Rapidly Tuning the PID Controller Based on the Regional Surrogate Model Technique in the UAV Formation.The leader-follower structure is widely used in unmanned aerial vehicle formation.  This paper adopts the proportional-integral-derivative (PID) and the linear quadratic regulator controllers to construct the leader-follower formation.  Tuning the PID controllers is generally empirical; hence, various surrogate models have been introduced to identify more refined parameters with relatively lower cost.  Compared with the global surrogate models, the RSMT reduces the run time of tuning PID controllers by one order of magnitude, and it improves the accuracy of surrogate models by dozens of orders of magnitude.
PID
This paper adopts the proportional-integral-derivative (PID) and the linear quadratic regulator controllers to construct the leader-follower formation. 
linear quadratic regulator
Rapidly Tuning the PID Controller Based on the Regional Surrogate Model Technique in the UAV Formation.The leader-follower structure is widely used in unmanned aerial vehicle formation.  Tuning the PID controllers is generally empirical; hence, various surrogate models have been introduced to identify more refined parameters with relatively lower cost.  However, the construction of surrogate models faces the problem that the singular points may affect the accuracy, such that the global surrogate models may be invalid.  Thus, to tune controllers quickly and accurately, the regional surrogate model technique (RSMT), based on analyzing the regional information entropy, is proposed.  Implementing the RSMT with various kinds of surrogate models, this study evaluates the Pareto fronts of the original simulation model and the RSMT to compare their effectiveness.  Compared with the global surrogate models, the RSMT reduces the run time of tuning PID controllers by one order of magnitude, and it improves the accuracy of surrogate models by dozens of orders of magnitude.
surrogate model
Thus, to tune controllers quickly and accurately, the regional surrogate model technique (RSMT), based on analyzing the regional information entropy, is proposed. 
regional surrogate model technique (RSMT)
The proposed RSMT cooperates only with the successful samples to mitigate the effect of singular points along with a classifier screening failed samples. 
classifier