LabVIEW based linear quadratic regulator and model predictive controller for DC motor and flexible link manipulator

2017 ◽  
Author(s):  
Yograj Sharma ◽  
Jyoti Ohri
Keyword(s):  
Linear Quadratic Regulator ◽  
Dc Motor ◽  
Flexible Link ◽  
Linear Quadratic ◽  
Model Predictive Controller ◽  
Predictive Controller

scholarly journals Trajectory Tracking and Stabilization of a Quadrotor Using Model Predictive Control of Laguerre Functions

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Mapopa Chipofya ◽  
Deok Jin Lee ◽  
Kil To Chong
Keyword(s):  
State Space ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Linear Quadratic Regulator ◽  
Laguerre Functions ◽  
Linear Quadratic ◽  
Model Predictive Controller ◽  
Predictive Controller ◽  
Linear State ◽  
Quadrotor System

This paper presents a solution to stability and trajectory tracking of a quadrotor system using a model predictive controller designed using a type of orthonormal functions called Laguerre functions. A linear model of the quadrotor is derived and used. To check the performance of the controller we compare it with a linear quadratic regulator and a more traditional linear state space MPC. Simulations for trajectory tracking and stability are performed in MATLAB and results provided in this paper.

scholarly journals The linear quadratic regular algorithm-based control system of the direct current motor

2019 ◽  
Vol 10 (2) ◽  
pp. 768
Author(s):  
Trong-Thang Nguyen
Keyword(s):  
Control System ◽  
Direct Current ◽  
State Space Model ◽  
Linear Quadratic Regulator ◽  
Response Speed ◽  
Dc Motor ◽  
Linear Quadratic ◽  
Lqr Controller ◽  
Mathematical Equations ◽  
Feed Forward Controller

<span>This research aims to propose an optimal controller for controlling the speed of the Direct Current (DC) motor. Based on the mathematical equations of DC Motor, the author builds the equations of the state space model and builds the linear quadratic regulator (LQR) controller to minimize the error between the set speed and the response speed of DC motor. The results of the proposed controller are compared with the traditional controllers as the PID, the feed-forward controller. The simulation results show that the quality of the control system in the case of LQR controller is much higher than the traditional controllers. The response speed always follows the set speed with the short conversion time, there isn't overshoot. The response speed is almost unaffected when the torque impact on the shaft is changed.</span>

Comparison of Performance Measures of Speed Control for a DC Motor Using Hybrid Intelligent Controller and Optimal LQR

2014 ◽  
Vol 622 ◽  
pp. 23-31
Author(s):  
T. Velayudham Narmadha ◽  
Chackaravarthy Baskaran ◽  
K. Sivakumar
Keyword(s):  
Rise Time ◽  
Linear Quadratic Regulator ◽  
Speed Control ◽  
Dc Motor ◽  
Pid Controllers ◽  
Rule Base ◽  
Fuzzy Pid ◽  
Linear Quadratic ◽  
Lqr Controller ◽  
Fuzzy Pd

-In this paper , performance of fuzzy PD , fuzzy PI , fuzzy PD+I , fuzzy PID controllers are evaluated and compared. This paper also describes the speed control based on Linear Quadratic Regulator (LQR) technique. The comparison is based on their ability of controlling the speed of DC motor, which merely focuses on performance index of the controllers, and also time domain specifications such as rise time, settling time and peak overshoot. The controller is modelled using MATLAB software, the simulation results shows that the fuzzy PID controllers are the best performing candidates in all aspects but it as higher overshoot and IAE in comparison with optimal LQR. The Fuzzy PI controller exhibited null offset but suffers from poor stability and peak overshoot, whereas the fuzzy PD controller has fast rise time, with no overshoots but the IAE is much greater. Thus, the comparative analysis recommends fuzzy PID controller but it is usually associated with complicated rule base and tedious tuning. To circumvent these problems, the proposed LQR controller gives better performance than the other controllers.

The Design of Hopping Angle Control by Using PID and LQR Techniques

2013 ◽  
Vol 419 ◽  
pp. 693-700
Author(s):  
Saifullah Samo ◽  
Shu Yuan Ma ◽  
Bdran Sameh
Keyword(s):  
Pid Controller ◽  
Linear Quadratic Regulator ◽  
Dc Motor ◽  
Linear Quadratic ◽  
Matlab Simulink ◽  
Hopping Robot ◽  
Hopping Robots ◽  
Obstacle Height

It is very difficult for hopping robots to follow the trajectory without controlling hopping angle. A hopping angle controller is designed for combustion piston type hopping robot to adjust the angle of hop which is required to achieve a desired distance or height. So, the controller adds functionality to hopping robot for altering the hopping angle during operation according to obstacle height and obstacle distance. A proportional Integrated Derivative (PID) and Linear Quadratic Regulator (LQR) are designed and compared for adjusting hopping angle by using MATLAB / SIMULINK environment. As result, both controllers are capable to control hopping angle but PID gives better performance. An implementation of PID controller for the hopping angle control is given by using a DC motor. The experiment also carried out on prototype by using PID controller and found satisfactory results.

scholarly journals Analyze and Evaluate the Performance Velocity Control in DC Motor

2020 ◽  
Vol 12 (4) ◽  
pp. 507-516
Author(s):  
Hazim M. Alkargole ◽  
◽  
Abbas S. Hassan ◽  
Raoof T. Hussein ◽  
◽  
...  
Keyword(s):  
Fuzzy Logic Controller ◽  
Linear Quadratic Regulator ◽  
Dc Motor ◽  
Rule Base ◽  
Velocity Control ◽  
Linear Quadratic ◽  
Proportional Integral ◽  
Motor Controller ◽  
Different Types ◽  
Rule Bases

A mathematical model of controlling the DC motor has been applied in this paper. There are many and different types of controllers have been used with purpose of analyzing and evaluating the performance of the of DC motor which are, Fuzzy Logic Controller (FLC), Linear Quadratic Regulator (LQR), Fuzzy Proportional Derivative (FPD) ,Proportional Integral Derivative (PID), Fuzzy Proportional Derivative with integral (FPD plus I) , and Fuzzy Proportional Integral (FPI) with membership functions of 3*3, 5*5, and 7*7 rule bases. The results show that the (FLC) controller with 5*5 rule base provides the best results among all the other controllers to design the DC motor controller.

scholarly journals Model predictive Controller for Mobile Robot

2017 ◽  
Vol 2 (2) ◽  
pp. 18 ◽  
Author(s):  
Alireza Rezaee
Keyword(s):  
Mobile Robot ◽  
Adaptive Controller ◽  
Quadratic Equation ◽  
Linear Quadratic ◽  
Model Predictive Controller ◽  
Prediction Horizon ◽  
Future Behavior ◽  
Ricatti Equation ◽  
Predictive Controller ◽  
Real Robot

This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is implemented on a real robot. The comparison between a PID controller, adaptive controller, and the MPC illustrates advantage of the designed controller and its ability for exact control of the robot on a specified guide path.

An implementable stabilizing model predictive controller applied to a rotary flexible link: An experimental case study

2020 ◽  
Vol 99 ◽  
pp. 104396
Author(s):  
Bernardo P.M. Silva ◽  
Bruno A. Santana ◽  
Tito L.M. Santos ◽  
Márcio A.F. Martins
Keyword(s):  
Flexible Link ◽  
Model Predictive Controller ◽  
Predictive Controller
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