Fuzzy controller theory: limit theorems for linear fuzzy control rules
Automatica (Journal of IFAC)
Tuning of PID controllers based on gain and phase margin specifications
Automatica (Journal of IFAC)
Design of PI controllers based on non-convex optimization
Automatica (Journal of IFAC)
Feedback Systems: Input-Output Properties
Feedback Systems: Input-Output Properties
Tuning and analysis of a fuzzy PI controller based on gain and phase margins
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Design of a hybrid fuzzy logic proportional plus conventional integral-derivative controller
IEEE Transactions on Fuzzy Systems
A multiregion fuzzy logic controller for nonlinear process control
IEEE Transactions on Fuzzy Systems
New design and stability analysis of fuzzy proportional-derivative control systems
IEEE Transactions on Fuzzy Systems
A new fuzzy logic learning control scheme for repetitive trajectory tracking problems
Fuzzy Sets and Systems - Theme: Fuzzy control
A Fuzzy Approach for the Network Congestion Problem
ICCS '02 Proceedings of the International Conference on Computational Science-Part I
Fuzzy PID Control of a Five DOF Robot Arm
Journal of Intelligent and Robotic Systems
Analytical structure and stability analysis of a fuzzy PID controller
Applied Soft Computing
Parallel distributed fuzzy PID control of hydro turbine generator
FS'08 Proceedings of the 9th WSEAS International Conference on Fuzzy Systems
Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system
Expert Systems with Applications: An International Journal
An error-based on-line rule weight adjustment method for fuzzy PID controllers
Expert Systems with Applications: An International Journal
Hi-index | 22.15 |
In this paper, a parallel structure of fuzzy PID control systems is proposed. It is associated with a new tuning method which, based on gain margin and phase margin specifications, determines the parameters of the fuzzy PID controller. In comparison with conventional PID controllers, the proposed fuzzy PID controller shows higher control gains when system states are away from equilibrium and, at the same time, retains a lower profile of control signals. Consequently, better control performance is achieved. With the proposed formula, the weighting factors of a fuzzy logic controller can be systematically selected according to the plant under control. By virtue of using the simplest structure of fuzzy logic control, the stability of the nonlinear control system can be analyzed and a sufficient BIBO stability condition is given. The superior performance of the proposed controller is demonstrated through an experimental example.