Fuzzy modeling and control of multilayer incinerator
Fuzzy Sets and Systems - Special issue: Dedicated to the memory of Richard E. Bellman
Model predictive control: theory and practice—a survey
Automatica (Journal of IFAC)
A course in fuzzy systems and control
A course in fuzzy systems and control
Fuzzy PID controller: Design, performance evaluation, and stability analysis
Information Sciences: an International Journal - Special issue analytical theory of fuzzy control with applications
Predictive fuzzy PID control: theory, design and simulation
Information Sciences: an International Journal
Analytical structure and stability analysis of a fuzzy PID controller
Applied Soft Computing
Analysis of direct action fuzzy PID controller structures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Online elicitation of Mamdani-type fuzzy rules via TSK-based generalized predictive control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of complex systems based on neural and Takagi-Sugeno fuzzy model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An improved robust fuzzy-PID controller with optimal fuzzy reasoning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Design of a hybrid fuzzy logic proportional plus conventional integral-derivative controller
IEEE Transactions on Fuzzy Systems
Fuzzy model predictive control
IEEE Transactions on Fuzzy Systems
A systematic study of fuzzy PID controllers-function-based evaluation approach
IEEE Transactions on Fuzzy Systems
Effective optimization for fuzzy model predictive control
IEEE Transactions on Fuzzy Systems
Fuzzy predictive control of a solar power plant
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Multifeedback-Layer Neural Network
IEEE Transactions on Neural Networks
Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems
IEEE Transactions on Neural Networks - Part 2
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In this paper, a novel multivariable predictive fuzzy-proportional-integral-derivative (F-PID) control system is developed by incorporating the fuzzy and PID control approaches into the predictive control framework. The developed control system has two main units referred as adaptation and application parts. The adaptation part consists of a F-PID controller and a fuzzy predictor. The incremental control actions are generated by the F-PID controller. The controller parameters are adjusted with the predictive control approach. The fuzzy predictor provides the multi-step ahead predictions of the plant outputs. Therefore, the F-PID controller parameters are adjusted by minimizing the errors between the predicted plant outputs and reference trajectories over the prediction horizon. The fuzzy predictor is trained with an on-line training procedure in order to adapt the changes in the plant dynamics and improve the prediction accuracy. The Levenberg-Marquardt (LM) optimization method with a trust region approach is used to adjust both the controller and predictor fuzzy systems parameters. In the application part, an identical F-PID controller of the adaptation part is used to control the actual plant. The adjusted parameter values are transferred to this identical controller at each time step. The performance of the proposed control system is tested for both single-input single-output (SISO) and multiple-input multiple-output (MIMO) nonlinear control problems. The adaptation, robustness to noise, disturbance rejection properties together with the tracking performances are examined in the simulations.