Conventional Fuzzy Control Method Research Articles

Robust Adaptive Fuzzy Control Using Genetic Algorithm for Dynamic Positioning System

This paper aims to develop a genetic algorithm to adjust an fuzzy controller for Vessels’ Dynamic Positioning System (Vessels’ DPs). It is well-known that nonlinearities affecting the control accuracy of the DPs are related to the arrangement of different types of thrusters in the vessels, such as azimuth thrusters (electric, L-drive, and Z-drive), bow thrusters, stern thrusters, water jets, and propulsion propellers with rudders. Compared with the traditional fuzzy control methods, the proposed Robust Adaptive Fuzzy Control using Genetic Algorithm (RAFC-GA) not only overcomes the influence of nonlinearities in the DPs, but also eliminates the impact of parameter uncertainties. Therefore, the tracking performance is excellent and robustness is maintained. The RAFC-GA control method is superior to the conventional fuzzy control methods in the two following aspects: 1) to find the optimal values for the fuzzy structure parameters to satisfy the robust condition under the effect of disturbances and nonlinearities in the DPs without weakening the output tracking performance and robustness, 2) to improve the quality of the system by optimizing values for the fuzzy structure using genetic algorithm which dynamically adjusted the coverage domain width and the overlap degree of membership functions. Simulation results of the RAFC-GA are evaluated in comparison with other methods. The RAFC-GA performs the desired transient response of DPs better than others in three case studies, which proves the effectiveness of the proposed solution.

Variable Universe Fuzzy Control for Battery Equalization

In order to avoid the overcharge and overdischarge damages, and to improve the lifetime of the lithium-ion batteries, it is essential to keep the cell voltages in a battery pack at the same level, i.e., battery equalization. Based on the bi-directional modified Cuk converter, variable universe fuzzy controllers are proposed to adaptively maintain equalizing currents between cells of a serially connected battery pack in varying conditions. The inputs to the fuzzy controller are the voltage differences and the average voltages of adjacent cell pairs. A large voltage difference requires large equalizing current while adjacent cells both with low/high voltages can only stand small discharge/charge currents. Compared with the conventional fuzzy control method, the proposed method differs in that the universe can shrink or expand as the effects of the input changes. This is important as the input may change in a small range. Simulation results demonstrate that the proposed variable universe fuzzy control method has fast equalization speed and good adaptiveness for varying conditions.

Online learning fuzzy vibration control of smart truss structure

This paper deals with the active vibration control of smart truss structure. First, the electro-mechanical coupled dynamic model of the smart structure is constructed. Then, the first-order ordinary differential equation of the control system is presented. After that, an online learning fuzzy control (OLFC) algorithm is proposed to control the structure vibrations. The OLFC algorithm is composed of a reward function, a Q learning algorithm, a rule base generator and a conventional fuzzy controller. The OLFC algorithm learns the rule base by interaction with the plant, and changes rule base generate policy via evaluative reward signal to realize the learning goal. The algorithm only needs little information about the plant to design the reward function. In order to prove the effectiveness of the proposed control algorithm, control responses are presented and compared with conventional fuzzy control method.

A Mobile Robot Path Tracking Method Based on Fuzzy Logic and Artificial Emotion

Design a subsection fuzzy controller for autonomous mobile robots based on location sensors. First section the controller produced some fuzzy output and part of the output was used for setting up an emotion factor called E. In the second section, E has an effect on the controller, and the mobile robot can move more like a car being driven by human. As the fuzzy controller is down to subsections, each of the section has a simpler fuzzy rule table. The simulation experiment data showed that this kind of control method for path tracking could fit the path better with short response time and strong robustness. It was more flexible and effective on processing different corners during tracking compared to conventional fuzzy control method.

Neuro-fuzzy control of underwater vehicle-manipulator systems

This paper presents an intelligent controller for underwater vehicle-manipulator systems (UVMS) based on the neuro-fuzzy approach. The controller is composed of fuzzy PD control with membership function tuning by linguistic hedge. A neural network compensator approximates the dynamics of the UVMS in decentralized form. The new controller has the advantages of simplicity of implementation due to decentralized design, precision, and robustness to payload variations and hydrodynamic disturbances. It has significantly low energy consumption compared to both the conventional PD and conventional fuzzy control methods. The effectiveness of the proposed controller is illustrated by results of simulations for a six degrees of freedom autonomous underwater vehicle with a three degrees of freedom on-board manipulator.

GA-based fuzzy controller design for tunnel ventilation systems

The main purpose of a tunnel ventilation system is to maintain CO pollutant concentration and visibility index (VI) under an adequate level to provide drivers with a comfortable and safe driving environment. Moreover, it is necessary to minimize power consumption used to operate the ventilation system. To achieve the objectives, fuzzy control (FLC) methods have been usually utilized due to the complex and nonlinear behavior of the system. The membership functions of the FLC consist of the inputs such as the pollutant level inside the tunnel, the pollutant emitted from passing vehicles, and the output such as the number of running jet-fans. Conventional fuzzy control methods rely on simple experiences and trial and error methods. In this paper, the FLC was optimally redesigned using the genetic algorithm (GA), which is a stochastic global search method. In the process of constructing the objective function of GA, two objectives listed above were included: maintaining an adequate level of the pollutants and minimizing power consumption. The results of extensive simulations performed with real data collected from existing tunnel ventilation system are provided in this paper. It was demonstrated that with the developed controller, the pollutant level inside the tunnel was well maintained near the allowable limit and the energy efficiency was improved compared to conventional control schemes.

Fuzzy Control Using Scaling Factors Suitable for Control Areas.

In the conventional fuzzy control method, as the value of a scaling factor in the consequent part is the same in all control areas, improvement of the nonlinear input/output characteristics of a fuzzy controller is limited thus, its control performance cannot be improved markedly. Therefore, a new fuzzy control method, which can markedly improve the control performance compared with the conventional fuzzy control method, is proposed in this paper. In the proposed fuzzy control method, the control performance is improved because the value of the scaling factor in the consequent part is tuned dynamically according to the control area by using the fuzzy inference method. Then, on the basis of the simulation results, in which the first- and second-order lag systems with dead time are set as the controlled system, the effectiveness of the proposed fuzzy control method in improving the control performance is compared with the conventional fuzzy control method.