Intelligent Controller Design by the Artificial Intelligence Methods
Open Access
- 10 August 2020
- Vol. 20 (16), 4454
- https://doi.org/10.3390/s20164454
Abstract
With the rapid growth of sensor networks and the enormous, fast-growing volumes of data collected from these sensors, there is a question relating to the way it will be used, and not only collected and analyzed. The data from these sensors are traditionally used for controlling and influencing the states and processes. Standard controllers are available and successfully implemented. However, with the data-driven era we are facing nowadays, there is an opportunity to use controllers, which can include much information, elusive for common controllers. Our goal is to propose a design of an intelligent controller–a conventional controller, but with a non-conventional method of designing its parameters using approaches of artificial intelligence combining fuzzy and genetics methods. Intelligent adaptation of parameters of the control system is performed using data from the sensors measured in the controlled process. All parts designed are based on non-conventional methods and are verified by simulations. The identification of the system’s parameters is based on parameter optimization by means of its difference equation using genetic algorithms. The continuous monitoring of the quality control process and the design of the controller parameters are conducted using a fuzzy expert system of the Mamdani type, or the Takagi–Sugeno type. The concept of the intelligent control system is open and easily expandable.Keywords
Funding Information
- Technology Agency of the Czech Republic (TN01000024)
This publication has 26 references indexed in Scilit:
- A bio-system inspired nonline ar intelligent controller with application to bio-reactor systemNeurocomputing, 2015
- Conventional controller design based on Takagi–Sugeno fuzzy modelsJournal of Applied Logic, 2015
- Performance Analysis and Experimental Verification of Buck Converter fed DC Series Motor using Hybrid Intelligent Controller with Stability Analysis and Parameter VariationsJournal of Electrical Engineering & Technology, 2015
- An Intelligent MPPT controller based on direct neural control for partially shaded PV systemEnergy and Buildings, 2015
- A novel intelligent controller for combating stiction in pneumatic control valvesControl Engineering Practice, 2014
- Intelligent controller of a flexible hybrid robot machine for ITER assembly and maintenanceFusion Engineering and Design, 2014
- A systematic study of fuzzy PID controllers-function-based evaluation approachIEEE Transactions on Fuzzy Systems, 2001
- Intelligent multi-controller assessment using fuzzy logicFuzzy Sets and Systems, 1996
- An architecture for expert system based feedback controlAutomatica, 1989
- Expert controlAutomatica, 1986