A neuro-fuzzy approach to hybrid intelligent control

Classical control theory is based on the mathematical models that describe the physical plant under consideration. The essence of fuzzy control is to build a model of human expert who is capable of controlling the plant without thinking in terms of mathematical model. The transformation of expert's knowledge in terms of control rules to fuzzy frame work has not been formalized and arbitrary choices concerning, for example, the shape of membership functions have to be made.

The foremost objective of operative control for the unpredictable system is the design of proper and appropriate control system. The handling of insufficient information by using modern methods is of great importance. Therefore, this paper proposes the design of Type-2 fuzzy sets to deal with uncertainties in the unpredictable system in better and appropriate way as Type-2 fuzzy sets possess the capability of providing extra parameters and degree of freedom. Moreover, the construction of Adaptive Neuro-Fuzzy Type-2 (ANFT2) having a basic fuzzy set of rules is demonstrated. Gradient descent methodology is the basic method for parameter updating rules. The proposed scheme is experienced for both control and identification purpose through a commonly used dynamic system. It is observed that the projected ANFT2 structure gives better outcomes as compared to other control and identification techniques.

1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228)

Neural Networks (NN), Genetic Algorithms (GA), and Genetic Programs (GP) are often augmented with fuzzy logic-based schemes to enhance artificial intelligence of a given system. Such hybrid combinations are expected to exhibit added intelligence, adaptation, and learning ability. In this paper, implementation of three hybrid fuzzy controllers are discussed and verified by experimental results. These hybrid controllers consist of a hierarchical NN-fuzzy controller applied to a direct drive motor, a GA-fuzzy hierarchical controller applied to a flexible robot link, and a GP-fuzzy behavior-based controller applied to a mobile robot navigation task. It is experimentally shown that all three architectures are capable of significantly improving the system response.

1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century, 1995

Proceedings of The IEEE, 1995

Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called adaptive-network-based fuzzy inference system (ANFIS), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed

Automatic Control and Computer Sciences, 2018

The paper deals with the control of complex dynamic systems. The main objective is to partition the whole operational system domain in local regions using an incremental neuro-fuzzy classifier in order to achieve multiple neural control strategies for the considered system. In our case, this approach is applied to a greenhouse operating during one day. Therefore, banks of neural controllers and direct neural local models are made from different partitioned greenhouse behaviors and two multiple neural control strategies are proposed to control the greenhouse. The selection of the suitable controller is accomplished by computing the minimal output error between desired and direct neural local models outputs in the case of the first control strategy and from a supervisor block containing the considered neuro-fuzzy classifier in the case of the second control strategy. Simulation results are then carried out to show the efficiency of the two control strategies.

Integration of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantages of a combination of ANN and FIS are obvious. There are several approaches to integrate ANN and FIS and very often it depends on the application. Neuro-fuzzy modelling can be regarded as a gray-box technique on the boundary between neural networks and qualitative fuzzy models. The tools for building neuro-fuzzy models are based on combinations of algorithms from the fields of neural networks, pattern recognition and regression analysis. In this paper, an overview of neuro-fuzzy modelling given.

Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. The fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic cont rol strategy. The ability of fuzzy logic to handle imprecise and inconsistent real - world problems has made it suitable for a wide variety of applications. The present paper is concerned with modeling and control of nonlinear systems using fuzzy and neuro - f uzzy techniques. Design of controllers using conventional methods for nonlinear systems is difficult due to absence of a system atic theory behind it. In such cases, an approach based on the use of neural network for identifying the requirements o f the cont roller and the system from the input output data have been shown to be attractive. But identification using a neuro - fuzzy approach will help in reducing the arbitrariness in the choice of the type pf membership functions a nd the ranges of variables in the universe of discourse. This paper presents two methods based on fuzzy logic for the control of nonlinear systems, one using PID like fuzzy control and another using a neuro - fuzzy approach.

Lecture Notes in Computer Science, 2001

In this paper, we present a novel approach to achieve global adaptation in neuro-fuzzy controllers. The adaptation process is achieved by means of two auxiliary systems: the first one is responsible for adapting the consequents of the main controller's rules with the target of minimizing the error arising at the plant output. The second auxiliary system compiles real input/output data obtained from the plant, which are then used in real time taking into account, not the current state of the plant but rather the global identification performed. Simulation results show that this approach leads to an enhanced control policy avoiding overfitting.

Informatica (lithuanian Academy of Sciences), 2002

This paper analyses the control of nonlinear plant with the changing dynamics. Adaptive controllers, based on fuzzy logics, are synthesized for the control of air pressure and water level. Their satisfactory efficiency is experimentally demonstrated under different working conditions. Fuzzy controllers are compared to conventional PI and PID controllers.