An on-line adaptive hybrid PID autopilot of ship heading control using auto-tuning BP & RBF neurons

2020 ◽  
Author(s):  
Guichen Zhang ◽  
Mengwei Chen ◽  
Run Lu
Keyword(s):  
Heading Control ◽  
On Line ◽  
Auto Tuning

Auto-Tuning Digital Control Based on Parameters Identification and Disturbance Observer

2006 ◽  
Vol 505-507 ◽  
pp. 529-534 ◽  
Author(s):  
Chin Sheng Chen ◽  
Yu Reng Lee
Keyword(s):  
Controller Design ◽  
Tracking Error ◽  
Parameters Identification ◽  
Least Square ◽  
Servo Motor ◽  
Recursive Least Square ◽  
Estimated Parameters ◽  
On Line ◽  
Feedforward Controller ◽  
Auto Tuning

This paper presented a digital servo driver that realizes an auto-tuning feedback and feedforward controller design using on-line parameters identification. Firstly, the variant inertia constant, damping constant and the disturbed load torque of the servo motor are estimated by the recursive least square (RLS) estimator, which is composed of an RLS estimator and a disturbance torque compensator. Furthermore, the auto-tuning algorithm of feedback and feedforward controller is realized according to the estimated parameters to match the tracking specification. The proposed auto-tuning digital servo controllers are evaluated and compared experimentally with a traditional controller on a microcomputer-controlled servo motor positioning system. The experimental results show that this auto-tuning digital servo system remarkably reduces the tracking error.

scholarly journals Predictive Nonlinear PID Neural Voltage-Tracking Controller Design for Fuel Cell based on Optimization Algorithm

2019 ◽  
pp. 47-60
Keyword(s):  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Controller Design ◽  
Proton Exchange ◽  
Optimal Weights ◽  
Voltage Controller ◽  
Tracking Controller ◽  
On Line ◽  
Auto Tuning ◽  
Chaotic Particle Swarm Optimization

This paper proposes a predictive nonlinear PID neural voltage-tracking controller design for Proton Exchange Membrane Fuel Cell (PEMFC) Model with an on-line auto-tuning intelligent algorithm. The purpose of the proposed robust feedback nonlinear PID neural predictive voltage controller is to find the optimal value of the hydrogen partial pressure action in order to control the stack terminal voltage of the (PEMFC) model for one-step-ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) is utilized as a stable and intelligent robust on-line auto-tuning algorithm to obtain the near-optimal weights for the proposed controller so as to improve the performance index of the system as well as to minimize the energy consumption. The Simulation results demonstrated the effectiveness of the proposed controller compared with the linear PID neural controller.

On-Line Genetic Auto-Tuning of PID Controllers for an Active Magnetic Bearing Application

1998 ◽  
Vol 31 (4) ◽  
pp. 207-209 ◽  
Author(s):  
P. Schroder ◽  
B. Green ◽  
N. Gnum ◽  
P.J. Fleming
Keyword(s):  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Pid Controllers ◽  
On Line ◽  
Auto Tuning
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