Neuro-fuzzy approaches for identification and control of nonlinear systems (original) (raw)

Adaptive Identification Neuro-Fuzzy System Control for Nonlinear Dynamic Objects

2015

An adaptive identifier for neuro-fuzzy control system nonlinear dynamic object operating in conditions of uncertainty intrinsic properties and the environment. The algorithms of structural and parametric identification in real time, which is a combination of an identification algorithm coefficients of linear equations and the theory of interactive adaptation method. The hybrid model based on neural networks and fuzzy models, improves the efficiency of solving the problem of managing complex dynamic objects under uncertainty.

MODELING AND CONTROL OF NONLINEAR FUZZY AND NEURO - FUZZY SYSTEMS

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.

Neuro-fuzzy methods for nonlinear system identification

Annual Reviews in Control, 2003

Most processes in industry are characterized by nonlinear and time-varying behavior. Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neuro-fuzzy models are gradually becoming established not only in the academia but also in industrial applications. Neuro-fuzzy modeling 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 modeling methods for nonlinear system identification is given, with an emphasis on the tradeoff between accuracy and interpretability.

Neuro-fuzzy modeling and control

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

Design of a Neuro Fuzzy Controller

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.

Adaptive Fuzzy Neural Networks as identifiers of discrete-time nonlinear dynamic systems

Journal of Intelligent & Robotic Systems, 1996

An adaptive supervised learning scheme is proposed in this paper for training Fuzzy Neural Networks (FNN) to identify discrete-time nonlinear dynamical systems. The FNN constructs are neural-network-based connectionist models consisting of several layers that are used to implement the functions of a fuzzy logic system. The fuzzy rule base considered here consists of Takagi-Sugeno IF-THEN rules, where the rule outputs are realized as linear polynomials of the input components. The FNN connectionist model is functionally partitioned into three separate parts, namely, the premise part, which provides the truth values of the rule preconditional statements, the consequent part providing the rule outputs, and the defuzzification part computing the final output of the FNN construct. The proposed learning scheme is a two-stage training algorithm that performs both structure and parameter learning, simultaneously. First, the structure learning task determines the proper fuzzy input partitions and the respective precondition matching, and is carried out by means of the rule base adaptation mechanism. The rule base adaptation mechanism is a self-organizing procedure which progressively generates the proper fuzzy rule base, during training, according to the operating conditions. Having completed the structure learning stage, the parameter learning is applied using the back-propagation algorithm, with the objective to adjust the premise/consequent parameters of the FNN so that the desired input/output representation is captured to an acceptable degree of accuracy. The structure/parameter training algorithm exhibits good learning and generalization capabilities as demonstrated via a series of simulation studies. Comparisons with conventional multilayer neural networks indicate the effectiveness of the proposed scheme.

Using Neural Networks for Identification and Control of Systems

2015

The present work addresses the utilization of Artificial Neural Networks (NN) for the identification and control of systems, in special to control nonlinear dynamic systems or systems with some degree of uncertainty. Because NNs have an inherent ability to approximate functions and to adapt to changes in input and parameters, they can be used to control systems too complex for linear controllers, such as PID controllers. In the present work a mathematical basis for NN is presented, the mathematical representation of a process unit, or neuron, and how they can be put together in order to form nets that can learn from external data. In sequence, it is presented structures of inputs that can be used along with NN to model nonlinear systems. The most common configurations of input vectors for the training of NN are highlighted. Following, a method of control is presented that take advantage of NN, where a NN is used to build a predictive nonlinear controller using a model predictive con...

Comparison of Neuro-Fuzzy Structures For System Identification

In this p aper an experimental comparison of neuro-fuzzy structures, namely linguistic and zero and first order Takagi-Sugeno, is developed. The implementation of the model is conducted through the training of a neuro-fuzzy network, i.e., a neural net architecture capable of representing a fuzzy system. In the first phase the structure of the model is obtained by subtractive clustering, which allows the extraction of a set of relevant rules based on a set of representative input-output data samples. In the second phase, the model parameters are tuned via the training of a neural network. Furthermore, different fuzzy operators are compared, as well as regular and two-sided Gaussian functions.

A fuzzy-neural multi-model for nonlinear systems identification and control

Fuzzy Sets and Systems, 2008

The paper proposed to apply a hierarchical fuzzy-neural multi-model and Takagi-Sugeno (T-S) rules with recurrent neural procedural consequent part for systems identification, states estimation and adaptive control of complex nonlinear plants. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive trajectory tracking control systems design. The designed local control laws are coordinated by a fuzzy rule-based control system. The upper level defuzzyfication is performed by a recurrent neural network. The applicability of the proposed intelligent control system is confirmed by simulation examples and by a DC-motor identification and control experimental results. Two main cases of a reference and plant output fuzzyfication are considered-a two membership functions without overlapping and a three membership functions with overlapping. In both cases a good convergent results are obtained.

Neuro fuzzy modeling of control systems

2006

The analysis of the models is carried out starting from experimental data of a multivariable system MISO (Many Input Single Output). The models' implementation was made using fuzzy logic. In fuzzy logic, the cluster technique was used to decrease the number of rules to use in the identification. This technique is opposed to the conventional method which requires a considerable number of fuzzy inference rules to approach the model. In the consequence of fuzzy model, different techniques are used to implement Takagi-Sugeno type rules. By other hand, we implemented the Neuro-fuzzy modeling methods, which let represent the non-linear system and at the same time a system with some learning degree using different topologies. By comparison the goodness of each method is obtained.