scholarly journals Hybrid Adaptive Bionic Fuzzy Control Method

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Yan Li ◽  
Yimin Li ◽  
Faxiang Zhang
Keyword(s):  
Fuzzy Control ◽  
Fuzzy System ◽  
Control Method ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy Control ◽  
Synthesis Method ◽  
Biologically Active ◽  
Stability And Convergence ◽  
System Parameters ◽  
Parameter Constraints

The bionic fuzzy system embeds the biologically active adaptive strategy into the traditional T-S fuzzy system, which increases the active adaptability. On the basis of the researches, an identification model is added to the system, and a hybrid bionic adaptive fuzzy control method is proposed in this paper, which makes the system have biological adaptability and strong anti-interference ability. The adaptive law contains two items: the first one is the general term for adjusting system parameters by using the current state and the second one is a compensation item for the adjustment of system parameters based on the development trend. Lyapunov synthesis method is used to analyze the stability and convergence of system. The design method of fuzzy controller, adaptive laws, and parameter constraints are given. Finally, the effectiveness of the method is verified by simulation of inverted pendulum model.

scholarly journals Type-2 Combined T-S Adaptive Fuzzy Control

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yunli Hao ◽  
Shuang Li ◽  
Qing Xia ◽  
Maohua Wang
Keyword(s):  
Fuzzy Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy Control ◽  
Synthesis Method ◽  
Approximation Error ◽  
Fuzzy Model ◽  
Input Variables ◽  
Interval Type

For a class of nonlinear systems with a nonlinear relationship between input and output, a fuzzy control method combining interval type-2 and T-S fuzzy controller is proposed based on type-2 fuzzy system theory. In order to ensure its stability, anti-interference ability, and minimum approximation error, this design combines direct, indirect, supervised, and compensation control types to construct the controller. In this way, the structure of the controller not only has the characteristics of the type-2 fuzzy set, which can reduce the uncertainty of rules, but also has a T-S fuzzy model with linear combination of input variables, which can improve the modeling accuracy and reduce the number of rules of the system. By using the Lyapunov synthesis method, the global stability and the convergence of the closed-loop system under the condition that all variables are uniformly bounded are analyzed, and the adaptive laws of the system parameters are given as well. Finally, the effectiveness and superiority of the proposed method are verified by simulation.

Energy Management System Optimal Strategy for Pure Electric Vehicle Based on the Dynamic Programming

2014 ◽  
Vol 556-562 ◽  
pp. 1472-1475 ◽  
Author(s):  
Bing Dong ◽  
Yan Tao Tian ◽  
Chang Jiu Zhou
Keyword(s):  
Dynamic Programming ◽  
Fuzzy Control ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management System ◽  
Control Method ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy Control ◽  
Energy Management System ◽  
Fuzzy Control Strategy

This thesis puts forward one optimal adaptive fuzzy control method based on the pure electric vehicle energy management system of the fuzzy control which has been founded already. By adding an optimizing researching model based on the conventional fuzzy control strategy, the thesis can pick up the valuable control rules based on the dynamic programming theory and also can adjust the parameter of the fuzzy controller automatically according to the system operating. These can make the sum of the energy loss reduce to the min. The experiment points out that this method makes the vehicle possess good economic performance in the same driving cycle.

Application Research on H∞ Optimization Control of Permanent Magnet Linear Servo System Based on Adaptive Fuzzy Control Strategy

2013 ◽  
Vol 694-697 ◽  
pp. 2185-2189
Author(s):  
Xiao Ping Zhu ◽  
Xiu Ping Wang ◽  
Chun Yu Qu ◽  
Jun You Zhao
Keyword(s):  
Fuzzy Control ◽  
Control Strategy ◽  
Control Method ◽  
Fuzzy Controller ◽  
External Disturbance ◽  
Adaptive Fuzzy Control ◽  
Servo System ◽  
Robust Controller ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy

In order to against the uncertain disturbance of AC linear servo system, an H mixed sensitivity control method based on adaptive fuzzy control was putted forward in the paper. The controller is comprised of an adaptive fuzzy controller and a H robust controller, the adaptive fuzzy controller is used to approximate this ideal control law, H robust controller is designed for attenuating the approximation errors and the influence of the external disturbance. The experimental results show that this control strategy not only has a strong robustness to uncertainties of the linear system, but also has a good tracking performance, furthermore the control greatly improves the robust tracking precision of the direct drive linear servo system.

Fuzzy Control for Underactuated Manipulator

2013 ◽  
Vol 397-400 ◽  
pp. 1490-1493 ◽  
Author(s):  
Qi Xiao Xia ◽  
Yue Qing Yu ◽  
Qing Bo Liu
Keyword(s):  
Numerical Simulation ◽  
Fuzzy Control ◽  
Fuzzy System ◽  
Position Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Underactuated Manipulator ◽  
Siso Systems

Control of an underactuated manipulator by fuzzy control method is investigated in this paper. The joint angular errors are directly feedbacked to the inputs of the fuzzy system. The MIMO fuzzy controller is decoupled into sub SISO systems for simplification of the design of the system. Numerical simulation for the position control of a planar was presented for verification of the proposed method.

Cost Analysis and Fuzzy Control for Collapsible Container Usage Based on Closed-Loop Supply Chain Model

2017 ◽  
Vol 139 (8) ◽  
Author(s):  
Xinyan Ou ◽  
Jorge Arinez ◽  
Qing Chang ◽  
Guoxian Xiao
Keyword(s):  
Mathematical Model ◽  
Fuzzy Control ◽  
Closed Loop ◽  
Control Method ◽  
Fuzzy Controller ◽  
Accurate Estimation ◽  
Chain Model ◽  
Market Demand ◽  
Total Cost ◽  
Logistic Cost

In the last decade, global competition has forced manufacturers to optimize logistics. The implementation of collapsible containers provides a new perspective for logistics cost savings, since using collapsible containers reduces the frequency of shipping freight. However, optimization of logistic cost is complicated due to the interactions in a system, such as market demand, inventory, production throughput, and uncertainty. Therefore, a systematic model and accurate estimation of the total cost and system performance are of great importance for decision making. In this paper, a mathematical model is developed to describe deterministic and stochastic scenarios for a closed-loop container dynamic flow system. The uncertainties in a factory and a supplier are considered in the model. The performance evaluation of the collapsible container system and total cost estimation are provided through model analysis. Furthermore, fuzzy control method is proposed to monitor the processing rate of the supplier and the factory and to adjust the rate of the supplier operation then further reduce the logistic cost. A case study with a matlab simulation is presented to illustrate the accuracy of the mathematical model and the effectiveness of the fuzzy controller.

scholarly journals Fuzzy Control of Cold Storage Refrigeration System with Dynamic Coupling Compensation

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Xiliang Ma ◽  
Ruiqing Mao
Keyword(s):  
Adverse Effects ◽  
Fuzzy Control ◽  
Cold Storage ◽  
Control Strategy ◽  
Control Method ◽  
Fuzzy Controller ◽  
Variable Load ◽  
Variable Degree ◽  
Refrigeration System ◽  
Dynamic Coupling

Cold storage refrigeration systems possess the characteristics of multiple input and output and strong coupling, which brings challenges to the optimize control. To reduce the adverse effects of the coupling and improve the overall control performance of cold storage refrigeration systems, a control strategy with dynamic coupling compensation was studied. First, dynamic model of a cold storage refrigeration system was established based on the requirements of the control system. At the same time, the coupling between the components was studied. Second, to reduce the adverse effects of the coupling, a fuzzy controller with dynamic coupling compensation was designed. As for the fuzzy controller, a self-tuning fuzzy controller was served as the primary controller, and an adaptive neural network was adopted to compensate the dynamic coupling. Finally, the proposed control strategy was employed to the cold storage refrigeration system, and simulations were carried out in the condition of start-up, variable load, and variable degree of superheat, respectively. The simulation results verify the effectiveness of the fuzzy control method with dynamic coupling compensation.

Composite Fuzzy Control Method for Temperature in Intelligent Moisture Analyzer

2013 ◽  
Vol 401-403 ◽  
pp. 1010-1013
Author(s):  
Jing Ling ◽  
Jin Che ◽  
Da Ming Liu
Keyword(s):  
Steady State ◽  
Fuzzy Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Time Lag ◽  
Threshold Value ◽  
National Standards ◽  
Infrared Heating ◽  
Temperature Control System ◽  
State Error

Temperature control system of infrared heating oven in moisture analyzer is characteristic of nonlinear, time-varying and time-lag. A composite fuzzy control (CFC) method is proposed, which combines improved Bang-Bang control with two-stage intelligent fuzzy control. The control algorithm is implemented by MSP430F5438. When the temperature error e between the desired temperature and actual temperature in heating oven is larger than threshold value, the improved Bang-Bang controller is employed in rapidly reducing the error; to decrease the system overshoot, the basic fuzzy controller is used; to reduce the steady-state error of basic fuzzy controller, the auxiliary fuzzy controller is applied. The steady-state error of improved fuzzy controller for oven temperature is less than 0.5°C, which is better than the Chinese National Standards for moisture content measurement.

The Fuzzy Control Method in Semi-Active Suspension

2010 ◽  
Vol 159 ◽  
pp. 644-649
Author(s):  
Jing Hua Zhao ◽  
Wen Bo Zhang ◽  
He Hao
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Simulation Analysis ◽  
Reference Model ◽  
Active Suspension ◽  
Vibration Characteristic ◽  
Acceleration Signal ◽  
Logic Control

Based on the analysis of performance of vehicle and its suspension, half vehicle model of five DOF and road model were built and the dynamic equations of half vehicle were derived according to the parameters of a commercial vehicle. In addition, a novel fuzzy logic control system based on semi-active suspension was introduced to achieve the optimal vibration characteristic, with changing the adjustable dampers according to dynamic vertical body acceleration signal. The fuzzy control was designed based on non-reference model method that acceleration value was sent to the fuzzy controller directly. And then, simulation analysis of semi-active suspension with fuzzy control method were implemented on the B-class road surface. The results showed that the semi-active suspension control system introduced in this paper has better performance on vieicle vibration characteristic, compared to passive suspension.

scholarly journals Design of simultaneous input-shaping-based SIRMs fuzzy control for double-pendulum-type overhead cranes

2015 ◽  
Vol 63 (4) ◽  
pp. 887-896 ◽  
Author(s):  
D. Qian ◽  
S. Tong ◽  
B. Yang ◽  
S. Lee
Keyword(s):  
Fuzzy Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Input Shaping ◽  
Double Pendulum ◽  
Overhead Cranes ◽  
Control Command ◽  
Single Input ◽  
System Variables ◽  
Simultaneous Input

Abstract Overhead cranes are extensively employed but their performance suffers from the natural sway of payloads. Sometime, the sway exhibits double-pendulum motions. To suppress the motions, this paper investigates the design of simultaneous input-shaping-based fuzzy control for double-pendulum-type overhead cranes. The fuzzy control method is based on the single input-rule modules (SIRMs). Provided the all the system variables are measurable, the SIRMs fuzzy controller is designed at first. To improve the performance of the fuzzy controller, the simultaneous input shaper is adopted to shape the control command generated by the fuzzy controller. Compared with other two control methods, i.e., the SIRMs fuzzy control and the convolved input-shaping-based SIRMs fuzzy control, simulation results illustrate the feasibility, validity and robustness of the presented control method for the anti-swing control problem of double-pendulum-type overhead cranes.

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