A Novel Intelligent Control System Design for Water BathTemperature Control

Abstract: In this paper a neuro-fuzzy controller (NFC) for temperaturecontrol of a water bath system is proposed.A five layer neural network is used to adjust input and output parameters of membership function in a fuzzy logic controller. The hybrid learning algorithm is used for training this network. The simulation results show that the proposedcontroller has good set point tracking and disturbance rejectionproperties. Also it is robust against changes in the systemparameters. It is also superior to the conventional PID controller

Design a New Intelligent Control for a Class of Nonlinear Systems

© 2019 IEEE. This paper presents a new method based on computational intelligence for precise control of a class of nonlinear systems. In this method, the Radial Basis Function Neural Networks (RBFNN) is used to approximate the uncertain functions in the system dynamics. In addition, a constraint is considered on the input. The Backstepping method is used for improving the overall accuracy of the control process. To evaluate the performance of the proposed method, a single-link robot arm with nonlinear dynamics and input saturation constraint is investigated. The simulation results show the performance of the proposed method

A New Type-II Fuzzy System for Flexible-Joint Robot Arm Control

© 2019 IEEE. In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) based on the Interval Gaussian Type-II Fuzzy sets in the antecedent part and Gaussian Type-I Fuzzy sets as coefficients of a linear combination of the input variables in the consequent part is presented. The capability of the proposed control method to function approximation and dynamical system identification is investigated. An adaptive learning rate based on the Backpropagation method with guaranteed convergence is employed for parameter learning. Finally, the proposed method is applied to control a flexible-joint robot arm. The simulation results show the robustness and effectiveness of the proposed control method. The proposed control method is also compared with the conventional ANFIS method

A New Intelligent Dynamic Control Method for a Class of Stochastic Nonlinear Systems

This paper presents a new method for a comprehensive stabilization and backstepping control system design for a class of stochastic nonlinear systems. These types of systems are so abundant in practice that the control system designer must assume that random noise with a definite probability distribution affects the dynamics and observations of state variables. Stochastic control is intended to determine the time course of control variables so that the control target is achievable even with minimal cost. Since the mathematical equations of stochastic nonlinear systems are not always constant, not every model-based controller can be accurate. Therefore, in this paper, a type-3 fuzzy neural network is used to estimate the parameters of the backstepping control method. In the simulation, the proposed method is compared with the Type-1 fuzzy and RBFN methods. Results clearly show that the proposed method has a very good performance and can be used for any system in this class

Model Development of a Hybrid Battery–Piezoelectric Fiber System Based on a New Control Method

By increasing the application of smart wearables, their electrical energy supply has drawn great attention in the past decade. Sources such as the human body and its motion can produce electrical power as renewable energy using piezoelectric yarns. During the last decade, the development of the piezoelectric fibers used in smart clothes has increased for energy-harvesting applications. Therefore, the energy harvesting from piezoelectric yarns and saving process is an important subject. For this purpose, a new control system was developed based on the combination of the sliding mode and particle swarm optimization (PSO). Using this method, due to the piezoelectric yarn cyclic deformation process, electrical power is produced. This power is considered the input voltage to the controlling system modeled in this article. This system supplies constant voltage to be saved in a battery. The battery supplies power for the electrical elements of smart fabric structure for different applications, such as health care. It is shown that the presence of PSO led to the improvement of system response and error reduction by more than 30%

A New General Type-2 Fuzzy Predictive Scheme for PID Tuning

The proportional-integral-derivative controller is widely used in various industrial applications. But, in many noisy problems the strong methods are needed to optimize the proportional-integral-derivative parameters. In this paper, a novel method is introduced for adjusting the proportional-integral-derivative parameters through the model predictive control and generalized type-2 fuzzy-logic systems. The rules of suggested fuzzy system are online adjusted and the parameters of proportional-integral-derivative are tuned based on the fuzzy model such that a cost function to be minimized. The designed controller is applied on continuous stirred tank reactor and the performance is compared with other traditional approaches. The main advantages are that the accuracy is improved by online modeling and optimization and a predictive scheme is added to the conventional proportional-integral-derivative controller

A New Short Term Electrical Load Forecasting by Type-2 Fuzzy Neural Networks

In this study, we present a new approach for load forecasting (LF) using a recurrent fuzzy neural network (RFNN) for Kermanshah City. Imagine if there is a need for electricity in a region in the coming years, we will have to build a power plant or reinforce transmission lines, so this will be resolved if accurate forecasts are made at the right time. Furthermore, suppose that by building distributed generation plants, and predicting future consumption, we can conclude that production will be more than consumption, so we will seek to export energy to other countries and make decisions on this. In this paper, a novel combination of neural networks (NNs) and type-2 fuzzy systems (T2FSs) is used for load forecasting. Adding feedback to the fuzzy neural network can also benefit from past moments. This feedback structure is called a recurrent fuzzy neural network. In this paper, Kermanshah urban electrical load data is used. The simulation results prove the efficiency of this method for forecasting the electrical load. We found that we can accurately predict the electrical load of the city for the next day with 98% accuracy. The accuracy index is the evaluation of mean absolute percentage error (MAPE). The main contributions are: (1) Introducing a new fuzzy neural network. (2) Improving and increasing the accuracy of forecasting using the proposed fuzzy neural network. (3) Taking data from a specific area (Kermanshah City) and forecasting the electrical load for that area. (4) The ability to enter new data without calculations from the beginning

Robust and General Model to Forecast the Heat Transfer Coefficient for Flow Condensation in Multi Port Mini/Micro-Channels

A general correlation for predicting the two-phase heat transfer coefficient (HTC) during condensation inside multi-port mini/micro-channels was presented. The model was obtained by correlating the two-phase multiplier, φtp with affecting parameters using the genetic programming (GP) method. An extensive database containing 3503 experimental data samples was gathered from 21 different sources, including a broad range of operating parameters. The newly obtained correlation fits the broad range of measured data analyzed with an average absolute relative deviation (AARD) of 16.87% and estimates 84.73% of analyzed data points with a relative error of less than 30%. Evaluation of previous correlations was also conducted using the same database. They showed the AARD values ranging from 36.94% to 191.19%. However, the GP model provides more accurate results, AARD lower than 17%, by considering the surface tension effects. Finally, the effect of various operating parameters on the HTC was studied using the proposed correlation