Designing of Fuzzy Logic Controller for Liquid Level Controlling

Ashish Singh Thakur,Himmat Singh,Sulochana Wadhwani 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.6

In control system there are a number of general systems and methods which are encountered in all areas of industry and technology. There are many ways to control any system, in which fuzzy is often the very best way. The only reason is faster and cheaper. One of successful application that is used for the controlling of liquid level is fuzzy logic controller. In order to find the best design to stabilize the liquid level in this method, some factors will be considered. For this paper, the liquid level was controlled by using three rules of membership function which then extended to five rules, seven rules and nine rules for verification purpose and further improvement of the system. This paper was focused to the software part only. By doing some modification in this paper, the design will be very useful for the system relates to liquid level control that widely use in industry nowadays. For a long time, the selection and definition of the parameters of PID controller are very difficult. There must be a bad effect if you do not choose nicely parameters. To strictly limit the overshoot, the use of Fuzzy controller can achieve a great control cause. In this paper, we take the liquid level water tank, and use MATLAB to design a Fuzzy Controller. Then we analyze the control effect and compare it with the effect of PID controller. As a result of comparing, Fuzzy Logic Controller is superior to PID controller. Comparison of the control results from these two systems indicated that the Fuzzy logic controller significantly reduced overshoot and steady state error.

ANF-RBC CONTROLLER TO REGULATE POWER FLOW OF ELECTRIC PROPULSION IN ELECTRIC VEHICLES

Begam Shaik Ruksana,Loveswara Rao B.,Shobha Rani D. 한국자동차공학회 2023 International journal of automotive technology Vol.24 No.4

In this paper design and simulation of a rule-based controller explained with performance analysis by using an adaptive-neuro-fuzzy and hybrid electric energy storage system to regulate power flow of electric propulsion in EVs named as (ANF-RBC) controller. In this proposed controller a hybrid electric energy storage system (HEESS) generated reference current and proposed controller for decreasing non-linear similarity generated by the usage of HEESS. Performance analysis of proposed controller carried out under three different load conditions and comparative analysis performed with existing one linear and three non-linear controllers. For performance trac under various loaded conditions done with greater than or equal to 90 % of battery current. By observing performance results of proposed controller, reducing non-linearity generated by HEESS and smooth tracking done well than that of traditional linear and non-linear controllers used in EVs. In performance analysis by using obtained results, we can infer proposed controller 17 %, 12 %, 10 % higher than existing RFOSM control, when compared with ASMC controller the proposed controller 25 %, 17 %, 13 % greater, proposed controller performance is 29 %, 21 %, 15 % greater than that of SMC controller and in case of comparing with existing PIDC controller with that of proposed controller performance is 32 %, 26 %, 19 % greater under heavily loaded, mediumly loaded and under lightly loaded condition respectively for all linear and non-linear traditional controllers.

A Vector-Controlled PMSM Drive with a Continually On-Line Learning Hybrid Neural-Network Model-Following Speed Controller

EI-Sousy Fayez F. M. The Korean Institute of Power Electronics 2005 JOURNAL OF POWER ELECTRONICS Vol.5 No.2

A high-performance robust hybrid speed controller for a permanent-magnet synchronous motor (PMSM) drive with an on-line trained neural-network model-following controller (NNMFC) is proposed. The robust hybrid controller is a two-degrees-of-freedom (2DOF) integral plus proportional & rate feedback (I-PD) with neural-network model-following (NNMF) speed controller (2DOF I-PD NNMFC). The robust controller combines the merits of the 2DOF I-PD controller and the NNMF controller to regulate the speed of a PMSM drive. First, a systematic mathematical procedure is derived to calculate the parameters of the synchronous d-q axes PI current controllers and the 2DOF I-PD speed controller according to the required specifications for the PMSM drive system. Then, the resulting closed loop transfer function of the PMSM drive system including the current control loop is used as the reference model. In addition to the 200F I-PD controller, a neural-network model-following controller whose weights are trained on-line is designed to realize high dynamic performance in disturbance rejection and tracking characteristics. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the 2DOF I-PD speed controller output to attain robust model-following characteristics under different operating conditions regardless of parameter variations and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed 200F I-PD NNMF controller. The results confirm that the proposed 2DOF I-PO NNMF speed controller produces rapid, robust performance and accurate response to the reference model regardless of load disturbances or PMSM parameter variations.

Stability Analysis and Proposal of a Simple Form of a Fuzzy PID Controller

Lee, Byung-Kyul,Kim, In-Hwan,Kim, Jong-Hwa The Korean Society of Marine Engineering 2004 한국마린엔지니어링학회지 Vol.28 No.8

This paper suggests the simple form of a fuzzy PID controller and describes the design principle, tracking performance, stability analysis and changes of parameters of a suggested fuzzy PID controller. A fuzzy PID controller is derived from the design procedure of fuzzy control. It is well known that a fuzzy PID controller has a simple structure of the conventional PID controller but posses its self-tuning control capability and the gains of a fuzzy PID controller become nonlinear functions of the inputs. Nonlinear calculation during fuzzification, defuzzification and the fuzzy inference require more time in computation. To increase the applicability of a fuzzy PID controller to digital computer, a simple form of a fuzzy PID controller is introduced by the backward difference mapping and the analysis of the fuzzy input space. To guarantee the BIBO stability of a suggested fuzzy PID controller, ‘small gain theorem’ which proves the BIBO stability of a fuzzy PI and a fuzzy PD controller is used. After a detailed stability analysis using ‘small gain theorem’, from which a simple and practical method to decide the parameters of a fuzzy PID controller is derived. Through the computer simulations for the linear and nonlinear plants, the performance of a suggested fuzzy PID controller will be assured and the variation of the gains of a fuzzy PID controller will be investigated.

Multi-Modal Controller Usability for Smart TV Control

Jeongil Yu,Seongmin Kim,Jaeho Choe,Eui S. Jung 대한인간공학회 2013 大韓人間工學會誌 Vol.32 No.6

Objective: The objective of this study was to suggest a multi-modal controller type for Smart TV Control. Background: Recently, many issues regarding the Smart TV are arising due to the rising complexity of features in a Smart TV. One of the specific issues involves what type of controller must be utilized in order to perform regulated tasks. This study examines the ongoing trend of the controller. Method: The selected participants had experiences with the Smart TV and were 20 to 30 years of age. A pre-survey determined the first independent variable of five tasks(Live TV, Record, Share, Web, App Store). The second independent variable was the type of controllers(Conventional, Mouse, Voice-Based Remote Controllers). The dependent variables were preference, task completion time, and error rate. The experiment consist a series of three experiments. The first experiment utilized a uni-modal Controller for tasks; the second experiment utilized a dual-modal Controller, while the third experiment utilized a triple-modal Controller. Results: The first experiment revealed that the uni-modal Controller (Conventional, Voice Controller) showed the best results for the Live TV task. The second experiment revealed that the dual-modal Controller(Conventional-Voice, Conventional-Mouse combinations) showed the best results for the Share, Web, App Store tasks. The third experiment revealed that the triple-modal Controller among all the level had not effective compared with dual-modal Controller. Conclusion: In order to control simple tasks in a smart TV, our results showed that a uni-modal Controller was more effective than a dual-modal controller. However, the control of complex tasks was better suited to the dual-modal Controller. User preference for a controller differs according the Smart TV functions. For instance, there was a high user preference for the uni-Controller for simple functions while high user preference appeared for Dual-Controllers when the task was complex. Additionally, in accordance with task characteristics, there was a high user preference for the Voice Controller for channel and volume adjustment. Furthermore, there was a high user preference for the Conventional Controller for menu selection. In situations where the user had to input text, the Voice Controller had the highest preference among users while the Mouse Type, Voice Controller had the highest user preference for performing a search or selecting items on the menu. Application: The results of this study may be utilized in the design of a controller which can effectively carry out the various tasks of the Smart TV.

A Vector-Controlled PMSM Drive with a Continually On-Line Learning Hybrid Neural-Network Model-Following Speed Controller

Fayez F. M. El-Sousy 전력전자학회 2005 JOURNAL OF POWER ELECTRONICS Vol.5 No.2

A high-performance robust hybrid speed controller for a permanent-magnet synchronous motor (PMSM) drive with an on-line trained neural-network model-following controller (NNMFC) is proposed. The robust hybrid controller is a two-degrees-of-freedom (2DOF) integral plus proportional & rate feedback (I-PD) with neural-network model-following (NNMF) speed controller (2DOF I-PD NNMFC). The robust controller combines the merits of the 2DOF I-PD controller and the NNMF controller to regulate the speed of a PMSM drive. First, a systematic mathematical procedure is derived to calculate the parameters of the synchronous d-q axes PI current controllers and the 2DOF I-PD speed controller according to the required specifications for the PMSM drive system. Then, the resulting closed loop transfer function of the PMSM drive system including the current control loop is used as the reference model. In addition to the 2DOF I-PD controller, a neural-network model-following controller whose weights are trained on-line is designed to realize high dynamic performance in disturbance rejection and tracking characteristics. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the 2DOF I-PD speed controller output to attain robust model-following characteristics under different operating conditions regardless of parameter variations and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed 2DOF I-PD NNMF controller. The results confirm that the proposed 2DOF I-PD NNMF speed controller produces rapid, robust performance and accurate response to the reference model regardless of load disturbances or PMSM parameter variations.

Design of Fuzzy PD+I Controller Based on PID Controller

오세준,유희한,소명옥,이윤형 한국항해항만학회 2010 한국항해항만학회지 Vol.34 No.2

Since fuzzy controllers are nonlinear, it is more difficult to set the controller gains and to analyse the stability compared to conventional PID controllers. This paper proposes a fuzzy PD+I controller for tracking control which uses a linear fuzzy inference(product-sum-gravity) method based on a conventional linear PID controller. In this scheme the fuzzy PD+I controller works similar to the control performance as the linear PD plus I(PD+I) controller. Thus it is possible to analyse and design an fuzzy PD+I controller for given systems based on a linear fuzzy PD controller. The scaling factors tuning scheme, another topic of fuzzy controller design procedure, is also introduced in order to fine performance of the fuzzy PD+I controller. The scaling factors are adjusted by a real-coded genetic algorithm(RCGA) in off-line. The simulation results show the effectiveness of the proposed fuzzy PD+I controller for tracking control problems by comparing with the conventional PID controllers.

Gain Tuning of a Fuzzy Logic Controller Superior to PD Controllers in Motor Position Control

Young-Real Kim 한국지능시스템학회 2014 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.14 No.3

Although the fuzzy logic controller is superior to the proportional integral derivative (PID) controller in motor control, the gain tuning of the fuzzy logic controller is more complicated than that of the PID controller. Using mathematical analysis of the proportional derivative (PD) and fuzzy logic controller, this study proposed a design method of a fuzzy logic controller that has the same characteristics as the PD controller in the beginning. Then a design method of a fuzzy logic controller was proposed that has superior performance to the PD controller. This fuzzy logic controller was designed by changing the envelope of the input of the of the fuzzy logic controller to nonlinear, because the fuzzy logic controller has more degree of freedom to select the control gain than the PD controller. By designing the fuzzy logic controller using the proposed method, it simplified the design of fuzzy logic controller, and it simplified the comparison of these two controllers.

A High-Performance Induction Motor Drive with 2DOF I-PD Model­Following Speed Controller

El-Sousy Fayez F. M. The Korean Institute of Power Electronics 2004 JOURNAL OF POWER ELECTRONICS Vol.4 No.4

A robust controller that combines the merits of the feed-back, feed-forward and model-following control for induction motor drives utilizing field orientation control is designed in this paper. The proposed controller is a two-degrees-of­freedom (2DOF) integral plus proportional & rate feedback (I-PD) speed controller combined with a model-following (2DOF I-PD MFC) speed controller. A systematic mathematical procedure is derived to find the parameters of the 2DOF I-PD MFC speed controller according to certain specifications for the drive system. Initially, we start with the I-PD feed­back controller design, then we add the feed-forward controller. These two controllers combine to form the 2DOF I-PD speed controller. To realize high dynamic performance for disturbance rejection and set point tracking characterisitics, a MFC controller is designed and added to the 2DOF I-PD controller. This combination is called a 2DOF I-PD MFC speed controller. We then study the effect of the 2DOF I-PD MFC speed controller on the performance of the drive system under different operating conditions. A computer simulation is also run to demonstrate the effectiveness of the proposed controller. The results verify that the proposed 2DOF I-PD MFC controller is more accurate and more reliable in the presence of load disturbance and motor parameter variations than a 2DOF I-PD controller without a MFC. Also, the proposed controller grants rapid and accurate responses to the reference model, regardless of whether a load disturbance is imposed or the induction machine parameters vary.

Design of Fuzzy PD+I Controller Based on PID Controller

Sea-June Oh,Heui-Han Yoo,Yun-Hyung Lee,Myung-Ok So 한국항해항만학회 2010 한국항해항만학회지 Vol.34 No.2

Since fuzzy controllers are nonlinear, it is more difficult to set the controller gains and to analyse the stability compared to conventional PID controllers. This paper proposes a fuzzy PD+I controller for tracking control which uses a linear fuzzy inference(product-sum-gravity) method based on a conventional linear PID controller. In this scheme the fuzzy PD+I controller works similar to the control performance as the linear PD plus I(PD+I) controller. Thus it is possible to analyse and design an fuzzy PD+I controller for given systems based on a linear fuzzy PD controller. The scaling factors tuning scheme, another topic of fuzzy controller design procedure, is also introduced in order to fine performance of the fuzzy PD+I controller. The scaling factors are adjusted by a real-coded genetic algorithm(RCGA) in off-line. The simulation results show the effectiveness of the proposed fuzzy PD+I controller for tracking control problems by comparing with the conventional PID controllers.