A stochastic multi-parameters divergence method for online auto-tuning of fractional order PID controllers

This paper presents a stochastic multi-parameters divergence method for online parameter optimization of fractional-order proportional–integral–derivative (PID) controllers. The method is used for auto-tuning without the need for exact mathematical plant model and it is applicable to diverse plant transfer functions. The proposed controller tuning algorithm is capable of adaptively responding to parameter fluctuations and model uncertainties in real systems. Adaptation skill enhances controller performance for real-time applications. Simulations and experimental observations are carried on a prototype helicopter model to confirm the performance improvements obtained by the online auto-tuning of fractional-order PID structure in laboratory conditions. •This paper presents a Stochastic Multi-parameters Divergence-based Optimization method for the online auto-tuning of fractional order PIDs.•The method is used for auto-tuning without the need for exact mathematical plant model and it is applicable to diverse plant transfer functions.•The proposed controller tuning algorithm is capable of adaptively responding to parameter fluctuations and model uncertainties in real systems.•This optimization basically follows the descent of an object function by consecutive sets and trials of parameters.