Article | 05-September-2013
A fuzzy control system was designed to command driving directions for a mountain agriculture robot. First, a fuzzy control system program was developed based on the scheme of the robot driving control system. Then, the core part of the system--the fuzzy controller--was designed. Finally, a system model was created and a simulation test was conducted through the application of the Fuzzy Toolbox in MATLAB and SIMULINK. The results showed that the system is effective.
Yuanjie Wang,
Fuzeng Yang,
Yu Zhou,
Guanting Pan,
Jinyi He,
Yubin Lan
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 4, 1725–1744
Article | 01-December-2014
We designed a fuzzy controller for ZigBee equipment’s transmission power adaptive adjustment. The controller is based on RSSI (Received Signal Strength Indicator). It can make a dynamic adjustment on the transmitted power according to the fuzzy control rules. The fuzzy control is suitable to solve the problem which is difficult to deal with in building system mathematical model. What’s more, the fuzzy control system has perfect performance in response speed and antijamming capability. It’s
Zhonghu Yuan,
Wenwu Hua,
Xiaowei Han
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 4, 1736–1752
Research Article | 12-December-2017
A laser diode package is mounted on an optical pickup head actuator. The laser diode package is used to sense the displacement of a moving target by self-mixing interference signals. A sliding mode based fuzzy control method is developed to achieve fast response of the optical pickup head actuator, which is driven in a focusing direction. Simulation results show the proposed method performs better than sliding-mode control. Experimental results further show that the proposed control method
M. Y. Tsai,
T. S. Liu
International Journal on Smart Sensing and Intelligent Systems, Volume 3 , ISSUE 2, 304–321
Article | 01-June-2016
The doubly salient mechanical structure and switching characteristics of switched reluctance motor (SRM) led to torque ripple, low dynamic performance and other problems when using conventional control algorithm in speed control method. In view of the fractional PID control algorithm has strong robustness and advantage of fuzzy control, and it does not depend on the precise mathematical model, the paper proposed a control algorithm based on fuzzy fractional order PID torque control algorithm
Yang Congkun,
Chen Chaobo,
Fu Yongsheng
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 2, 864–883
Article | 30-November-2018
control the oil pump accurately and stably, and is easy to cause system shock. With the development of intelligent control technology, such as fuzzy control, neural network control, and sensor technology, more and more control methods and control technology become more and more mature. This paper synthesizes the advantages of simple PID control, fuzzy control does not depend on model, and neural network control is adaptive. Fuzzy neural network PID control is used to control oil pump accurately, after
Chen Gong,
Shengquan Yang
International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 4, 63–68
research-article | 30-November-2018
elements of each of the term sets are mapped on to the domain of the corresponding linguistic variables.
Decision logic stage
Basically, the decision logic stage is similar to a rule base consisting of fuzzy control rules to decide how FLC works. This stage is constructed by expert knowledge and experiences. The rules are generated heuristically from the response of the conventional controller: 49 rules are derived for each fuzzy controller from careful analysis of the trend obtained from the
Himanshukumar R. Patel,
Vipul A. Shah
International Journal on Smart Sensing and Intelligent Systems
, Volume 12 , ISSUE 1, 1–20
Article | 03-November-2017
In recent years, the research and development of energy-saving control in air-condition system has become a hot spot with the advance of science and technology. In this paper, we proposed a fuzzy control mechanism for air-condition system, which combines the fuzzy control and multi-point sensing technology. When people feel cooler or hotter indoor, the air condition should promptly detect the temperature variance and switch the temperate between hot and cool smoothly. Therein an intelligent
Tzu-Ming Wang,
I-Ju Liao,
Jen-Chi Liao,
Tain-Wen Suen,
Wei-Tsong Lee
International Journal on Smart Sensing and Intelligent Systems, Volume 2 , ISSUE 4, 636–652
Article | 01-June-2016
Ashwani Kharola
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 2, 616–636
Article | 01-March-2015
Enhancing the safety of forklift power-assisted steering system is a problem urgently to be solved in practice. First of all, forklift power-assisted steering system model is established according to Lagrange dynamical equations, and three variable assistance characteristics curve fitted for reach trucks is designed combined with fuzzy control algorithm. Then sliding mode variable-structure control method based on motor current control is used tracking the target current and making contrast
Yan He,
Benxian Xiao
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 1, 749–765
Article | 01-September-2016
This paper introduced the self-tuning fuzzy PID controller based on particle swarm optimization which aims to gain more precise control over the position of pneumatic proportional valve barrel, where particle swarm works to optimize the membership function, fuzzy rule and PID parameter in fuzzy control. The study fruits also include online optimization of the self-tuning fuzzy PID controller parameters. Comparing to the conventional control methodology, The self-tuning fuzzy PID controller with
Zhang Haiyan,
Song Lepeng,
Dong Zhiming
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 3, 1497–1515
Research Article | 20-February-2013
fusion. The second one was control layer, composed of traffic light controller nodes in which fuzzy neural controller was nested. Traffic light controller nodes were used to accept the traffic data detected by the first layer, and fuzzy neural controller determined the signal cycle at artery, and on-line adjusted the green ratio at all directions on crossroads to accomplish traffic light control. Simulating results showed that the method is superior to the common fuzzy control, effectively reducing
Peng Xiaohong,
Mo Zhi,
Liao Riyao
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 1, 352–367
Research Article | 27-December-2017
magnetic tape used as the guide-path. Fuzzy control is proposed to eliminate two deviations for path tracking that keeps the AGV on its path. A set of optics emitters and receivers is arranged on some specific points in the AGV and the station respectively to determine the longitudinal position for material transshipment, and to coordinate the transmission operation of two equipments. The experiment for the palletized materials transshipment shows that positioning control of our AGV parking system can
Xing Wu,
Peihuang Lou,
Ke Shen,
Guangqing Peng,
Dunbing Tang
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 1, 48–71
Research Article | 27-December-2017
responses with different speed references, a kind of online tuning method is proposed to modulate the proportional coefficient of fuzzy control. Experiments indicate the validity of the proposed fuzzy controller.
Jingzhuo Shi,
Juanping Zhao,
Zhe Cao,
Yunpeng Liang,
Lan Yuan,
Bin Sun
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 1, 301–320
Article | 08-December-2019
the one-way section, two independent variable vessel flows from opposite directions are encountered, and fixed (predefined) signal plans lead to an increase in vessel delays. An appropriate solution is development of a Fuzzy Control System (FCS) for the vessel traffic control. A control algorithm is designed according to a set of linguistic rules that describes input parameters for the control strategy. The estimated and approximate input parameters are implemented in the algorithm as fuzzy sets
Vladimir BUGARSKI,
Todor BAČKALIĆ,
Željko KANOVIĆ,
Filip KULIĆ,
Miloš ZEKOVIĆ
Transport Problems, Volume 14 , ISSUE 4, 39–50