Publication Type:

Journal Article


Chemical Product and Process Modeling, Volume 8, Number 1 (2013)



Adaptive neuro-fuzzy inference system, Cascade control systems, Combination of neural-network, Controllers, Conventional proportional integrals, Fuzzy control, Fuzzy logic, Fuzzy logic control, Internal model control, Model predictive control, Neural networks, Neurofuzzy control, Parallel cascade controls, Systematic methodology, Three term control systems


<p>This paper discusses the application of adaptive neurofuzzy 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.</p>


cited By (since 1996)0

Cite this Research Publication

Ra Karthikeyan, Manickavasagam, Kb, Tripathi, S., and Murthy, K. V. Vc, “Neuro-fuzzy-based control for parallel cascade control”, Chemical Product and Process Modeling, vol. 8, 2013.