Systematic design of membership functions for fuzzy-logic control: A case study on one-stage partial nitritation/anammox treatment systems

Boiocchi, Riccardo; Gernaey, Krist V.; Sin, Gürkan

doi:10.1016/j.watres.2016.06.047

Cited by 14 publications

(13 citation statements)

References 23 publications

(9 reference statements)

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“…Following the systematic development of the membership functions used by Boiocchi et al (2016), the approach to develop the fuzzy-logic control strategy involved the following workflow:…”

Section: Design Of the Control Strategymentioning

confidence: 99%

“…In contrast to what has been done for the controller developed by Boiocchi et al (2016), the objectives for N O emissions cannot be expressed numerically because -differently from the TN removal efficiency -the minimum amount of N 2 O possibly emitted varies quite a lot from one plant to another according to many operating and design parameters. Instead, the control objectives are here defined qualitatively as follows:…”

Section: Definition Of the Control Objectivesmentioning

confidence: 99%

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A novel fuzzy-logic control strategy minimizing N2O emissions

2017

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A novel control strategy for achieving low NO emissions and low effluent NH concentration is here proposed. The control strategy uses the measurements of ammonium and nitrate concentrations in inlet and outlet of the aerobic zone of a wastewater treatment plant to calculate a ratio indicating the balance among the microbial groups. More specifically, the ratio will indicate if there is a complete nitrification. In case nitrification is not complete, the controller will adjust the aeration level of the plant in order to inhibit the production of NO from AOB and HB denitrification. The controller was implemented using the fuzzy logic approach. It was comprehensively tested for different model structures and different sets of model parameters with regards to its ability of mitigating NO emissions for future applications in real wastewater treatment plants. It is concluded that the control strategy is useful for those plants having AOB denitrification as the main NO producing process. However, in treatment plants having incomplete NHOH oxidation as the main NO producing pathway, a cascade controller configuration adapting the oxygen supply to respect only the effluent ammonium concentration limits was found to be more effective to ensure low NO emissions.

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“…Following the systematic development of the membership functions used by Boiocchi et al (2016), the approach to develop the fuzzy-logic control strategy involved the following workflow:…”

Section: Design Of the Control Strategymentioning

confidence: 99%

Section: Definition Of the Control Objectivesmentioning

confidence: 99%

A novel fuzzy-logic control strategy minimizing N2O emissions

2017

Self Cite

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“…A fuzzy logic describes a control protocol by means of if–then rules. In engineering systems, it provides a convenient and user-friendly front-end to develop control programs [32,33]. …”

Section: Fuzzy Controlmentioning

confidence: 99%

“…Designing a fuzzy controller is a simple concept that includes the three stages: fuzzification (an input stage), rule evaluation (a processing stage) and defuzzification (an output stage) [32]. The developed fuzzy logic control strategy for the IWS was designed in MATLAB using max–min inference and centroid defuzzification.…”

Section: Fuzzy Controlmentioning

confidence: 99%

Design and Implementation of an Intelligent Windowsill System Using Smart Handheld Device and Fuzzy Microcontroller

Wang

Yang

Chen

2017

Sensors

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With the advance of science and technology, people have a desire for convenient and comfortable living. Creating comfortable and healthy indoor environments is a major consideration for designing smart homes. As handheld devices become increasingly powerful and ubiquitous, this paper proposes an innovative use of smart handheld devices (SHD), using MIT App Inventor and fuzzy control, to perform the real-time monitoring and smart control of the designed intelligent windowsill system (IWS) in a smart home. A compact weather station that consists of environment sensors was constructed in the IWS for measuring of indoor illuminance, temperature-humidity, carbon dioxide (CO2) concentration and outdoor rain and wind direction. According to the measured environment information, the proposed system can automatically send a command to a fuzzy microcontroller performed by Arduino UNO to fully or partly open the electric curtain and electric window for adapting to climate changes in the indoor and outdoor environment. Moreover, the IWS can automatically close windows for rain splashing on the window. The presented novel control method for the windowsill not only expands the SHD applications, but greatly enhances convenience to users. To validate the feasibility and effectiveness of the IWS, a laboratory prototype was built and confirmed experimentally.

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“…In this work the fuzzy rules differ from the study in [34] as they provide a better tuning behaviour for the fully actuated system in the disturbed non-linear environment. The aim of using Gaussian membership functions is to obtain a non-linear response because of the non-linearity of the AUV system dynamics and hydrodynamics [35]. The AUV configuration along with the inverse kinematics and control architecture used in this research is demonstrated and presented in our previous work [18].…”

Section: Introductionmentioning

confidence: 99%

Trajectory following and stabilization control of fully actuated AUV using inverse kinematics and self-tuning fuzzy PID

2017

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In this work a design for self-tuning non-linear Fuzzy Proportional Integral Derivative (FPID) controller is presented to control position and speed of Multiple Input Multiple Output (MIMO) fully-actuated Autonomous Underwater Vehicles (AUV) to follow desired trajectories. Non-linearity that results from the hydrodynamics and the coupled AUV dynamics makes the design of a stable controller a very difficult task. In this study, the control scheme in a simulation environment is validated using dynamic and kinematic equations for the AUV model and hydrodynamic damping equations. An AUV configuration with eight thrusters and an inverse kinematic model from a previous work is utilized in the simulation. In the proposed controller, Mamdani fuzzy rules are used to tune the parameters of the PID. Nonlinear fuzzy Gaussian membership functions are selected to give better performance and response in the non-linear system. A control architecture with two feedback loops is designed such that the inner loop is for velocity control and outer loop is for position control. Several test scenarios are executed to validate the controller performance including different complex trajectories with and without injection of ocean current disturbances. A comparison between the proposed FPID controller and the conventional PID controller is studied and shows that the FPID controller has a faster response to the reference signal and more stable behavior in a disturbed non-linear environment.

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Systematic design of membership functions for fuzzy-logic control: A case study on one-stage partial nitritation/anammox treatment systems

Cited by 14 publications

References 23 publications

A novel fuzzy-logic control strategy minimizing N2O emissions

A novel fuzzy-logic control strategy minimizing N2O emissions

Design and Implementation of an Intelligent Windowsill System Using Smart Handheld Device and Fuzzy Microcontroller

Trajectory following and stabilization control of fully actuated AUV using inverse kinematics and self-tuning fuzzy PID

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