A neural network‐based design method of the fractional order PID controller for a class of motion control systems

2021 ◽  
Author(s):  
Weijia Zheng ◽  
YangQuan Chen ◽  
Xiaohong Wang ◽  
Meijin Lin
Keyword(s):  
Neural Network ◽  
Control Systems ◽  
Motion Control ◽  
Fractional Order ◽  
Pid Controller ◽  
Design Method ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller

An Enhanced Robust Fractional Order PID Controller for Electric Machinery System: Tuning Rules and Simulations

2015 ◽  
Author(s):  
Chunyang Wang ◽  
Meng Wu ◽  
Nianchun Cai ◽  
Xuelian Liu ◽  
Chengjun Tian
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Optimal Parameter ◽  
Design Method ◽  
Open Loop ◽  
Electrical Machinery ◽  
Integer Order ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller ◽  
Machinery System

A design method of enhanced robust fractional order PID controller is proposed to control electrical machinery system. Magnitude margin constraint, phase margin constraint and the gain robustness constraints of partly flat phase in specified dots around crossover frequency are adopted to design enhanced robust fractional order PID controller which has stronger robustness to open-loop gain variation compared with integer order PID controller. Besides, nonlinear optimization function is adopted to hunt for optimal parameter solutions of enhanced robust fractional order PID controller, so the five parameters of enhanced robust fractional order PID controller can be solved. The electrical machinery control system models are simulated and tested by MATLAB/SIMULINK, and the results show that the proposed fractional order PID controller has stronger robustness and smaller overshoot, compared with integer order PID controller.

scholarly journals A Tuning Method via Borges Derivative of a Neural Network-Based Discrete-Time Fractional-Order PID Controller with Hausdorff Difference and Hausdorff Sum

2021 ◽  
Vol 5 (1) ◽  
pp. 23
Author(s):  
Zhe Gao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fractional Order ◽  
Discrete Time ◽  
Pid Controller ◽  
Order Derivative ◽  
Composite Function ◽  
Fractal Derivative ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller

In this paper, the fractal derivative is introduced into a neural network-based discrete-time fractional-order PID controller in two areas, namely, in the controller’s structure and in the parameter optimization algorithm. The first use of the fractal derivative is to reconstruct the fractional-order PID controller by using the Hausdorff difference and Hausdorff sum derived from the Hausdorff derivative and Hausdorff integral. It can avoid the derivation of the Gamma function for the order updating to realize the parameter and order tuning based on neural networks. The other use is the optimization of order and parameters by using Borges derivative. Borges derivative is a kind of fractal derivative as a local fractional-order derivative. The chain rule of composite function is consistent with the integral-order derivative. It is suitable for updating the parameters and the order of the fractional-order PID controller based on neural networks. This paper improves the neural network-based PID controller in two aspects, which accelerates the response speed and improves the control accuracy. Two illustrative examples are given to verify the effectiveness of the proposed neural network-based discrete-time fractional-order PID control scheme with fractal derivatives.

scholarly journals Kalman Filter and Variants for Estimation in 2DOF Serial Flexible Link and Joint Using Fractional Order PID Controller

2021 ◽  
Vol 11 (15) ◽  
pp. 6693
Author(s):  
Sagar Gupta ◽  
Abhaya Pal Singh ◽  
Dipankar Deb ◽  
Stepan Ozana
Keyword(s):  
Kalman Filter ◽  
Fractional Order ◽  
Pid Controller ◽  
Degree Of Freedom ◽  
Flexible Link ◽  
System Properties ◽  
Tool Position ◽  
Fractional Order Pid ◽  
Two Degree Of Freedom ◽  
Fractional Order Pid Controller

Robotic manipulators have been widely used in industries, mainly to move tools into different specific positions. Thus, it has become necessary to have accurate knowledge about the tool position using forward kinematics after accessing the angular locations of limbs. This paper presents a simulation study in which an encoder attached to the limbs gathers information about the angular positions. The measured angles are applied to the Kalman Filter (KF) and its variants for state estimation. This work focuses on the use of fractional order controllers with a Two Degree of Freedom Serial Flexible Links (2DSFL) and Two Degree of Freedom Serial Flexible Joint (2DSFJ) and undertakes simulations with noise and a square wave as input. The fractional order controllers fit better with the system properties than integer order controllers. The KF and its variants use an unknown and assumed process and measurement noise matrices to predict the actual data. An optimisation problem is proposed to achieve reasonable estimations with the updated covariance matrices.

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