scholarly journals Fractional-order Learning Algorithm for PID Neural Network Decoupling Control Based on Sparrow Search Algorithm

2021 ◽  
Author(s):  
Min Wu ◽  
Jie Ding ◽  
Tingting Yuan ◽  
Min Xiao
Keyword(s):  
Neural Network ◽  
Fractional Order ◽  
Learning Algorithm ◽  
Search Algorithm ◽  
Decoupling Control ◽  
Local Optimum ◽  
Control Effect ◽  
Control Scheme ◽  
Tracking Time ◽  
System Variables

Abstract Research on control of multi-variable system with strong coupling has been a significant issue in industry. To accurately eliminate the coupling between system variables and improve the control effect, decoupling control techniques are investigated. In this paper, a decoupling control scheme based on fractional-order proportion integration differentiation neural network and sparrow search algorithm (SSA-FPIDNN) is proposed, where sparrow search algorithm is employed to derive the optimal initial weights, preventing the weights from falling into the local optimum, while the fractional-order algorithm is used to correct its connection weights to improve control accuracy. Compared with traditional PIDNN, the proposed SSA-FPIDNN has better decoupling control performance, and the tracking time can be reduced significantly. Numerical simulation and engineering examples verified its effectiveness.

Adaptive Feedback Linearization Control of SISO Nonlinear Processes Using a Self-Generating Neural Network-Based Approach

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Karim Salahshoor ◽  
Amin Sabet Kamalabady
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Control ◽  
Feedback Linearization ◽  
Learning Algorithm ◽  
Rbf Neural Networks ◽  
Adaptive Feedback ◽  
Control Scheme ◽  
Feedback Linearization Control ◽  
Adaptive Control Scheme

This paper presents a new adaptive control scheme based on feedback linearization technique for single-input, single-output (SISO) processes with nonlinear time-varying dynamic characteristics. The proposed scheme utilizes a modified growing and pruning radial basis function (MGAP-RBF) neural network (NN) to adaptively identify two self-generating RBF neural networks for online realization of a well-known affine model structure. An extended Kalman filter (EKF) learning algorithm is developed for parameter adaptation of the MGAP-RBF neural networks. The MGAP-RBF growing and pruning criteria have been endeavored to enhance its performance for online dynamic model identification purposes. A stability analysis has been provided to ensure the asymptotic convergence of the proposed adaptive control scheme using Lyapunov criterion. Capabilities of the adaptive feedback linearization control scheme is evaluated on two nonlinear CSTR benchmark processes, demonstrating good performances for both set-point tracking and disturbance rejection objectives.

scholarly journals A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2790
Author(s):  
Qi Xiong ◽  
Xinman Zhang ◽  
Shaobo He ◽  
Jun Shen
Keyword(s):  
Fractional Order ◽  
Iris Recognition ◽  
Search Algorithm ◽  
Fitness Function ◽  
Histogram Equalization ◽  
Enhancement Effect ◽  
Local Optimum ◽  
Long Distance ◽  
Iris Image ◽  
Bee Colony

At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

Neural Network Inverse Based Omnidirectional Rotation Decoupling Control to the Electrodynamic Reaction Sphere

2019 ◽  
Author(s):  
Zhouyu Huai ◽  
Ming Zhang ◽  
Yu Zhu ◽  
Anlin Chen ◽  
Xin Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Attitude Control ◽  
Inverse Dynamics ◽  
Back Propagation ◽  
Good Control ◽  
Original System ◽  
Decoupling Control ◽  
Field Analysis ◽  
Element Analysis ◽  
Control Scheme

Abstract The electrodynamic reaction sphere is a novel actuator for the spacecraft attitude control subsystem. This paper proposes a neural network inverse based decoupling control scheme to actualize the omnidirectional rotation of the electrodynamic reaction sphere which has strong multivariable nonlinear coupling features due to the induction-based drive. And an integrated electromagnetic torque model of the reaction sphere is firstly derived from the electromagnetic field analysis and modified with the finite element analysis method. Then based on the integrated torque model, a back propagation feedforward neural network is constructed and trained to approach the inverse dynamics which transforms the original system into a pseudo-linear system. Furthermore, an additional PI controller is introduced to achieve good control performance against the unmodelled dynamics. Finally, the effectiveness of the proposed method is validated by simulations.

Study on Glass Furance Temperature Dual Control Based on Neural Networks Decoupling Control under WINCC

2013 ◽  
Vol 341-342 ◽  
pp. 856-860
Author(s):  
Hao Ming Yang ◽  
Lan Qing Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Algorithm ◽  
Decoupling Control ◽  
Dual Control ◽  
Script Language ◽  
Learning Speed ◽  
The Neural Network ◽  
Levenberg Marquardt ◽  
Experiment Control

Experiment control platform for the neural network decoupling control is constructed for the glass furnace taking heavy oil as fuel. By dual control, the improving Levenberg-Marquardt learning algorithm is discussed in order to improve the learning speed and to satisfy the real control. The neural network decoupling real control based on C-Script language and PLC S7-400 hard system under WINCC is realized with satisfying control results.

A novel modeling approach and its application in polymer quality index prediction

2018 ◽  
Vol 41 (7) ◽  
pp. 2005-2015
Author(s):  
Miao Zhang ◽  
Xinggao Liu ◽  
Zeyin Zhang ◽  
Guangbi Gong ◽  
Guoqing Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Quality Index ◽  
Learning Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Fuzzy Model ◽  
Wavelet Neural Network ◽  
Propylene Polymerization ◽  
Modeling Approach ◽  
Gradient Descent Algorithm

Accurate and reliable quality index prediction is indispensable in quality control of the industrial propylene polymerization (PP) processes. This paper presents a novel modeling approach for quality index prediction based on optimal fuzzy wavelet neural network (FWNN) with improved gravitational search algorithm (IGSA), where the constant or a linear function of inputs in conclusion part of traditional TSK fuzzy model is replaced with wavelet neural network (WNN). Then, an online learning algorithm of the FWNN model is derived by using gradient descent algorithm, and an IGSA algorithm is proposed to online adapt the learning rates of FWNN. Research on the proposed soft sensor is carried out with the data from a real industrial PP plant, and the results are compared among the WNN, FWNN and IGSA-FWNN models. The research results show that the proposed prediction model achieves a good performance in practical industrial quality index, melt index, prediction process.

RBF Neural Network in the Application of Synthetic Tower Temperature Decoupling Control Research

2013 ◽  
Vol 380-384 ◽  
pp. 491-494
Author(s):  
Zhe Zhang ◽  
Li Jun Hao ◽  
Bing Ma
Keyword(s):  
Neural Network ◽  
Production Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Decoupling Control ◽  
Economic Returns ◽  
Chemical Production ◽  
Control Effect ◽  
Control Research ◽  
Coupling Characteristics

The chemical production is vital to the development of our country.It is greatly significant to improve the ammonia synthesis production control project and to increase the economic returns. In allusion to a controlled object with coupling characteristics,in this paper RBF neural network decoupling controller is designed to realize the decoupling control of the synthetic tower temperature. Through the comparison of the simulation test results, the scheme shows that it has a better control effect than the conventional PID decoupling control method, so, if the scheme could be used in the actual chemical production process, it shall have a certain value in use.

scholarly journals Robust Adaptive Control for Nonlinear Uncertain Systems Using Type-2 Fuzzy Neural Network System

2011 ◽  
Vol 2011 ◽  
pp. 1-25
Author(s):  
Ching-Hung Lee ◽  
Yu-Ching Lin
Keyword(s):  
Neural Network ◽  
Uncertain Systems ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Robust Adaptive Control ◽  
Control Scheme ◽  
Fuzzy Neural ◽  
Nonlinear Uncertain Systems ◽  
Robust Adaptive

This paper proposes a novel intelligent control scheme using type-2 fuzzy neural network (type-2 FNN) system. The control scheme is developed using a type-2 FNN controller and an adaptive compensator. The type-2 FNN combines the type-2 fuzzy logic system (FLS), neural network, and its learning algorithm using the optimal learning algorithm. The properties of type-1 FNN system parallel computation scheme and parameter convergence are easily extended to type-2 FNN systems. In addition, a robust adaptive control scheme which combines the adaptive type-2 FNN controller and compensated controller is proposed for nonlinear uncertain systems. Simulation results are presented to illustrate the effectiveness of our approach.

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 Adaptive Backstepping Fuzzy Neural Network Fractional-Order Control of Microgyroscope Using a Nonsingular Terminal Sliding Mode Controller

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Juntao Fei ◽  
Xiao Liang
Keyword(s):  
Neural Network ◽  
Fractional Order ◽  
Fuzzy Neural Network ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Terminal Sliding Mode ◽  
Nonsingular Terminal Sliding Mode ◽  
Control Scheme ◽  
Fuzzy Neural ◽  
Neural Network Compensator

An adaptive fractional-order nonsingular terminal sliding mode controller for a microgyroscope is presented with uncertainties and external disturbances using a fuzzy neural network compensator based on a backstepping technique. First, the dynamic of the microgyroscope is transformed into an analogical cascade system to guarantee the application of a backstepping design. Then, a fractional-order nonsingular terminal sliding mode surface is designed which provides an additional degree of freedom, higher precision, and finite convergence without a singularity problem. The proposed control scheme requires no prior knowledge of the unknown dynamics of the microgyroscope system since the fuzzy neural network is utilized to approximate the upper bound of the lumped uncertainties and adaptive algorithms are derived to allow online adjustment of the unknown system parameters. The chattering phenomenon can be reduced simultaneously by the fuzzy neural network compensator. The stability and finite time convergence of the system can be established by the Lyapunov stability theorem. Finally, simulation results verify the effectiveness of the proposed controller and the comparison of root mean square error between different fractional orders and integer order is given to signify the high precision tracking performance of the proposed control scheme.

scholarly journals A rubust fractional order PID controller for MIMO power systems

2019 ◽  
Vol 87 ◽  
pp. 01024
Author(s):  
Vasampalli Shashidhar ◽  
Dola Gobinda Padhan
Keyword(s):  
Power Systems ◽  
Fractional Order ◽  
Pid Controller ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Distribution Networks ◽  
Cuckoo Search Algorithm ◽  
Search Optimization ◽  
Control Scheme ◽  
Fractional Order Pid

This paper proposes a novel fractional order PID control scheme for Multi Input and Multi Output (MIMO) power systems. This control scheme utilizes Cuckoo Search algorithm to tune the fractional PID controller to guarantee better closed loop performance in the transmission or distribution networks. Cuckoo Search optimization algorithm is proposed to optimize gain values of the fractional PID controller. The effectiveness of the proposed control strategy has been assessed by simulations in MATLAB/Simulink platform. The robustness of the proposed controller has been validated by varying the plant parameters.

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