally, the simulations carried out on MATLAB and the result proved the effectiveness of proposed recommended method for stabilization of attitude and altitude of quad-rotor.
Now a day's Unmanned Aerial Vehicle (UAV) systems are frequently used in many commercial applications. This makes UAV application as one of the hottest topic among the researchers. The most critical task for the researchers working on UAV is its proper and stable flight controlling under uncertainty and perturbed condition. In fixed wing UAV system altitude and attitude angles (Yaw, Pitch, Roll) play a significant role for flight stabilization. This paper presents a control algorithm for fixed-wing UAV longitude and latitude angles (Yaw, Pitch and Roll) controlling. This control algorithm is based on Proportional Integral Derivate (PID) conjunction with Extended Kalman Filter (EKF). It is also describes that how an unstable system become stable after applying proposed control technique. The proposed control method is simulated on fixedwing UAV mathematical model under perturbed and unperturbed conditions using MATLAB platform. The Simulation results shows that the proposed controller performed well and maintains the stability of an airplane and holds desired angle values under uncertainties and perturbed conditions.
This paper proposes a nature inspired, meta-heuristic optimization technique to tune a proportional-integral-derivative (PID) controller for a robotic arm exoskeleton RAX-1. The RAX-1 is a two-degrees-of-freedom (2-DOFs) upper limb rehabilitation robotic system comprising two joints to facilitate shoulder joint movements. The conventional tuning of PID controllers using Ziegler-Nichols produces large overshoots which is not desirable for rehabilitation applications. To address this issue, nature inspired algorithms have recently been proposed to improve the performance of PID controllers. In this study, a 2-DOF PID control system is optimized offline using particle swarm optimization (PSO) and artificial bee colony (ABC). To validate the effectiveness of the proposed ABC-PID method, several simulations were carried out comparing the ABC-PID controller with the PSO-PID and a classical PID controller tuned using the Zeigler-Nichols method. Various investigations, such as determining system performance with respect to maximum overshoot, rise and settling time and using maximum sensitivity function under disturbance, were carried out. The results of the investigations show that the ABC-PID is more robust and outperforms other tuning techniques, and demonstrate the effective response of the proposed technique for a robotic manipulator. Furthermore, the ABC-PID controller is implemented on the hardware setup of RAX-1 and the response during exercise showed minute overshoot with lower rise and settling times compared to PSO and Zeigler-Nichols-based controllers.
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