Joint Confidence Region for the Tuning Parameters of the PID Controller

2012 ◽  
Vol 45 (3) ◽  
pp. 727-732
Author(s):  
Nilton Silva ◽  
Heleno Bispo ◽  
Romildo Brito ◽  
João Manzi
Keyword(s):  
Pid Controller ◽  
Confidence Region ◽  
Tuning Parameters ◽  
Joint Confidence Region

PSO-Based Tunning of PID Controller for Speed Control of DC Motor

2014 ◽  
Vol 7 (3) ◽  
pp. 65-79
Author(s):  
Ibrahem S. Fatah
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Dc Motor ◽  
Proportional Integral Derivative ◽  
Stable Convergence ◽  
Swarm Optimization ◽  
Tuning Parameters ◽  
Pid Controller Tuning ◽  
Rising Time

In this paper, a Proportional-Integral-Derivative (PID) controller of DC motor is designed by using particle swarm optimization (PSO) strategy for formative optimal PID controller tuning parameters. The proposed approach has superior feature, including easy implementation, stable convergence characteristics and very good computational performances efficiency. The DC Motor Scheduling PID-PSO controller is modeled in MATLAB environment. Comparing with conventional PID controller using Genetic Algorithm, the planned method is more proficient in improving the speed loop response stability, the steady state error is reduced, the rising time is perfected and the change of the required input do not affect the performances of driving motor with no overtaking.

scholarly journals Decoupling Fuzzy-Neural PID Controller for TITO Nonlinear Systems

2016 ◽  
Vol 14 (2) ◽  
pp. 12-19 ◽  
Author(s):  
I. Ganchev ◽  
S. Ahmed ◽  
A. Taneva ◽  
M. Petrov
Keyword(s):  
Nonlinear System ◽  
Pid Controller ◽  
Fuzzy Pid ◽  
Neural Structure ◽  
Tuning Parameters ◽  
Fuzzy Pid Controller ◽  
Fuzzy Neural ◽  
Design Paradigm ◽  
Self Tuning ◽  
Set Up

AbstractThis paper presents a fuzzy-neural structure of a Decoupling Fuzzy PID controller with self-tuning parameters. This structure is appropriate for Two-Input-Two-Output (TITO) nonlinear system. The main advantage here is that the equation of classical PID control and decoupling coefficients are used as a Sugeno function into the fuzzy rules. Hence the designed decoupling fuzzy PID controller can be viewed as a natural similarity to the conventional one with decoupling elements. A benchmark quadruple tank, implementing a TITO nonlinear system is considered to illustrate the benefits of the design paradigm. The performance of this set up was studied for reference tracking and disturbance rejection cases. Simulation results confirm the effectiveness of the proposed solution.

scholarly journals PID Control for Electric Vehicles Subject to Control and Speed Signal Constraints

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Amanda Danielle O. da S. Dantas ◽  
André Felipe O. de A. Dantas ◽  
João Tiago L. S. Campos ◽  
Domingos L. de Almeida Neto ◽  
Carlos Eduardo T. Dórea
Keyword(s):  
Angular Velocity ◽  
Electric Vehicles ◽  
Pid Control ◽  
Pid Controller ◽  
Dc Motor ◽  
Invariant Set ◽  
Velocity Signal ◽  
Tuning Parameters ◽  
Multiparametric Programming ◽  
Control Actions

A PID control for electric vehicles subject to input armature voltage and angular velocity signal constraints is proposed. A PID controller for a vehicle DC motor with a separately excited field winding considering the field current constant was tuned using controlled invariant set and multiparametric programming concepts to consider the physical motor constraints as angular velocity and input armature voltage. Additionally, the integral of the error, derivative of the error constraints, and λ were considered in the proposed algorithm as tuning parameters to analyze the DC motor dynamic behaviors. The results showed that the proposed algorithm can be used to generate control actions taking into account the armature voltage and angular velocity limits. Also, results demonstrate that a controller subject to constraints can improve the electric vehicle DC motor dynamic; and at the same time it protects the motor from overvoltage.

Response of a Maglev Vehicle Moving on a Two-Span Flexible Guideway

2010 ◽  
Vol 26 (1) ◽  
pp. 95-103 ◽  
Author(s):  
J. D. Yau
Keyword(s):  
Pid Controller ◽  
Degrees Of Freedom ◽  
Magnetic Force ◽  
Static Load ◽  
Interaction Analysis ◽  
Equations Of Motion ◽  
Inertial Force ◽  
Tuning Parameters ◽  
Maglev Vehicle ◽  
Electrodynamic Suspension

AbstractThis paper is intended to present a preliminary framework for dynamic interaction analysis of a maglev (magnetically levitated) vehicle running on a two-span guideway using a comprehensive iterative approach. A maglev vehicle with electrodynamic suspension (EDS) system is simplified as a two degrees-of-freedom (2-DOF) maglev oscillator tuned by a PID (Proportional-Integral-Derivative) controller. The guideway is modeled as a two-span continuous beam with uniform section. Two sets of equations of motion are written, with the first set for the guideway and the second set for the maglev oscillator traveling on the guideway through a motion-dependent magnetic force. To achieve the stable levitation gap for a maglev vehicle moving on a flexible guideway, Ziegler-Nicholas (Z-N) tuning rules are used to determine the tuning parameters of the PID controller. Numerical simulations demonstrate that the levitation gap affects the dynamic response of the maglev vehicle while little influence on the guideway response since the inertial force of the moving maglev vehicle is much lower than its static load.

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