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Multiobjective design of fuzzy neural network controller for wastewater treatment process

APPLIED SOFT COMPUTING 2018年 67卷 467-478页

作者： Han, Hong-Gui Zhang, Lu Liu, Hong-Xu Qiao, Jun-Fei Beijing Univ Technol Coll Automat Fac Informat Technol Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China

In this paper, an improved multiobjective optimal control (MOOC) strategy is developed to improve the operational efficiency, satisfy the effluent quality (EQ) and reduce the energy consumption (EC) in wastewater treatment process (WWTP). First, the adaptive kernel function models of the process, which can describe the complex dynamics of EQand EC, are developed for the proposed MOOC strategy. Meanwhile, a multiobjective optimization problem is constituted to account for WWTP. Second, an improved multiobjective particle swarm optimization (MOPSO) algorithm, using a self-adaptive flight parameters mechanism and a multiobjective gradient (MOG) method, is designed to minimize the established objectives. And then the optimal set-points of dissolved oxygen (So) and nitrate (SNQ) are obtained in the treatment process. Third, an adaptive fuzzy neural network controller (FNNC) is applied for realizing the tracking control of the obtained set-points in the proposed MOOC strategy. Finally, Benchmark Simulation Model No.1 (BSM1) is introduced to evaluate the effectiveness of the proposed MOOC strategy. Experimental results show the efficacy of the proposed method. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Multiobjective optimal control Wastewater treatment process Adaptive kernel function models Multiobjective particle swarm optimization fuzzy neural network controller

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Varying parameters of the Object Model on fuzzy neural network controller for Activated sludge method of the Sewage treatment system

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International Conference on Frontiers of Manufacturing Science and Measuring Technology (ICFMM2011)

作者： Shi, Zhaohui Wang, Chengzhi Dezhou Vocat & Tech Coll Dezhou Shandong Peoples R China

ISBN: (纸本)9783037851333

In this paper,we take characteristics of wastewater treatment and process technology, drawing on the effectiveness of the traditional PID control and on the basis of its lack,with the key steps in the sewage treatment process Aeration control of part of the process parameters, fuzzy neural network control of dissolved oxygen concentration (DO) to achieve negative feedback control loop,design a model-based closed-loop cascade control system. fuzzy systems,membership function,the structure of the network topology and algorithms are based on the actual issues identified in the fuzzy variables. Aiming at the four parts of the fuzzy control,adopting four fuzzy neural network based on the standard model the input layer, fuzzy layer,Inference layer, Clear layer are corresponding with it. Standing on two point: the dissolved oxygen concentration control and the rate of change from the error, then design the fuzzy neural network controller. Then the fuzzy neural network control technology could be used in wastewater treatment on the specific application of process control.

关键词： Urban sewage treatment fuzzy neural network controller DO Cascade closed-loop control system

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New Temperature Idity fuzzy neural neural network controller Based On Improved Genetic Algorithm

<bold>New Temperature Idity Fuzzy Neural Neural Network Cont...

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8th International Conference on Electronic Measurement and Instruments

作者： Zhang Su Zhang Shuguang Liu Yu Gao Li'ai Agr Univ Hebei Coll Mech & Elect Engn Baoding 071001 Peoples R China Agr Univ Hebei Sch Run Ind Management Syst Baoding 071001 Peoples R China

ISBN: (纸本)1424411351

The greenhouse is a complex system. There is strong coupling relationship among its environm ental factors in side greenhouse. So it's difficult to get satisfying effect by using conventional control methods. Based on the characteristics of the greenhouse, a new fuzzy neural network controller was proposd to create a proper condition for crop growth. The genetic algorithm was used to train the architecture of fuzzy neural network controller, which was adopted in the greenhouse Finally the simulations with Matlab about this system were done. The result was that the speed and precision of the system were improved. The overshoot was reduced also. And the greenhouse system with this fuzzy neural network based genetic algorithm had such features as responding quickly, smooth transition.

关键词： fuzzy neural network controller genetic algorithm greenhouse tempreture

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Implementation of DSP-based fuzzy neural network-PI controller for PMSM

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ENERGY EDUCATION SCIENCE AND TECHNOLOGY PART A-ENERGY SCIENCE AND RESEARCH 2012年第2期29卷 1177-1182页

作者： Gencer, Cetin Firat Univ Dept Elect Sci Fac Tech Educ TR-23169 Elazig Turkey

An improved fuzzy neural network Proportional Integral (FNN-PI) controller based on Sugeno fuzzy model is proposed in this paper for Permanent Magnet Synchronous Motor (PMSM) speed control. The improved FNN-PI controller has a compact structure and used for gradient descent algorithm. Experimental results have shown excellent tracking performance of the proposed FNN-PI controller, and have convincingly demonstrated the usefulness of the proposed controller in industrial drives with uncertainties.

关键词： fuzzy neural network controller Sugeno model Permanent magnet DC motor Motor drive

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New Temperature Idity fuzzy neural neural network controller Based On Improved Genetic Algorithm

New Temperature Idity Fuzzy Neural Neural Network Controller...

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2007 8th International Conference on Electronic Measurement & Instruments（ICEMI’2007）

作者： Zhang Su 1 Zhang Shuguang 1 Liu Yu 2 Gao Li'ai 1 (1 College of Mechanical and Electrical Engineering,Agriculture University of Hebei,Baoding 071001 China) (2 The office of the School-run Industrial Management System,Agriculture University of Hebei,Baoding 071001 China)

The greenhouse is a complex *** is strong coupling relationship among its environm ental factors in side *** it's difficult to get satisfying effect by using conventional control *** on the characteristics of the greenhouse,a new fuzzy neural network controller was proposd to create a proper condition for crop *** genetic algorithm was used to train the architecture of fuzzy neural network controller,which was adopted in the ***,the simulations with Matlab about this system were *** result was that the speed and precision of the system were *** overshoot was reduced *** the greenhouse system with this fuzzy neural network based genetic algorithm had such features as responding quickly,smooth transition.

关键词： fuzzy neural network controller genetic algorithm greenhouse tempreture

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Adaptive fuzzy-neural-network velocity sensorless control for robot manipulator position tracking

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IET CONTROL THEORY AND APPLICATIONS 2010年第6期4卷 1079-1093页

作者： Wai, R. -J. Huang, Y. -C. Yang, Z. -W. Shih, C. -Y. Yuan Ze Univ Dept Elect Engn Chungli 32003 Taiwan Yuan Ze Univ Fuel Cell Ctr Chungli 32003 Taiwan

This study focuses on the development of an adaptive fuzzy-neural-network velocity sensorless control (AFNNVSC) scheme for an n-link robot manipulator to achieve high-precision position tracking. In general, it is difficult to adopt a model-free design without the joint velocity/acceleration information to achieve this control objective owing to uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order to cope with this problem, an AFNNVSC scheme including a non-linear observer and a fuzzy-neural-network (FNN) controller is investigated without the requirement of prior system information. This non-linear observer is used to estimate joint velocities of the robot manipulator. Then, a four-layer FNN is utilised for the major control role without auxiliary compensated control, and the adaptive tuning laws of network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the stable control performance. Experimental results of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed AFNNVSC methodology. In addition, the superiority of the proposed control scheme is indicated in comparison with the proportional-integral-differential control, computed torque control, Takagi-Sugeno-Kang-type fuzzy-neural-network control and robust-neural-fuzzy-network control systems.

关键词： adaptive control proportional integral differential control neurocontrollers manipulators Stability in control theory robot manipulator position tracking fuzzy control nonlinear observer velocity control Mechanical variables control auxiliary compensated control Velocity, acceleration and rotation control adaptive tuning laws Self-adjusting control systems Lyapunov methods velocity sensorless control computed torque control Takagi Sugeno Kang fuzzy control torque control Neurocontrol robust-neural-fuzzy-network control systems tracking Manipulators Lyapunov stability theorem observers dc servomotors friction forces servomotors fuzzy neural network controller

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Mechanical performance analysis of a piezoelectric ceramic friction damper and research of its semi-active control strategy

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STRUCTURES 2021年 33卷 1510-1531页

作者： Wang, Tianlong Zhang, Xun'an Li, Kun Yang, Shixuan Northwestern Polytech Univ Sch Mech Civil Engn & Architecture 1 Dongxiang Rd Xian 710129 Shaanxi Peoples R China Huaqiao Univ Coll Civil Engn 668 Jimei Ave Xiamen 361021 Fujian Peoples R China

Based on piezoelectric ceramics' piezoelectric effect, a new type of piezoelectric ceramic friction damper (PCFD) is designed and manufactured to provide a reliable energy dissipation device for structural response control. In the PCFD, high-strength bolts limit the deformation of piezoelectric ceramics under voltage, and the resulting reaction force changes the preload of high-strength bolts, thereby realizing real-time adjustment of friction by adjusting the positive pressure on the friction surface. The influence of friction plates with different materials on the hysteretic performance of friction damper under different tightening torque is investigated by cyclic loading test to determine the friction plate's selection in the PCFD. The hysteretic performance test is carried out to evaluate the energy dissipation capacity of PCFD with different voltages, and the experimental results are compared with the theoretical values. The finite element model of PCFD is established to analyze the stress distribution of each component of the PCFD under different voltages. Based on the semi-active control characteristics of PCFD, the Takagi-Sugeno fuzzy neural network semi-active control system for frame structure with PCFD is designed. The adaptive learning function of the adaptive network-based fuzzy inference system (ANFIS) is used to generate fuzzy rules and fuzzy neural network controller (FNNC). Finally, the semi-active control simulation of three-storey frame structure model under seismic excitation is carried out by Simulink. The experimental and numerical results show that the PCFD designed in this paper has good energy dissipation capacity, and the maximum control force increases linearly with the increase of voltage. The combination of FNNC, which is established by ANFIS, and PCFD can effectively reduce the structure response.

关键词： Piezoelectric ceramic friction damper Friction plate fuzzy neural network controller Adaptive network-based fuzzy inference system Semi-active control

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fuzzy control technique based on Genetic Algorithms optimizing and its application

Fuzzy control technique based on Genetic Algorithms optimizi...

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IEEE International Conference on Intelligent Processing Systems

作者： Yi, JK Yan, HP Sun, HT Hou, YB Beijing Polytech Univ Dept Automat Beijing 100022 Peoples R China

ISBN: (纸本)0780342534

Aimed at a kind of complex industrial production processes with nonlinear dynamic properties, the fuzzy-neural network controller based on Genetic Algorithms was designed in this paper. The coding optimization method for the parameters of the stimulating functions of the network's hidden nodes is the concatenated, multi-parameters, mapped, and fixed-point coding method. Based on this method, we can get the optimized fuzzy controller on-line. The step response curve of practical oven temperature control is given in this paper. The experimental results show that the fuzzy-neural network controller based on Genetic Algorithms optimizing is robust and effective and will find wide industrial application scope.

关键词： process control production control fuzzy control fuzzy set theory fuzzy neural nets neurocontrollers genetic algorithms temperature control fuzzy control technique genetic algorithms complex industrial production process nonlinear dynamic properties fuzzy neural network controller coding optimization method stimulating functions hidden nodes fixed point coding method optimized fuzzy controller step response curve oven temperature control industrial application

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The fuzzy neural network Temperature Control in PCR Reactor

The Fuzzy Neural Network Temperature Control in PCR Reactor

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4th International Conference on Wireless Communications, networking and Mobile Computing

作者： Zhang Su Liu Yu Cheng Man Zhao Xueliang Agr Univ Hebei Coll Mech Elect Engn Baoding Peoples R China Agr Univ Hebei Off Sch Run Ind Management Syst Baoding Peoples R China CGS Ctr Hydrol environm geol Baoding Peoples R China

The paper discusses a new fuzzy neural network controller in PCR reactor. By tuning the three parameters KP, KI, KD on line, the new fuzzy neural network controller has better response rate and stability property than... 详细信息

ISBN: (纸本)9781424421077

关键词： PCR Reactor fuzzy neural network controller genetic algorithm temperature control

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Application of Mind Evolutionary Computation based fuzzy neural network in Variable-speed Wind Turbines

Application of Mind Evolutionary Computation based fuzzy neu...

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31st Chinese Control Conference

作者： Liu Qingsong Qian Suxiang Xiong Yuansheng Jiaxing Univ Jiaxing Shi 314001 Zhejiang Sheng Peoples R China

ISBN: (纸本)9789881563811

To satisfy the requirements of widely operational range of rotor speed, high nonlinearity and time variant characteristics in variable-speed wind turbines, a system with a fuzzy neural network controller based on mind evolutionary computation (MEC) optimization was designed. The controller combined the advantage of fast searching optimization of MEC and the advantage of not depending on controlled plant of fuzzy neural network controller. This method uses MEC to search the optimal mean, the optimal standard deviation and the optimal weights that connect membership layer and rule layer, and the fuzzy neural network controller has good control performance. Simulation and experimental results verified the effectiveness of the method that based on mind evolutionary fuzzy neuron (MEFN) controller. The results show that this method has good control effect on system regulating. For variable-speed wind turbines system in practice, this method has has excellent adaptability and robustness.

关键词： fuzzy neural network controller Mind evolutionary computation Wind power generation

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