Tuning of PID controllers with fuzzy logic

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Tuning and Assessment of Proportional–Integral–Derivative Controller for an Automatic Voltage Regulator System Employing Local Unimodal Sampling Algorithm (2014, methodology): “of PID controllers, such as Ziegler–Nichols tuning [2], the genetic algorithm (GA) [3], neural networks [4], fuzzy-based approaches [5], particle swarm optimization (PSO) [6], multiobjective optimization [7-8], differential evolution (DE) [9, 10], and the artificial bee colony (ABC) [11].”
Machine Learning Control – Taming Nonlinear Dynamics and Turbulence (2016, methodology): “For example, GA may be used to tune the gains of a proportional-integral-derivative (PID) control law [270].”
A particle swarm optimization approach for optimum design of PID controller in AVR system (2004, methodology): “Numerous control methods such as adaptive control, neural control, and fuzzy control have been studied [1]–[5].”
A review of PID control, tuning methods and applications (2020, background): “In beginning of the 20 th century G. Mann in [45], Visi-oli and Antonio in [46] proposed new PID tuning rules such as time-domain based design analysis and fuzzy logic tuning rules respectively.”