Online Self-Tuning Precompensation for a PID Heading Control of a Flying Robot

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.