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
Application of hybrid system control method for real-time power system stabilization
Fuzzy Sets and Systems
Design and analysis of direct-action CMAC PID controller
Neurocomputing
Design of robust fuzzy neural network controller with reduced rule base
International Journal of Hybrid Intelligent Systems
Function based hybrid-fuzzy genetic controller for VSI based STATCOM
International Journal of Knowledge-based and Intelligent Engineering Systems
Design of a Fuzzy PI Controller to Guarantee Proportional Delay Differentiation on Web Servers
AAIM '07 Proceedings of the 3rd international conference on Algorithmic Aspects in Information and Management
Design of Intelligent PID Controller Based on Adaptive Genetic Algorithm and Implementation of FPGA
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Three-dimensional min--max-gravity based fuzzy PID inference analysis and tuning
Fuzzy Sets and Systems
On the equivalence of single input type fuzzy inference methods
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
On the monotonicity of fuzzy-inference methods related to T-S inference method
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Adaptive fuzzy PID temperature control system based on OPC and modbus/TCP protocol
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Survey paper: A survey on industrial applications of fuzzy control
Computers in Industry
SIRMs connected fuzzy inference method adopting emphasis and suppression
Fuzzy Sets and Systems
A multivariable predictive fuzzy PID control system
Applied Soft Computing
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A function-based evaluation approach is proposed for a systematic study of fuzzy proportional-integral-derivative (PID)-like controllers. This approach is applied for deriving process-independent design guidelines from addressing two issues: simplicity and nonlinearity. To examine the simplicity of fuzzy PID controllers, we conclude that direct-action controllers exhibit simpler design properties than gain-scheduling controllers. Then, we evaluate the inference structures of direct-action controllers in five criteria: control-action composition, input coupling, gain dependency, gain-role change, and rule/parameter growth. Three types of fuzzy PID controllers, using one-, two- and three-input inference structures, are analyzed. The results, according to the criteria, demonstrate some shortcomings in Mamdani's two-input controllers. For keeping the simplicity feature like a linear PID controller, a one-input fuzzy PID controller with "one-to-three" mapping inference engine is recommended. We discuss three evaluation approaches in a nonlinear approximation study: function-estimation-based, generalization-capability-based and nonlinearity-variation-based approximations. The study focuses on the last approach. A nonlinearity evaluation is then performed for several one-input fuzzy PID controllers based on two measures: nonlinearity variation index and linearity approximation index. Using these quantitative indices, one can make a reasonable selection of fuzzy reasoning mechanisms and membership functions without requiring any process information. From the study we observed that the Zadeh-Mamdani's "max-min-gravity" scheme produces the highest score in terms of nonlinearity variations, which is superior to other schemes, such as Mizumoto's "product-sum-gravity" and "Takagi-Sugeno-Kang" schemes