Moradi et al., 2022 - Google Patents
Intelligent fuzzy controller design: Disturbance rejection casesMoradi et al., 2022
- Document ID
- 8603980208833068692
- Author
- Moradi M
- Seyedtabaii S
- Publication year
- Publication venue
- Applied Soft Computing
External Links
Snippet
In this paper, a modified fuzzy gain scheduling (GS) Proportional–Integral–Derivative (PID) controller where the output of the integral term of PID, ui, heuristically used as the scheduling variable is discussed. Then, it is demonstrated that ui can effectively represent …
- 230000004044 response 0 abstract description 19
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
- G05B13/024—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Haji et al. | Fractional order fuzzy-PID control of a combined cycle power plant using Particle Swarm Optimization algorithm with an improved dynamic parameters selection | |
| US20150227121A1 (en) | Method for controlling and/or regulating a technical system in a computer-assisted manner | |
| Sambariya et al. | A robust PID controller for load frequency control of single area re-heat thermal power plant using elephant herding optimization techniques | |
| Saha et al. | Automatic generation control of a multi‐area CCGT‐thermal power system using stochastic search optimised integral minus proportional derivative controller under restructured environment | |
| Elhosseini et al. | Modeling and control of an interconnected combined cycle gas turbine using fuzzy and ANFIS controllers | |
| Chang-Chien et al. | Online estimation of system parameters for artificial intelligence applications to load frequency control | |
| Mohanty et al. | A hybrid chemical reaction-particle swarm optimisation technique for automatic generation control | |
| Moradi et al. | Intelligent fuzzy controller design: Disturbance rejection cases | |
| Mohamed et al. | Comparative study between three modeling approaches for a gas turbine power generation system | |
| Arastou et al. | Modeling and Parameter Estimation of a Steam Power Plant Including Condenser Back-Pressure Uncertainty Using Operational Data. | |
| Saez et al. | Fuzzy predictive supervisory control based on genetic algorithms for gas turbines of combined cycle power plants | |
| Burgasi et al. | Fuzzy and PID controllers performance analysis for a combined-cycle thermal power plant | |
| MUSTAFA et al. | A neuro-fuzzy controller for grid-connected heavy-duty gas turbine power plants | |
| Shete et al. | Design of a fuzzy modified model reference adaptive controller for a gas turbine rotor speed control using TS fuzzy mechanism | |
| Muthukumar et al. | Mayfly algorithm-based PID controller for LFC of multi-sources single area power system | |
| Saravanan et al. | Load frequency control for enhanced power system stability and reliability using hybrid RSA–HBA technique | |
| Kumar et al. | Small signal modelling of gas turbine plant for load frequency control | |
| Rubio et al. | Control of two electrical plants | |
| Shouran et al. | A novel fuzzy PIDF enhancing PIDF controller tuned in two stages by TLBO and PSO algorithms for reliable AVR performance | |
| Khaladkar et al. | Particle swarm optimization based PI controller for two area interconnected power system | |
| Ahamed et al. | Reinforcement learning controllers for automatic generation control in power systems having reheat units with GRC and dead-band | |
| Buragohain et al. | Load frequency control of a single area system using fuzzy logic controller and comparison with integral and PID controller | |
| Ghany et al. | Design of Fuzzy PID Load Frequency Controller Tuned by Relative Rate Observer for the Egyptian Power System | |
| Meseret et al. | Agc of a Multi‐Area Hydrothermal Interconnected Diverse Deregulated Power System Using a Proposed Novel SBOA‐Multi‐Level Fuzzy Logic‐Tilt Controller Incorporated With IPFC | |
| Mohamed Mustafa | Simple cycle gas turbine dynamic analysis using fuzzy gain scheduled PID controller |