Neuro-fuzzy Regulation Research Articles

Synthesis of the neuro-fuzzy regulator with genetic algorithm

Real-acting objects are characterized by the presence of various types of random perturbations, which significantly reduce the quality of the control process, which determines the use of modern methods of intellectual technology to solve the problem of synthesis of control systems of structurally complex dynamic objects, allowing to compensate the influence of external factors with the properties of randomness and partial uncertainty. The article considers issues of synthesis of the automatic control system of dynamic objects by applying the theory of intelligent control. In this case, a neural network based on radial-basis functions is used at each discrete interval for neuro-fuzzy approximation of the control system, allowing real-time adjustment of the regulator parameters. The radial basis function is designed to approximate functions defined in the implicit form of pattern sets. The neuro-fuzzy regulator's parameter configuration is accomplished using a genetic algorithm, enabling more efficient computation to determine the regulator's set parameters. The regulator's parameters are represented as a vector, facilitating their application to multidimensional objects. To determine the optimal tuning parameters of the neuro-fuzzy regulator, characterized by high convergence and the possibility of determining global extrema, a genetic algorithm was used. The effectiveness of the neuro-fuzzy regulator is explained by the possibility of providing quality control of the dynamic object under random perturbations and uncertainty of input data.

An ANFIS Control Approach of a Bi-Directional Buck-Boost used for a Battery Charger

In this work, a neuro-fuzzy regulator based on ANFIS is designed for the current control of a Bidirectional Buck-Boost chopper battery charger. First, a PI regulator is used in the control loop. Data from the PI regulator is extracted and then used to train ANFIS. The performances of the two PI and neuro-fuzzy commands were evaluated under the MATLAB/SIMULINK environment. According to the simulation results, it was found that the neuro-fuzzy regulator ANFIS is more effective in improving the current response by reducing the response time. In conclusion, the neuro-fuzzy control gives a better performance compared to the PI control.

DESIGN AND MODELING OF ANFIS REGULATOR TO COMPENSATE DEVIATIONS OF MOBILE PLATFORM OF PARALLEL CABLE-DRIVEN ROBOT

The article deals with a description of design and modeling of neuro-fuzzy regulator (ANFIS) which provides a compensation of deviations of mobile platform of parallel cable-driven robot. The regulator uses adjusting of lengths of unwounded cables. Evaluation of the control system has been made according to the quality criterion as an elongation of each cable comparatively to given values.

Artificial intelligence components and fuzzy regulators in entrepreneurship development

The article provides a comparative study of the possibility of entrepreneurship development based on fuzzy signals of business activity and applied elements of artificial intelligence. The principal research methods that determine the logic and practical basis of the application of fuzzy logic in entrepreneurship are highlighted. It has been determined that fuzzy modeling is effective when technological processes are too complex for analysis using generally accepted quantitative methods, or when available sources of information in the business environment are interpreted poorly, inaccurately, and indefinitely. It has been shown experimentally that fuzzy logic gives better results compared to those obtained with generally accepted algorithms for analyzing the quality of doing business. A model of a neuro-fuzzy regulator has been developed and measures for its implementation in the business environment have been proposed. A neural network model in entrepreneurial development has been formed. Studies have shown the possibility of effective use of the principles of artificial intelligence and modeling in solving problems of developing entrepreneurial potential and making business decisions under conditions of uncertainty. This ensures objective and well-grounded decision-making in solving various applied problems of business development and taking into account environmental factors. The applied tasks of supporting the adoption of entrepreneurial decisions in the conditions are formulated; uncertainty; indicating that approaches to decision-making under conditions of uncertainty based on artificial intelligence and fuzzy logic tools are universal and require appropriate careful study and adaptation to a specific applied problem in the business environment.

An Optimal Home Energy Management Paradigm With an Adaptive Neuro-Fuzzy Regulation

In the smart grid paradigm, residential consumers should participate actively in the energy exchange mechanisms by adjusting their consumption and generation. To this end, a proper home energy management system (HEMS), in addition to achieving a high level of comfort for the consumers, should handle the practical difficulties due to the uncertainty and technical limits. With this aim, in this paper, a new HEMS is proposed to carry out day-ahead management and real-time regulation. While an optimal scheduling solution based on some forecasted values of uncertain parameters is achieved for day ahead management, real-time regulation is accomplished by an adaptive neuro-fuzzy inference system, which can regulate the gaps between the forecasted and real values. Investigated case studies indicate that the proposed HEMS can find an optimal operating scenario with an acceptable success rate for real-time regulation.

Justification of the neuro-fuzzy regulation in evaporator plant control system

Justification of the neuro-fuzzy regulation in evaporator plant control system

The conceptual model of neuro-fuzzy regulation of the microclimate in the room

Abstract The purpose of this study is to develop a model of neuro-fuzzy regulation of the microclimate in the room. The proposed model consists of an artificial neural network serving to form a comfort index PMV, a fuzzy logic controller for regulating temperature and humidity in the room. The fuzzy controller takes three in-put values - the current temperature and humidity, the value of the PMV index. The fuzzy controller regulates the level of heating/cooling and dehumidification/humidification of room air. This approach makes it easy to manage these parameters through an estimate of the PMV index, which indicates the level of thermal comfort in the room. The humidity level serves to adjust the comfort index, which in some cases avoids temperature adjustments. If the PMV index obtained on the basis of the actual temperature and humidity parameters is outside optimal values, the fuzzy controller, based on the developed rule base, begins to generate a control signal for the climate control device in order to achieve the optimum level of thermal comfort. The model is developed using Matlab software.

The conceptual model of neuro-fuzzy regulation of the microclimate in the room

The purpose of this study is to develop a model of neuro-fuzzy regulation of the microclimate in the room. The proposed model consists of an artificial neural network serving to form a comfort index PMV, a fuzzy logic controller for regulating temperature and humidity in the room. This approach makes it easy to manage these parameters through an estimate of the PMV index, which indicates the level of thermal comfort in the room.

Adaptive fuzzy critic based control design for AGC of power system connected via AC/DC tie‐lines

This study presents the design of adaptive fuzzy critic based emotional learning control design for automatic generation control (AGC) of a two-area power system interconnected via parallel AC/DC tie-lines. The adaptive fuzzy critic evaluates the current system situation and provides the emotional signal so that the artificial neuro fuzzy regulator to modify its characteristic and reduce the critic stress. The adaptive fuzzy critic based emotional learning control design are implemented and the system dynamic responses are obtained considering 1% load disturbance in area-1. A comparative study of performance of proposed control, fuzzy logic and conventional integral based control is carried out and presented with and without considering the system non-linearities such as governor dead-band and generation rate constraint. The proposed control design technique has been demonstrated as a superior one as compared with other techniques used for the AGC design in the study. Furthermore, the sensitivity analysis of the proposed control is also examined by varying the system parameters over the wide range from the nominal system values.

Soft-Computing Model-Based Controllers for Increased Photovoltaic Plant Efficiencies

Improved solar cell models and control methods using synergies of soft-computing techniques are used to demonstrate increased energy efficiencies of photovoltaic (PV) power plants connected to the electricity grid via space-vector-modulated three-phase inverters. The models and control strategies are combined to form two new model-based controllers that are more accurate and resilient than existing solutions resulting in increased power production. A radial-basis-function-network (RBFN) model with a neuro-fuzzy regulator applied to a plant well characterized by the conventional solar cell model provided an estimated 1.5% increase in power production over an existing conventional model proportional integral (PI)-regulator combination. A neuro-fuzzy model with a neuro-fuzzy controller applied to a plant poorly characterized by the conventional solar cell model gave an 8.6% increase in power. An analysis of the net contributions to the increased efficiencies shows that the improved models had the most effect on power gains.

NEURO-FUZZY CONTROL OF A PH PLANT

This paper studies the control of a pH process by using a neuro fuzzy controller with gain scheduling. As the process to be controlled is highly non-linear the PI-type fuzzy controller that will be used generally is not able to control the system adequately. For this, a very simple feedforward neural network trained on-line, is put at the output of the PI-type fuzzy controller in order to calculate the gain of the controller. This neuro-fuzzy regulator has been tested in real-time on a bench plant. On-line results show that the designed control system allows the plant to operate in a range of pH values, despite perturbations and variations of the plant parameters, obtaining good performance at the desired workings points.