scholarly journals ALGORITHM FOR SELF-TUNING THE PID CONTROLLER

2021 ◽  
Vol XXVIII (4) ◽  
pp. 63-73
Author(s):  
Irina Cojuhari ◽  
Keyword(s):  
Pid Controller ◽  
Optimization Method ◽  
Open Loop ◽  
Open Loop System ◽  
Stability Degree ◽  
Maximum Stability ◽  
Self Tuning ◽  
Experimental Response ◽  
Second Order Systems ◽  
Parametrical Optimization

An algorithm for self-tuning the PID controller to the second order systems is proposed in this paper. The proposed self-tuning procedure was developed according to the maximum stability degree criterion, the criterion that permits to achieve the high stability degree, good performance and robustness of the system. According to the proposed algorithm, the controller can be tuned according to the parameters that characterize the process and they can be determinate from the experimental response of the open loop system. To demonstrate the efficiency of proposed procedure of self-tuning the PID controller, the computer simulation was performed and the obtained results were compared with Haeri’s method, maximum stability degree method with iterations and parametrical optimization method. According to the developed algorithm, it was performed the control of the thermal regime in the oven.

Research on Novel Self-Spinning High Speed On/Off Valve Based on Fuzzy-Logic Parameter Self-Tuning PID Controller

2012 ◽  
Vol 468-471 ◽  
pp. 1448-1452 ◽  
Author(s):  
Jian Chen ◽  
Jian Ping Shu ◽  
Mian Li ◽  
Qi Zhou ◽  
Zhu Ming Su
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Pid Controller ◽  
High Speed ◽  
Position Control ◽  
Open Loop ◽  
Axial Position ◽  
Valve Body ◽  
Loop Control ◽  
Self Tuning

The flow rate of a novel self-spinning high speed hydraulic on/off valve is regulated by changing the relative axial position (the duty cycle) of the valve spool to the nozzles located on the valve body through driving a gerotor pump using brushless direct current motor. The closed loop axial position control system of the valve spool with feedback of the axial displacement of the valve spool has shorter response time, and not more ripple of output pressure than corresponding open loop control system. A fuzzy logic parameter self-tuning PID controller is investigated to overcome nonlinearity of the control system. The simulation results show that the overshoot is decreased greatly than conventional PID controller.

Cost of Controls for Multi-Rotor Drones

2021 ◽  
Author(s):  
Samantha Hoang ◽  
I. Y. Shen
Keyword(s):  
Energy Efficient ◽  
Pid Controller ◽  
Small Amplitude ◽  
High Amplitude ◽  
Open Loop ◽  
Loop System ◽  
Energy Costs ◽  
Pd Controller ◽  
Open Loop System ◽  
Integral Control

Abstract This paper focuses on the energy costs of using a control system to follow a specific trajectory as opposed to directly choosing the inputs in an open-loop sense to achieve a specific trajectory. Results show that the PID controller will use 7–13% more energy than the open-loop system for the small-amplitude trajectories and 62–74% more energy for the high-amplitude trajectories while maintaining trajectory errors of approximately 5% and 19%, respectively. The PD controller will use 0.03–0.04% less energy than the open loop system for the small-amplitude trajectories and 0.35% less energy for the high-amplitude trajectories while maintaining similar trajectory errors to the PID controller. The two trajectories have different trajectory errors (5% vs. 19%). Therefore, the controller does not have to move the rotor speeds to the same level, thus causing more energy saved for the large-amplitude trajectory. Based on these simulations, it can be concluded that the PD controller will be significantly more energy efficient than the PID controller while maintaining similar trajectory errors throughout the entire flight. This is because the integral control term in the PID controller accumulates the error so any initial large errors in the position will result in large controller inputs later in the flight. The accumulation of error in the integral term then results in large variations in rotor speed which often leads to a higher energy usage. Thus, a PD controller may be a more energy efficient choice while maintaining effectiveness in trajectory-tracking. These results highlight the importance of considering and comparing the energy costs of various control systems when designing controllers for multi-rotor drones.

Multi-Objective Optimization of the Electromagnetic Actuator Control

2013 ◽  
Vol 198 ◽  
pp. 553-558
Author(s):  
Sebastian Pecolt ◽  
Tomasz Krzyżyński
Keyword(s):  
Pid Controller ◽  
Feedback Loop ◽  
Open Loop ◽  
Multi Objective Optimization ◽  
Fixed Position ◽  
Open Loop System ◽  
Electromagnetic Brake ◽  
Neodymium Magnet ◽  
Linear Electromagnetic Motor ◽  
Actuator Control

This paper presents the linear electromagnetic motor (LEM) which consists of two solenoids and a slide control with neodymium magnet bars placed on its end. The work of the electromagnetic motor is based on the phenomenon of electromagnetic repulsion. The device also includes an electromagnetic brake which allows to stop the slider or to hold the slider in a fixed position, without electricity supply. The range of the slider movement is up to 50mm. The paper deals with the problem of implementation of the Genetic Algorithm GA to calculate Pareto-optimal solutions. In this process, the functions of voltage powering solenoids used to control the position of the linear electromagnetic motor are evaluated. This procedure assumes minimizing two conflicted criteria: the time of movement of the motor and the energy input. In this case the device works in the open-loop system without a feedback loop. The results of the application of the conventional PID controller are also presented, which allows precision positioning up to 1µm. The experimental results are compared with Matlab-Simulink simulations.

Design of a Data-Driven Multi-loop Self-Tuning PID Controller

2019 ◽  
Vol 139 (4) ◽  
pp. 356-363
Author(s):  
Yoichiro Ashida ◽  
Shin Wakitani ◽  
Toru Yamamoto
Keyword(s):  
Pid Controller ◽  
Data Driven ◽  
Self Tuning
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