A Fuzzy Approach for the Network Congestion Problem
ICCS '02 Proceedings of the International Conference on Computational Science-Part I
Hybrid fuzzy proportional-integral plus conventional derivative control of robotics systems
Autonomous robotic systems
Analytical structure and stability analysis of a fuzzy PID controller
Applied Soft Computing
Design, Simulation and Implementation of a Fuzzy-PID Controller for Controlling a DC-DC Converter
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Artificial intelligence applications in Permanent Magnet Brushless DC motor drives
Artificial Intelligence Review
Parallel genetic algorithms for the tuning of a fuzzy AQM controller
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartI
Brief Parallel structure and tuning of a fuzzy PID controller
Automatica (Journal of IFAC)
A multivariable predictive fuzzy PID control system
Applied Soft Computing
Performance analysis of fractional order fuzzy PID controllers applied to a robotic manipulator
Expert Systems with Applications: An International Journal
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Presents approaches to the design of a hybrid fuzzy logic proportional plus conventional integral-derivative (fuzzy P+ID) controller in an incremental form. This controller is constructed by using an incremental fuzzy logic controller in place of the proportional term in a conventional PID controller, By using the bounded-input/bounded-output “small gain theorem”, the sufficient condition for stability of this controller is derived. Based on the condition, we modify the Ziegler and Nichols' approach to design the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. When a plant can be described by any modeling method, the fuzzy P+ID controller can be determined by an optimization technique. Finally, this controller is used to control a nonlinear system. Numerical simulation results demonstrate the effectiveness of the fuzzy P+ID controller in comparison with the conventional PID controller, especially when the controlled object operates under uncertainty or in the presence of a disturbance