scholarly journals Optimization of electrochemical parameters in microbial fuel cell system based on Fuzzy-PID and CMAC neural network

2019 ◽  
Vol 9 (2) ◽  
pp. 135-142 ◽  
Author(s):  
Chengcai Fu ◽  
Fengying Ma
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Microbial Fuel Cells ◽  
Pid Controller ◽  
Cerebellar Model Articulation Controller ◽  
Fuzzy Pid ◽  
Control Effect ◽  
Cmac Neural Network ◽  
Electrochemical Parameters ◽  
Control Scheme

Due to the extensive application prospects on wastewater treatment and new energy development, microbial fuel cells (MFCs) have gained more and more attention by many scholars all over the world. The bioelectrochemical reaction in MFC system is highly complex, serious nonlinear and time-delay dynamic process, in which the optimal control of electrochemical parameters is still a considerable challenge. A new optimal control scheme for MFC system which combines proportional integral derivative (PID) controller with parameters fuzzy optional algorithm and cerebellar model articulation controller (CMAC) neural network was proposed. The simulation results demonstrate that the proposed control scheme has rapider response, better control effect and stronger anti-interference ability than Fuzzy PID controller by taking constant voltage output of MFC under the different load disturbances as example.

scholarly journals Fuzzy PD Control of Networked Control Systems Based on CMAC Neural Network

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Li-lian Huang ◽  
Jin Chen
Keyword(s):  
Neural Network ◽  
Control Systems ◽  
Control Method ◽  
Networked Control Systems ◽  
Networked Control ◽  
Cerebellar Model Articulation Controller ◽  
Pd Controller ◽  
Control Effect ◽  
Cmac Neural Network ◽  
Reasoning Mechanism

The network and plant can be regarded as a controlled time-varying system because of the random induced delay in the networked control systems. The cerebellar model articulation controller (CMAC) neural network and a PD controller are combined to achieve the forward feedback control. The PD controller parameters are adjusted adaptively by fuzzy reasoning mechanism, which can optimize the control effect by reducing the uncertainty caused by the network-induced delay. Finally, the simulations show that the control method proposed can improve the performance effectively.

Application of CMAC Neural Network & PID Control on Hydraulic Speed Servo System

2011 ◽  
Vol 323 ◽  
pp. 128-132
Author(s):  
Rui Tang
Keyword(s):  
Pid Control ◽  
Pid Controller ◽  
Servo System ◽  
Cerebellar Model Articulation Controller ◽  
Cmac Neural Network ◽  
Control Scheme ◽  
Tracking Ability ◽  
Velocity Tracking ◽  
The Mathematical Model ◽  
Neural Network Pid

Pointing on nonlinear and parameters vary with time in the velocity servo system, and the requirement of servo system is difficult to meet by the traditional PID control scheme. A control scheme of ANN-based PID controller is developed here for velocity tracking control for an electro-hydraulic velocity servo system. In this paper the mathematical model is established for the system, and the PID controller based on cerebellar model articulation controller (CMAC) are designed and connected in parallel. Numerical simulation results indicate that the proposed control scheme has an excellent performance on high precision velocity tracking ability and rejection of disturbance, comparing with conventional PID control strategy.

scholarly journals Applying a Cerebellar Model Articulation Controller Neural Network to a Photovoltaic Power Generation System Fault Diagnosis

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Kuei-Hsiang Chao ◽  
Bo-Jyun Liao ◽  
Chin-Pao Hung
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Fault Diagnosis ◽  
Cerebellar Model Articulation Controller ◽  
Training Process ◽  
Cmac Neural Network ◽  
Photovoltaic Power ◽  
Training Samples ◽  
Parallel Connections ◽  
Model Features

This study employed a cerebellar model articulation controller (CMAC) neural network to conduct fault diagnoses on photovoltaic power generation systems. We composed a module array using 9 series and 2 parallel connections of SHARP NT-R5E3E 175 W photovoltaic modules. In addition, we used data that were outputted under various fault conditions as the training samples for the CMAC and used this model to conduct the module array fault diagnosis after completing the training. The results of the training process and simulations indicate that the method proposed in this study requires fewer number of training times compared to other methods. In addition to significantly increasing the accuracy rate of the fault diagnosis, this model features a short training duration because the training process only tunes the weights of the exited memory addresses. Therefore, the fault diagnosis is rapid, and the detection tolerance of the diagnosis system is enhanced.

Adaptive Learning Algorithm for Cerebellar Model Articulation Controller: Neural Network Based Hybrid-Type Controller—Part I

2000 ◽  
Author(s):  
Magdy Mohamed Abdelhameed ◽  
Sabri Cetinkunt
Keyword(s):  
Neural Network ◽  
Control System ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Cerebellar Model Articulation Controller ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hybrid Type ◽  
Cmac Neural Network ◽  
Learning Technique

Abstract Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learning algorithm, especially when it is used in a hybrid- type controller. This work is intended to introduce a simulation study for examining the performance of a hybrid-type control system based on the conventional learning algorithm of CMAC neural network. This study showed that the control system is unstable. Then a new adaptive learning algorithm of a CMAC based hybrid- type controller is proposed. The main features of the proposed learning algorithm, as well as the effects of the newly introduced parameters of this algorithm have been studied extensively via simulation case studies. The simulation results showed that the proposed learning algorithm is a robust in stabilizing the control system. Also, this proposed learning algorithm preserved all the known advantages of the CMAC neural network. Part II of this work is dedicated to validate the effectiveness of the proposed CMAC learning algorithm experimentally.

DEVELOPMENT OF ANN-BASED PID CONTROLLER FOR ACTIVE FLUID-FILM BEARING CONTROL

2020 ◽  
Vol 6 ◽  
pp. 94-100
Author(s):  
N.N. MAKHOVA ◽  
A.Yu. BABIN
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Particle Swarm Optimization Algorithm ◽  
Fluid Film ◽  
Training Dataset ◽  
Swarm Optimization ◽  
Controlled System ◽  
Active Fluid ◽  
Control Scheme ◽  
Controlled Object

The article proposes a method for controlling an active fluid-film bearing, based on the use of a classical PID controller in conjunction with an artificial neural network. The regulator coefficients are not constant numbers, but are chosen by the network depending on the state of the controlled system. To implement such a control scheme, the coefficients are selected using a particle swarm optimization algorithm, which constitutes the training dataset, and an ANN is trained using the dataset. The controlled object is represented with a model operating in the Simulink environment.

CMAC Based Hybrid Control System for Solving Electrohydraulic System Nonlinearities

2014 ◽  
Vol 4 (2) ◽  
pp. 47-72
Author(s):  
Amro Shafik ◽  
Magdy Abdelhameed ◽  
Ahmed Kassem
Keyword(s):  
Neural Network ◽  
Control System ◽  
Hybrid Control ◽  
Learning Algorithm ◽  
Cerebellar Model Articulation Controller ◽  
Stick Slip ◽  
Electrohydraulic System ◽  
Cmac Neural Network ◽  
Wide Range ◽  
Hybrid Control System

Automation based electrohydraulic servo systems have a wide range of applications in nowadays industry. However, they still suffer from several nonlinearities like deadband in electrohydraulic valves, hysteresis, stick-slip friction in valves and cylinders. In addition, all hydraulic system parameters have uncertainties in their values due to the change of temperature while working. This paper addresses these problems by designing a suitable intelligent control system that has the ability to deal with the system nonlinearities and parameters uncertainties using a fast and online learning algorithm. A novel hybrid control system based on Cerebellar Model Articulation Controller (CMAC) neural network is presented. The proposed controller is composed of two parallel controllers. The first is a conventional Proportional-Velocity (PV) servo type controller which is used to decrease the large initial error of the closed-loop system. The second is a CMAC neural network which is used as an intelligent controller to overcome nonlinear characteristics of the electrohydraulic system. A fourth order model for the electrohydraulic system is introduced. PV controller parameters are tuned to get optimal values. Simulation and experimental results show a good tracking performance obtained using the proposed controller. The controller shows its robustness in two working environments. The first is by adding different inertia loads and the second is working with noisy level input signals.

Study on Control Strategies of Semi-Active Air Suspension for Heavy Vehicles Based on Road-Friendliness

2012 ◽  
Vol 452-453 ◽  
pp. 328-333
Author(s):  
Feng He ◽  
Jing Zhao ◽  
Hao Yu Wang
Keyword(s):  
Pid Controller ◽  
Fuzzy Controller ◽  
Control Strategies ◽  
Fuzzy Pid ◽  
Control Effect ◽  
The Road ◽  
Fuzzy Pid Controller ◽  
Air Suspension ◽  
Road Excitation ◽  
Band Limited

Targeting the road-friendliness of vehicles, the paper has analyzed dynamic deformation and dynamic load of tires under different control strategies through co-simulation. A vehicle dynamics model with semi-active air suspension has been made through using Adams, and a PID controller, a fuzzy controller and a fuzzy PID controller have been set in the Matlab to adjust the damping of the suspension, with the road excitation modeled through band-limited white noise. The result shows that the fuzzy PID controller has overcome the shortcomings of the PID controller and the fuzzy controller and a better control effect has been achieved.

Application of BP Neural Network PID Controller in Landing Gear Based on MRF Damper

2015 ◽  
Vol 779 ◽  
pp. 226-232 ◽  
Author(s):  
Shi Xing Zhu ◽  
Yue Han ◽  
Bo Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Experimental Testing ◽  
Computation Time ◽  
Landing Gear ◽  
Time Varying ◽  
Control Effect ◽  
Simulation And Experiment ◽  
Neural Network Pid

For characteristics of nonlinearity and time-varying volatility of landing gear based on MR damper, a BP neural network PID controller with a momentum was designed on basis of established dynamic mathematical model. BP neural network would adjust three parameters of PID online in time. The controller was inputted the energy which was combined by the feedback of acceleration and displacement of the control system, which greatly reduced the computation time of controller and the control effect was more obvious. After compared with PID, the simulation and experiment have showed that BP neural network PID has a better effect. The arithmetic can be put into practice through experimental testing.

Design of Fuzzy PID Controller for Water Level of Boiler Steam Drum Based on DSP

2011 ◽  
Vol 130-134 ◽  
pp. 3455-3458
Author(s):  
Yuan Fang Xin
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Water Level ◽  
Pid Controller ◽  
Good Control ◽  
Fuzzy Pid ◽  
Control Effect ◽  
Fuzzy Pid Controller ◽  
Steam Drum ◽  
Pid Arithmetic

A control system for water level of boiler steam drum based on DSP microprocessor is designed in this paper. The technique of fuzzy control was applied to controlling the water level of the steam drum, the operational principle and application of the fuzzy-PID arithmetic are described. The water level controller with good control effect is developed. Firstly the hardware structure of system is introduced in the paper and then investigates the fuzzy-PID arithmetic carry out, and finally, the simplified program flowchart are given.

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