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 3-PRS Parallel Robot Control Based on Fuzzy-PID Controller

© 2019 IEEE. In this paper, a new Type-II Fuzzy-PID based approach is proposed to control a 3-PRS parallel robot. 3-PRS parallel robots are very common in industrial applications due to their high precision. In addition, according to the unique kinematic characteristics of these kinds of robots, they are capable of avoiding the singularity at the zenith angle during the tracking. The two inputs of the Fuzzy System (FS) are the error and its changes. The proposed controller applies the best control signal to the robot system by determining the PID coefficients. The simulation results confirm that a PID controller tuned by the Type-II Fuzzy system is an effective method to control these kinds of robots. The performance comparison of the Type-I and Type-II Fuzzy systems indicates the superiority of the Type-II Fuzzy system

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 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

Optimal Control of an Energy-Storage System in a Microgrid for Reducing Wind-Power Fluctuations

In conventional low-voltage grids, energy-storage devices are mainly driven by final consumers to correct peak consumption or to protect against sources of short-term breaks. With the advent of microgrids and the development of energy-storage systems, the use of this equipment has steadily increased. Distributed generations (DGs), including wind-power plants as a renewable energy source, produces vacillator power due to the nature of variable wind. Microgrids have output power fluctuations, which can cause devastating effects such as frequency fluctuations. Storage can be used to fix this problem. In this paper, a grid-connected wind turbine and a photovoltaic system are investigated considering the atmospheric conditions and wind-speed variations, and a control method is proposed. The main purpose of this paper is to optimize the capacity of energy-storage devices to eliminate power fluctuations in the microgrid. Finally, the conclusion shows that, in microgrids with supercapacitors, the optimal capacity of microgrid supercapacitors is determined. This method of control, utilizing the combined energy-storage system of the battery supercapacitor, in addition to reducing the active power volatility of the wind turbine and photovoltaic generation systems, also considers the level of battery protection and reduction in reactive-power fluctuations. In the proposed control system, the DC link in the energy-storage systems is separate from most of the work conducted, which can increase the reliability of the whole system. The simulations of the studied system are performed in a MATLAB software environment

A New Model Predictive Control Method for Buck-Boost Inverter-Based Photovoltaic Systems

This study designed a system consisting of a photovoltaic system and a DC-DC boost converter with buck-boost inverter. A multi-error method, based on model predictive control (MPC), is presented for control of the buck-boost inverter. Incremental conductivity and predictive control methods have also been used to track the maximum power of the photovoltaic system. Due to the fact that inverters are in the category of systems with fast dynamics, in this method, by first determining the system state space and its discrete time model, a switching algorithm is proposed to reduce the larger error for the converter. By using this control method, in addition to reducing the total harmonic distortion (THD), the inverter voltage reaches the set reference value at a high speed. To evaluate the performance of the proposed method, the dynamic performance of the converter at the reference voltage given to the system was investigated. The results of system performance in SIMULINK environment were simulated and analyzed by MATLAB software. According to the simulation results, we can point out the advantage of this system in following the reference signal with high speed and accuracy