Journal: Journal of Applied Research and Technology

Article Title: Online tuning of fuzzy logic controller using Kalman algorithm for conical tank system

doi: 10.1016/j.jart.2017.05.004

Figure Lengend Snippet: Fig. 11. Response of fuzzy and PID controller.

Article Snippet: A certain amount of a riori information about the system is necessary in constructing he control rules for adapting the filter parameters. . Simulink model of fuzzy and PID controller Simulink is a Matlab toolbox designed for the dynamic simuation of linear and nonlinear systems as well as continuous and iscrete-time systems.

Techniques:

Journal: Uncertain Supply Chain Management

Article Title: Robust simulation-optimization of dynamic-stochastic production/inventory control system under uncertainty using computational intelligence

doi: 10.5267/j.uscm.2020.9.002

Figure Lengend Snippet: Fig. 3. Block diagram representation of the stochastic PID controller in the P/I control system under uncertainty in demand and frustrating rate. This model aims to reduce lead time to zero. The PID gain parameters (𝑲𝒑, 𝑲𝒊, 𝑲𝒅) tuned robustly against source of variability (uncertainty).

Article Snippet: We uniformly produce random numbers for uncertain variables (demand and frustrating rates) and tune the model with MATLAB®/Simulink, PID tuning toolbox.

Techniques: Blocking Assay, Control

Journal: Micromachines

Article Title: Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control.

doi: 10.3390/mi13081264

Figure Lengend Snippet: Figure 7. Block diagram representation for the closed-loop system including the PID controller.

Article Snippet: The proposed method updates the PID controller gains based on the Simulink TM controller tuning toolbox.

Techniques: Blocking Assay