Although drone appears in different applications, such as environmental inspection, agriculture or transportation, some aspects require more studies to clarify the efficient outcomes. One of them is to investigate the filtering performance such as Kalman and Complementary filters when the autonomous aerial system (AAS) handles its mission. However, it lacks the systematic research about these filters to provide the proper evaluation. Therefore, in this paper, the research topic related to AAS model to indicate the filtering effects in the agricultural application for making an alternative solution is presented. Firstly, the mathematical representation of system model is established in order to describe the dynamical performance and motion constraints. Then, the theory of filter structure is implemented to estimate the system state. The proposed design is validated in both numerical simulation and experiments. The system parameters that are monitored, include angular values of roll, pitch and yaw in three axes, motion parameters and its trajectories. By utilizing various sensing devices such as gyroscope, accelerometer and compass in real-world hardware, the experimental results could evaluate more precise and efficient design. The findings of this study are to (1) propose the model of AAS and proper filters, (2) launch the verified process and calibration, and (3) demonstrate the competitive performance among filters. From these results of our work, it could be clearly seen that the AAS plays an important role in daily applications and the related topics are still attractive.

A pneumatic artificial muscle actuator (PAM actuator), has been regarded during the recent decades as an interesting alternative to hydraulic and electric actuators because of these advantages such as high power to weight ratio, low cost, readily available and cheap power source, inherent safety and mobility assistance to humans performing tasks. However, problems with the air compressibility and the lack of damping ability have made it difficult to realize motion with high accuracy, high speed and with respect to various external inertia loads in order to realize a human-friendly therapy robot (HFTR). An intelligent phase plane switching controller, which harmonizes a phase plane switching control (PPSC) algorithm, conventional PID controller and the adaptabilities of neural network, is newly proposed in this study. In order to realize satisfactory control performance, Magneto-Rheological Brake (MRB) is equipped to the joint of the manipulator. The experiments were carried out in practical PAM manipulator and the effectiveness of the newly proposed control algorithm was demonstrated through experiments, which proved that the stability of the manipulator could be improved greatly in a high gain control without regard to the change of external inertia loads.