Grey Fuzzy Control Research Articles

Novel fuzzy direct torque control based on constructed functional transformed grey model

To reduce the ripple of the magnetic flux and torque of motors and the reduce the hysteresis in motor speed control, an improved grey model predictive fuzzy direct torque control (DTC) method based on function transformation is proposed. First, a function transformation is used to transform the sampled sequences to nonnegative values. This overcomes the disadvantages caused by fluctuant and random sampling of the motor torque and stator flux linkage. Second, an equal dimensional new information model is used to keep the dimensions unchanged, which reduces the time to predict the motor parameters through the model. Moreover, the voltage space vector plane is divided into six sectors, which simplifies the fuzzy control system rules. Simulation results show that the proposed fuzzy direct torque control based on the improved grey model method reduces the influence of hysteresis on the control system, decreases the motor flux chain and torque ripple, improves the response speed of the torque and rotational speed, reduces overshoot, achieves good effects in terms of anti-interference capability and dynamic response, and improves the real-time performance and accuracy of the fuzzy control system.

Development and Experimental Analysis of a Fuzzy Grey Control System on Rapeseed Cleaning Loss

One of the most important means of improving the mechanization of rapeseed harvests and increasing farmers’ income is to reduce the cleaning loss of rapeseed. In this study, a fuzzy grey control system was developed using an assembled cleaning loss sensor. Based on experimental data, the relationship between the cleaning loss and the opening of the louver sieve in the cleaning device was obtained. The fuzzy control scheme was established by combining grey prediction and the fuzzy control principle. Secondly, a microcontroller unit (MCU) was used as the controller, and the opening of the louver sieve was automatically regulated by detecting the signal of the cleaning loss. Finally, the performance and robustness of the control system was evaluated in field tests. Different experiments were conducted under different speed conditions to reflect the variable throughput. Results showed that using the grey prediction control system can realize the adjustment of the louver sieve opening in real time. The cleaning loss could be maintained within the ideal setpoint interval, compared with the operation with the control system switched off. These findings indicate that the application of the grey fuzzy control system reduces cleaning loss, and the nonlinear, time-variable and time delay problems in cleaning devices can be solved effectively.

Active Vibration Isolation of the 6-RSS Parallel Mechanism Based on Grey-Fuzzy Control

In this paper, the 6-RSS parallel mechanism is studied to deal with multiple DOF vibration isolation problems of precision control, the active isolation vibration control is investigated. Based on the dynamics model of active isolation vibration system, a Grey Fuzzy Control (GFC) approach is proposed to improve the performance and stability of the 6-RSS parallel mechanism. It proves that GFC is a better design method in the presence of constraints. And the control algorithm utilizes the grey predictive theory to predict the system next-step output response for pre-correction. Simulation results demonstrate the feasibility of the GFC algorithm for real-time implementation.

Improved joint radio resource management usage grey fuzzy control in heterogeneous wireless networks

This paper presents an innovative approach to joint radio resource management (JRRM) over a complex heterogeneous network by using several realistic radio access technologies (RATs). This work applies a novel grey fuzzy control process to joint radio resource management (JRRM). In this architecture, the JRRM techniques are divided between grey fuzzy control (GFC) and grey fuzzy multi-attribute decision making (GFMADM). These enhancements lead to a new JRRM architecture which seeks to minimize the multi-user interference, decrease blocking, lower dropping probabilities and improve load balancing. The policy uses the signal strength, path loss, and load algorithms for admission control (AC), horizontal handoff (HHO) and vertical handover (VHO) in a heterogeneous wireless scenario which includes GSM/ EDGE radio access network (GERAN), high-speed packet access (HSPA) and long term evolution (LTE) RATs. Our simulation results show that the proposed GFC/GFMADM algorithm can decreases multi-user interference which results in the increasing system radio capacity, decreasing blocking and dropping probabilities and enhancing load balancing.

Study on the Control System of Grinding the Nozzle of Twin Flapper-Nozzle Valve

In this paper, neural network and grey fuzzy control technology are applied in Abrasive Flow Machining (AFM) to grind the mico-hole in th nozzle of the twin flapper-nozzle valve. An intelligent control system with fine tuning working pressure is established that can predict the process parameters automatically before machining and forcast the flow to adjust the working pressure in machining.The result of experiment indicates that this system has high level of intelligent and can get very high machining accuracy.

Based on Multi-Factors Grey Prediction Control for Elevator Velocity Modulation

This paper uses the double-factors grey prediction and the fuzzy controller for the elevator car speed control. We introduce double-factors grey control to predict car vibration for elevator speed during the operation. Simulation results show that based on multi-factors gray prediction fuzzy PI control for elevator velocity modulation system closer than simple gray fuzzy PI control elevator speed control system to the actual operation. The control effect of double factors grey fuzzy PI control elevator speed control system is also satisfactory. This paper uses the double-factors grey prediction and the fuzzy controller for the elevator car speed control. We exploit the merit of which not depend on the object model and strong robustness to overcome elevator PMSM parameters drift and nonlinear effects. And we introduce double-factors grey control to predict car vibration for elevator speed during the operation. Simulation results show that based on multi-factors gray prediction fuzzy PI control for elevator velocity modulation system closer than simple gray fuzzy PI control elevator speed control system to the actual operation. The control effect of double factors grey fuzzy PI control elevator speed control system is also satisfactory. DOI: http://dx.doi.org/10.11591/telkomnika.v10i6.1408 Full Text: PDF

Based on Grey Prediction and Fuzzy PID Control Method

This paper fusions advantages of the grey forecast, fuzzy control and the traditional PID control, puts forward the grey prediction fuzzy PID control method. This method is based on PID Controlling Regulation determine interrelated parameters with the fuzzy reasoning, use online grey prediction theory forecast the system outputs which instead of the controlled object measurement value to exert feedback control for the system. The simulation results show that this control ways have more excellent control quality than traditional PID and Fuzzy PID.

Torque Control of Servo Load Simulator with Generalized Dynamic Fuzzy Neural Network Based on Grey Prediction

Considering the testing requirements of dynamically loaded about servo system in weapons, a load simulator is presented in this paper and the transfer function of “extraneous torque" is obtained. In order to curb the amplitude of extra torque and achieve dynamic load simulation, this paper proposes a grey prediction-based fuzzy neural network controller, which selects Generalized Dynamic Fuzzy Neural Network as the learning algorithm and selects the ε-completeness as a criterion to determine the width of Gaussian functions. Simulation and experimental results show that the proposed torque controller can significantly reduce the amplitude of the extra torque.

Grey fuzzy PI control for packing pressure during injection molding process

This study presents a grey fuzzy PI (GFPI) controller for packing pressure control during the injection process. The novel controller was designed for solving the problems of large overshoot, static error, and long delay time on servo motor-driven injection molding machines. Based on reasonable assumptions, a mathematical model of injection system was established. According to the nonlinear, severe interferences, and long delay time characteristics in injection process, this paper integrates predictive grey system, robust fuzzy ratiocination, and PI control. Using these mathematical model and control algorithms, a GFPI controller is implemented into an MCU using C programming techniques. The integral discrete PID controller and solid fuzzy controller were realized for contrast experiments. The experimental results show that GFPI controller had better performance on reducing overshoot and static error, increasing both response rate and repeatable accuracy. Such a developed technology would provide helpful references for designing the controller of energy-saving servo motor-driven injection molding equipment.

A Grey-Fuzzy Controller for Optimization Technique in Wireless Networks

A Grey-Fuzzy Controller for Optimization Technique in Wireless Networks

An adaptive grey PID-type fuzzy controller design for a non-linear liquid level system

Product-sum-type fuzzy controllers are known to have similar characteristics to PD-type controllers. In the case of type-0 control systems, PID-type fuzzy controllers have been proposed in the literature in order to eliminate the steady-state error. However, these control methods, essentially based on conventional PID theory, have no predictive capabilities. The concept of grey system theory, which has a certain prediction capability, offers an alternative approach for various kinds of conventional control methods, such as PID control and fuzzy control. This paper proposes a grey prediction-based fuzzy PID controller that can overcome the stated shortcomings. In order to obtain a better controller performance, another fuzzy controller is designed to change the step size of the grey predictor. A non-linear liquid level system is taken as a test bed. The grey model developed is examined under several different conditions and it is shown that the proposed grey fuzzy PID controller can predict the future output value of the system. It is clear that the proposed adaptive PID-type fuzzy controller is effective in controlling such a non-linear system accurately by changing the step size adaptively for real-time working.

Grey Predictive Process Control Charts

It has been stated by Pan (2002) that “modern manufacturing environments where the life cycle of products has decreased rapidly and customized short-run manufacturing process become quite common for achieving custom satisfactions”. In classical statistical control theory, it is assumed that for a stable production process if the product measurements were within control, the intrinsic variability poses no impacts to product quality. In this article, we will investigate a small-sample oriented grey predictive control scheme which is developed in terms of grey differential equation theory. This new quality control methodology does not impose the stability constraint to the production process and easy to be implemented for constant monitoring product quality. As illustrative examples, grey predictive Shewhart chart and CUSUM control chart are discussed. A grey fuzzy predictive control scheme is proposed and investigated as well. Finally, we discuss the usage of grey systematic equation system for process control predictive charts.

Optimal predicted fuzzy controller of a constant turning force system with fixed metal removal rate

The grey-fuzzy control scheme, which is a predictive fuzzy control scheme, is proposed in this paper to control the constant turning force process with a fixed metal removal rate under various cutting conditions. The grey-fuzzy control scheme consists of two parts: the grey predictor and the fuzzy logic controller. When the grey-fuzzy control scheme is used to design the constant turning force operation with a fixed metal removal rate, it is necessary to adjust the control parameters of both the grey predictor and the fuzzy controller (i.e., the sample size and grey constants of the grey predictor, and the scaling factors of the fuzzy controller) for ensuring stability and obtaining optimal control performance. Therefore, in order to search for the optimal control parameters by way of systematic reasoning instead of the time-consuming trial-and-error procedure, the Taguchi genetic method is applied in this paper to search for the optimal control parameters of both the grey predictor and the fuzzy controller such that the grey-fuzzy controller is an optimal controller. Computer simulations are performed to verify the effectiveness of the above optimal grey-fuzzy control scheme designed by the Taguchi genetic method. It is shown that satisfactory performance has been achieved by this designed optimal grey-fuzzy control scheme.

Optimal grey-fuzzy controller design for a constant turning force system

In this paper the grey-fuzzy control scheme is proposed to control the turning process with constant cutting force under various cutting conditions. The grey-fuzzy control scheme consists of two parts: the grey predictor and the fuzzy logic controller. When we use the grey-fuzzy control scheme to design the constant turning force system, it is necessary to adjust the control parameters of both the grey predictor and the fuzzy controller (i.e., the sample size and grey constants of the grey predictor, and the scaling factors of the fuzzy controller) for guaranteeing stability and obtaining optimal control performances. Therefore, in order to search for the optimal control parameters by way of systematic reasoning instead of the time-consuming trial-and-error procedure, in this paper the Taguchi method is applied to search for the optimal control parameters of both the grey predictor and the fuzzy controller such that the grey-fuzzy controller is an optimal controller. Computer simulations are performed to verify the effectiveness of the above optimal grey-fuzzy control scheme designed by the Taguchi method. It is shown that satisfactory performances have been achieved by the optimal grey-fuzzy control scheme designed in this way.

Target tracking using a hierarchical grey-fuzzy motion decision-making method

This paper presents a hierarchical grey-fuzzy motion decision-making (HGFMD) algorithm, which is capable of integrating multiple sequential data for decision making and for the design of the control kernel of the target tracking system. The algorithm combines multiple grey prediction modules, each of which can estimate a suitable model from sequential sensory information for approximating the observed dynamic system for future-trend prediction and for decision making through a multilayer fuzzy logic inference engine. We have designed the HGFMD controller for a target tracking system and implemented it in our autonomous mobile robot. The HGFMD is compared with the conventional fuzzy logic controller, multilayer fuzzy controller, and the original grey-fuzzy controller developed previously in various target-tracking experiments. We demonstrated the high reliability of the HGFMD controller and tracking system even when encountering the uncertain status of slow sensory response time and the nonlinear motion behaviors of the target.

Application of the Taguchi-genetic method to design an optimal grey-fuzzy controller of a constant turning force system

A grey-fuzzy control scheme is proposed in this paper to control a constant cutting force turning process under various cutting conditions. The grey-fuzzy control scheme consists of two parts: a grey predictor and the fuzzy logic controller. When the grey-fuzzy control scheme is used to design the constant turning force system, it is necessary to adjust the control parameters of both the grey predictor and the fuzzy controller (i.e., the sample size and grey constants of the grey predictor, and the scaling factors of the fuzzy controller) for ensuring stability and obtaining optimal control performances. Therefore, in order to search for the optimal control parameters by way of systematic reasoning instead of the time-consuming trial-and-error procedure, the Taguchi-genetic method is applied in this paper to search for the optimal control parameters of both the grey predictor and the fuzzy controller such that the grey-fuzzy controller is an optimal controller. Computer simulations are performed to verify the effectiveness of the above optimal grey-fuzzy control scheme designed by the Taguchi-genetic method. It is shown that satisfactory performance is achieved by the designed optimal grey-fuzzy control scheme.

Design and Implementation of a DSP-Based Grey-Fuzzy Controller for Induction Motor Drive

A DSP-based magnetic flux controlled induction motor drive with a grey-fuzzy controller is designed and implemented. This paper investigates the possibility of applying grey theory and fuzzy algorithm in a DSP-based induction motor controller, which requires faster and more accurate response, compared with a traditional fuzzy controller. The proposed controller is composed of two parts. The first one is a grey prediction controller, and the other is a general fuzzy logic controller. The experiments are performed by controlling the induction machines, and the whole system is executed by a digital signal processor (TMS320C30). The experimental results show that the better dynamic speed performances and torque regulation characteristics are obtained by the proposed controller.

Autonomous mobile target tracking system based on grey-fuzzy control algorithm

This paper presents a new position-based tracking system for autonomous mobile target tracking task. A grey-fuzzy controller (GFC) is developed for motion control of the tracker, in which dynamics models of the target and tracker are not required a priori. The target detection is based on the adaptive visual detector (AVD), which can online adjust the histogram model based on the change of surrounding conditions, such as light variation, in a natural environment. The AVD and GFC are integrated together for mobile target-tracking applications. There are several advantages of the integrated system, in particular: (1) it can rapidly learn the target appearance model for the detection involved with the tracking task; (2) the temporal dynamics model of the target motion can be approximated for the predictive localization of the moving target; and (3) the system can deal with the uncertain environmental conditions to ensure the tracking performance by GFC. Three mobile robots in the authors' laboratory have been used to demonstrate the success of this integrated system experimentally. They also conduct target tracking experiments, in which Chung Cheng-I tracks various moving targets. The results demonstrate the robustness and flexibility of the overall system in dealing with mobile target-tracking problems under varied natural environment conditions.

Controller Design For Constant Turning Force System With Parameter Uncertainties And Disturbances

Fuzzy control suits a system with parameter uncertainties or disturbances. Due to plural input into the fuzzy rule, there are always 49 (7 Ð 7) time-consuming linguistic rules. To save computational time, this study adapts the single input for the fuzzy rule, thus reducing the linguistic rules to merely 7. On the other hand, a grey fuzzy controller (GFC) is introduced for the control system, and used to establish a prediction model that predicts the future step error instead of the present step error to determine the next step command of the fuzzy controller. The GFC system, which integrates grey prediction model and the fuzzy set technique, is developed to control a turning process with time-varying cutting conditions. The advantages of the GFC with the single input include simplicity, which decreases the decision rules, and prediction ability, which improves to some extent the output performance of the fuzzy control system. Simulation results show that the GFC regulates the control output effectively under parameter uncertainties and disturbances.