Nguyen et al., 2014 - Google Patents
Using motor speed profile and genetic algorithm to optimize the fuzzy logic controller for controlling DC servomotorNguyen et al., 2014
View PDF- Document ID
- 17678110304672076933
- Author
- Nguyen T
- Komeda T
- Publication year
- Publication venue
- International Journal of Computer Applications
External Links
Snippet
The paper describes a new proposed algorithm to automatically tune a Fuzzy Logic Controller by using motor Speed profile and Genetic Algorithm (FLCSGA algorithm) in controlling a DC Servo Motor. In the new method, the tuning process of the Fuzzy Logic …
- 230000002068 genetic 0 title abstract description 14
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
- G05B13/0275—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 fuzzy logic 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
- G05B13/0265—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
- G05B13/0265—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
- G05B13/0285—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 and fuzzy logic
-
- 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
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
-
- 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
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
-
- 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
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Lin et al. | Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive | |
Wang et al. | An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems | |
Purnama et al. | Intelligent control strategies for tuning PID of speed control of DC motor-a review | |
Ustun et al. | Modeling and control of V/f controlled induction motor using genetic-ANFIS algorithm | |
Kayacan et al. | A servo system control with time-varying and nonlinear load conditions using type-2 TSK fuzzy neural system | |
Xie et al. | Fuzzy adaptive internal model control | |
Adewuyi | DC motor speed control: A case between PID controller and fuzzy logic controller | |
Kim et al. | Robust self-learning fuzzy controller design for a class of nonlinear MIMO systems | |
Shahsadeghi et al. | A robust and simple optimal type II fuzzy sliding mode control strategy for a class of nonlinear chaotic systems | |
Nguyen et al. | Designing PSO-based PI-type fuzzy logic controllers: a typical application to load-frequency control strategy of an interconnected hydropower system | |
Ghany et al. | Fuzzy type two self-tuning technique of single neuron PID controller for brushless DC motor based on a COVID-19 optimization | |
Nguyen et al. | Using motor speed profile and genetic algorithm to optimize the fuzzy logic controller for controlling DC servomotor | |
Bhatti et al. | Genetically optimized ANFIS-based PID controller design for posture-stabilization of self-balancing-robots under depleting battery conditions | |
Abdalla | Optimal fuzzy controller: Rule base optimized generation | |
Cardoso et al. | A comparative study of a PI, neural network and fuzzy genetic approach controllers for an AC-drive | |
Ghalehpardaz et al. | Speed control of DC motor using imperialist competitive algorithm based on PI-Like FLC | |
Luan et al. | Load-following control of nuclear reactors based on Takagi-Sugeno fuzzy model | |
Gonçalves et al. | Solving economic load dispatch problem by natural computing intelligent systems | |
Bharti et al. | Design of optimized PID type fuzzy logic controller for higher order system | |
Nürnberger et al. | Neuro-fuzzy control based on the nefcon-model under matlab/simulink | |
Aydogdu et al. | Design of a Real Coded GA Based Fuzzy Controller for Speed | |
Kazemian | Intelligent fuzzy PID controller | |
Cai | Artificial Intelligence Applied to Control of DC Drives | |
Zerikat et al. | High performance speed tracking of induction motor using an Adaptive Fuzzy-Neural Network Control | |
Wahyunggoro et al. | Evaluations of fuzzy-logic-based self tuning PI controller and fuzzy-scheduled PID controller for DC servomotor |