Semi-active seismic control of a 9-story benchmark building using adaptive neural-fuzzy inference system and fuzzy cooperative coevolution

Masoud Bozorgvar,Seyed Mehdi Zahrai 국제구조공학회 2019 Smart Structures and Systems, An International Jou Vol.23 No.1

Control algorithms are the most important aspects in successful control of structures against earthquakes. In recent years, intelligent control methods rather than classical control methods have been more considered by researchers, due to some specific capabilities such as handling nonlinear and complex systems, adaptability, and robustness to errors and uncertainties. However, due to lack of learning ability of fuzzy controller, it is used in combination with a genetic algorithm, which in turn suffers from some problems like premature convergence around an incorrect target. Therefore in this research, the introduction and design of the Fuzzy Cooperative Coevolution (Fuzzy CoCo) controller and Adaptive Neural-Fuzzy Inference System (ANFIS) have been innovatively presented for semi-active seismic control. In this research, in order to improve the seismic behavior of structures, a semi-active control of building using Magneto Rheological (MR) damper is proposed to determine input voltage of Magneto Rheological (MR) dampers using ANFIS and Fuzzy CoCo. Genetic Algorithm (GA) is used to optimize the performance of controllers. In this paper, the design of controllers is based on the reduction of the Park-Ang damage index. In order to assess the effectiveness of the designed control system, its function is numerically studied on a 9-story benchmark building, and is compared to those of a Wavelet Neural Network (WNN), fuzzy logic controller optimized by genetic algorithm (GAFLC), Linear Quadratic Gaussian (LQG) and Clipped Optimal Control (COC) systems in terms of seismic performance. The results showed desirable performance of the ANFIS and Fuzzy CoCo controllers in considerably reducing the structure responses under different earthquakes; for instance ANFIS and Fuzzy CoCo controllers showed respectively 38 and 46% reductions in peak inter-story drift (J1) compared to the LQG controller; 30 and 39% reductions in J1 compared to the COC controller and 3 and 16% reductions in J1 compared to the GAFLC controller. When compared to other controllers, one can conclude that Fuzzy CoCo controller performs better.

직류전압 퍼지 제어 기반의 3상 Z-소스 PWM 정류기

수효동,정영국,임영철 전력전자학회 2013 전력전자학회 논문지 Vol.18 No.5

This paper describes a fuzzy control method to control the output voltage of the three-phase Z-source PWM rectifier. A fuzzy control system is a control system based on fuzzy logic, and the fuzzy controller uses a single input fuzzy theory with its fuzzification. Analytical structure of the simplest fuzzy controller is derived through the triangular membership functions with its fuzzification. By setting the membership functions of the fuzzy rules, fuzzy control is achieved. The PI portion of the output DC voltage controller is controlled by fuzzy method. To confirm the validity of the proposed method, the simulation and experiment were performed, The simulation is performed with PSIM and MATLAB/SIMULINK. For the experiment, we used a DSP(TMS320F28335) controller to compute the reference value and generate the PWM pulses. For the transient state performance of the output DC voltage control of Z-source PWM rectifier, the PI controller and fuzzy controller were compared, also the conventional PWM rectifier and Z-source PWM rectifier were compared. From the results, the Z-source rectifier could allow to buck or boost of the output DC voltage. Through the analysis of the transient state, we could observe that the fuzzy controller has better performance than the conventional PI controller.

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.

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.

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

Kim, Young-Real Korean Institute of Intelligent Systems 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.

Three-Phase Z-Source PWM Rectifier Based on the DC Voltage Fuzzy Control

Xiao-Dong Qiu(수효동),Young-Gook Jung(정영국),Young-Cheol Lim(임영철) 전력전자학회 2013 전력전자학회 논문지 Vol.18 No.5

This paper describes a fuzzy control method to control the output voltage of the three-phase Z-source PWM rectifier. A fuzzy control system is a control system based on fuzzy logic, and the fuzzy controller uses a single input fuzzy theory with its fuzzification. Analytical structure of the simplest fuzzy controller is derived through the triangular membership functions with its fuzzification. By setting the membership functions of the fuzzy rules, fuzzy control is achieved. The PI portion of the output DC voltage controller is controlled by fuzzy method. To confirm the validity of the proposed method, the simulation and experiment were performed, The simulation is performed with PSIM and MATLAB/SIMULINK. For the experiment, we used a DSP(TMS320F28335) controller to compute the reference value and generate the PWM pulses. For the transient state performance of the output DC voltage control of Z-source PWM rectifier, the PI controller and fuzzy controller were compared, also the conventional PWM rectifier and Z-source PWM rectifier were compared. From the results, the Z-source rectifier could allow to buck or boost of the output DC voltage. Through the analysis of the transient state, we could observe that the fuzzy controller has better performance than the conventional PI controller.

퍼지제어에 의한 자연 환기 온실의 온도제어

정태상,민영봉,문경규 (사) 한국생물환경조절학회 2001 시설원예‧식물공장 Vol.10 No.1

This study was carried out to develop a fuzzy control technique of ventilation window for control-ling a temperature in a greenhouse. To reduce the fuzzy variables, the inside air temperature slop wastaken as one of fuzzy variables, because the inside air temperature variation of a greenhouse by ven-tilation at the same window aperture is affected by the difference between inside and outside air tem-perature, outside wind speed and the wind direction. Therefore, the antecedent variables for fuzzyalgorithm were used the control error and its slop, which was same value as the inside air tempera-ture slop during the control period, and the conclusion variable was used the window aperture open-ing rate. Through the basic and applicative control experiment with the control period of 3 minutes,the optimum ranges of fuzzy variables were decided. The control error and its slop were taken as 3and 1.5 times compared with target error in steady state, and the window opening rate were taken as30% of full size of the window aperture. To evaluate the developed fuzzy algorithm in which the opti-mized 19 rules of fuzzy production were used, the performances of fuzzy control and PID control werecompared. The temperature control errors by the fuzzy control and PID control were lower than1.3oC and 2.2oC respectively. The accumulated operating size of the window, the number of operatingand the number of inverse operating for the fuzzy control were 0.4 times, 0.5 times and 0.3 times ofthose compared with the PID control. Therefore, the fuzzy control can operate the window moresmooth and reduce the operating energy by 1/2 times of PID control.

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.

다풍력 발전기 피치 제어를 위한 퍼지 PI 제어기

천종민,김진욱,김홍주,최영규,김무림 사단법인 유공압건설기계학회 2018 드라이브·컨트롤 Vol.15 No.1

When the wind speed rises above the rated wind speed, the produced power of the wind turbines exceeds the rated power. Even more, the excessive power results in the undesirable mechanical load and fatigue. A solution to this problem is pitch control of the wind turbines. This paper presents a systematic design method of a collective pitch controller for the wind turbines using a discrete fuzzy Proportional-Integral (PI) controller. Unlike conventional PI controllers, the fuzzy PI controller has variable gains according to its input variables. Generally, tuning the parameters of fuzzy PI controller is complex due to the presence of too many parameters strongly coupled. In this paper, a systematic method for the fuzzy PI controller is presented. First, we show the fact that the fuzzy PI controller is a superset of the PI controller in the discrete-time domain and the initial parameters of the fuzzy PI controller is selected by using this relationship. Second, for simplicity of the design, we use only four rules to construct nonlinear fuzzy control surface. The tuning parameters of the proposed fuzzy PI controller are also obtained by the aforementioned relationship between the PI controller and the fuzzy PI controller. As a result, unlike the PI controller, the proposed fuzzy PI controller has variable gains which allow the pitch control system to operate in broader operating regions. The effectiveness of the proposed controller is verified with computer simulations using FAST, a NREL's primary computer-aided engineering tool for horizontal axis wind turbines.

A Model-free Output Feedback Adaptive Optimal Fuzzy Controller for LC-filtered Three-phase Voltage Source Inverters

Nam Hai Trinh,Loc Ong Xuan,Nga Thi-Thuy Vu,Anh Tuan Nguyen 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.6

This paper proposes a model-free output feedback control-based adaptive fuzzy controller using a current-sensorless configuration for LC-filtered three-phase voltage source inverters (VSIs). The proposed adaptive fuzzy scheme is constructed of three parts: an adapter, an adaptive optimal fuzzy controller, and an adaptive optimal fuzzy identifier. The adapter is designed based on an adaptive neuro-fuzzy inference system (ANFIS) network which uses the error between the system output and identifier output as an input to generate the online updated parameters. Next, both the adaptive fuzzy controller and the fuzzy identifier are designed based on the Takagi-Sugeno (T-S) fuzzy model. In particular, the proposed algorithm is robust against external disturbance and parameter uncertainties due to not requiring the system parameters. Moreover, the proposed scheme uses a current-sensorless configuration, which reduces the system complexity and cost. Both the stability of the proposed method and the convergence of adapted parameters are completely assured by using the Lyapunov stability theory. Finally, the effectiveness of the proposed adaptive fuzzy controller is verified through simulation in comparison with a conventional T-S fuzzy controller. The results show that the proposed model-free output feedback control-based adaptive fuzzy controller yields better control performance, such as faster transient response, smaller steady-state error, and lower total harmonic distortion (THD) under the change of load (step changes of linear load, unbalanced load, and nonlinear load), parameter variations, and input disturbances.