Neuro-Fuzzy-Based Control for Parallel Cascade Control
-
R. Karthikeyan
,
K. Manickavasagam
Abstract
This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptation. A combination of neural networks and fuzzy logic offers the possibility of solving tuning problems and design difficulties of fuzzy logic. Due to their complementary advantages, these two models are integrated together to form more robust learning systems, referred to as adaptive neuro-fuzzy inference system (ANFIS). The secondary controller is designed using the internal model control approach. The performance of the proposed ANFIS-based control is evaluated using different case studies and the simulated results reveal that the ANFIS control approach gives improved servo and regulatory control performances compared to the conventional proportional integral derivative controller.
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- Neuro-Fuzzy-Based Control for Parallel Cascade Control
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Articles in the same Issue
- Masthead
- Masthead
- Ionic Liquids as Green Solvents for the Extraction of Endosulfan from Aqueous Solution: A Quantum Chemical Approach
- Neuro-Fuzzy-Based Control for Parallel Cascade Control
- Modelling the Heat and Mass Transfer during Hot Pressing of Medium Density Fibreboard
- Drying of Apple Slices in Combined Heat and Power (CHP) Dryer: Comparison of Mathematical Models and Neural Networks
- First Principle Modeling and Neural Network–Based Empirical Modeling with Experimental Validation of Binary Distillation Column
- The Correlation of Activity Coefficients of Ionic Species of Aqueous Electrolytes Using a New Model
