[go: up one dir, main page]

Kumar et al., 2025 - Google Patents

Experimental Validation of ANN-Tuned PI Controller Design for Nonlinear Systems

Kumar et al., 2025

Document ID
840739943914928150
Author
Kumar S
Samanta S
Kanagalakshmi S
Publication year
Publication venue
Advances in Data-Driven Computing and Intelligent Systems: Selected Papers from ADCIS 2024, Volume 2

External Links

Snippet

This work presents an artificial neural network (ANN)-tuned proportional integral (PI) controller design algorithm to control the water level of nonlinear systems. Conventional PI controllers are working well in linear systems; it degrades the performance of nonlinear …
Continue reading at books.google.com (other versions)

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
    • G05B13/0265—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
    • G05B13/0205—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
    • 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
    • G05B11/00—Automatic controllers
    • G05B11/01—Automatic controllers electric
    • G05B11/32—Automatic controllers electric with inputs from more than one sensing element; with outputs to more than one correcting element
    • 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
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • 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
    • 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
    • G05B11/00—Automatic controllers
    • G05B11/01—Automatic controllers electric
    • G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential

Similar Documents

Publication Publication Date Title
Wang et al. Dynamic learning from neural control for strict-feedback systems with guaranteed predefined performance
CN110501909B (en) A fuzzy predictive control method with enhanced robustness based on disturbance observer
CN115202211B (en) Predictive control method and equipment for temperature stability control of roasting furnace
CN111812975A (en) A Generalized Predictive Control Method for Speed Regulation System of Pumped Storage Units Based on Fuzzy Model Identification
CN117452993A (en) Temperature control method of temperature-cycle test box
Abonyi et al. Adaptive fuzzy inference system and its application in modelling and model based control
Kusumoputro et al. System identification and control of pressure process rig system using Backpropagation Neural Networks
Han et al. Hierarchical self-organizing fuzzy control for uncertain nonlinear systems
Xu et al. Cooperative control of recurrent neural network for PID-based single phase hotplate temperature control systems
Kumar et al. Experimental Validation of ANN-Tuned PI Controller Design for Nonlinear Systems
Scott et al. Experiences with model-based controllers based on neural network process models
Sathish Kumar et al. Experimental Validation of ANN-Tuned PI Controller Design for Nonlinear Systems
Mohamed et al. A comparative analysis of two controllers for trajectory tracking control: application to a biological process
Pawar et al. MRAC and modified MRAC controller design for level process control
Essa et al. Control of hardware implementation of hydraulic servo application based on adaptive neuro fuzzy inference system
Wang et al. PID Parameter Tuning Method for Steam Generator Level Control Based on Ant Colony Algorithm
Bagua et al. Gas turbine monitoring using fuzzy control approaches: Comparison between fuzzy type 1 and 2
Yordanova et al. Design of supervisor-based adaptive process fuzzy logic control
Pham et al. Adaptive control of dynamic systems using neural networks
Jiménez-Morales et al. Optimal Tuning of a Bounded e-Modified Adaptive Control Law using a Particle Swarm Optimization algorithm
Minhat et al. Hybrid Core Power Control Using PI, Fuzzy and MPC for TRIGA Nuclear Reactor
Gupta et al. Machine learning approach based on fuzzy logic for industrial temperature regulation
Zdesar et al. A 2 DOF predictive control based on evolving fuzzy model
Abonyi et al. Adaptive Sugeno fuzzy control: A case study
Huaguang et al. Fuzzy adaptive control of nonlinear processes with feedforward compensator and its application