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In this paper, an online self-tuning precompensation for a Proportional-Integral-Derivative (PID) controller is proposed to control heading direction of a flying robot. The flying robot is a highly nonlinear plant, it is a modified X-Cell 60 radio-controlled helicopter. Heading direction is controlled to evaluate efficiency of the proposed precompensation algorithm. The heading control is based on the conventional PID control combined with an online self-tuning precompensation so that both the desired transient and steady state responses can be achieved. The precompensation is applied to compensate unsatisfied performances of the conventional PID controller by adjusting reference command. The precompensator is based on Takagi-Sugeno's type fuzzy model, which learns to tune itself online. The main contribution of the proposed controller is to enhance the controlled performance of the conventional PID controller by adding a self-tuning precompensator on the existing conventional PID controller. The results show that the conventional PID controller with an online self-tuning precompensation has a superior performance than the conventional PID controller. In addition, the online self-tuning precompensation algorithm is implemented simply by adding the precompensator to the existing conventional PID controller and letting the self-tuning mechanism tune itself online.

This paper detail the developments of a flying robot based on a radio controlled helicopter. The designed goal is to have a flying robot to maintain its attitude and heading that are commanded from the operator. The focus of this paper is on the development of a simple multi-loop SlSO attitude and heading controllers for control the highly nonlinear plant such as this small-sized helicopter for closed loop system identification in the future. The flight experiments are also presented at the end of this paper to show the results of the control algorithm.

In this paper, an online self-tuning precompensation for a Proportional-Integral-Derivative (PID) controller i s proposed to control heading direction of a flying robot, The flying robot is a highty nonlinear plant, it is a modified X-Cell 60 radio-controlled helicopter. Heading direction is controlled to evaluate efficiency of the proposed precompensation algorithm. The heading control is based on the conventional PID control combined with an online sefftuning precompensation so that both the desired transient and steady state responses can be achieved. The precompensation is applied to compensate unsatisfied performances of the conventional PID controller by adjusting reference command of the conventional PID controller. The precompensator is based on Takagi-Sugeno's type fuzzy model, which learns to tune itself online. The main contribution of the proposed controller is to enhance the controiled performance of the conventional PID controller by adding a self-tuning precompensator on the existing conventional PID controller. The results show that the conventional PID controller with an online self-tuning precompensation has a superior performance than the conventional PID controller. h addition, the online self-tuning precompensation algorithm is implemented simply by adding the precompensator to the existing conventiona1 PID controller and letting the self-tuning mechanism tune itself online.

This paper presents a computationally efficient wind estimation method which can be run onboard a miniature Unmanned Aerial Vehicle. Estimated wind can be used to calculate parachute deploy position during automated parachute landing phase. The method involves flying in an orbit at drop zone for one round. We formulate wind estimation problem into a linear least square minimization problem. Structure of the problem allows all calculations to be done onboard a microcontroller. Simulation and flight test results are also discussed.