Mobile Robot Motion Control Using Laguerre-Based Model Predictive Control

2015 ◽  
Vol 776 ◽  
pp. 403-410 ◽  
Author(s):  
Mapopa Chipofya ◽  
Deok Jin Lee ◽  
Kil To Chong
Keyword(s):  
Mobile Robot ◽  
Linear Model ◽  
Motion Control ◽  
Predictive Control ◽  
Robot Motion ◽  
Laguerre Functions ◽  
Model Predictive Controller ◽  
Cartesian System ◽  
Predictive Controller ◽  
Simulation Results

This paper presents a method of solving the problem of mobile robot motion control using a model predictive controller designed using Laguerre functions. A linear model of the two-wheeled nonholonomic robot is used. This linear model is obtained by converting the nonlinear model in the Cartesian system to a polar one. This change is preferred because it is easier to work with the linear model than its corresponding nonlinear one. Simulation results obtained from MATLAB showing that Laguerre-based MPC (LMPC) performs well are presented.

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.

Model Predictive Control of a Differential-Drive Mobile Robot

2018 ◽  
Vol 10 (1) ◽  
pp. 20-41
Author(s):  
Samir Bouzoualegh ◽  
El-Hadi Guechi ◽  
Ridha Kelaiaia
Keyword(s):  
Mathematical Model ◽  
Model Predictive Control ◽  
Mobile Robot ◽  
Predictive Control ◽  
Control Law ◽  
Input Output ◽  
Linearization Technique ◽  
Differential Drive ◽  
Predictive Controller ◽  
Simulation Results

Abstract This paper presents a model predictive control (MPC) for a differential-drive mobile robot (DDMR) based on the dynamic model. The robot’s mathematical model is nonlinear, which is why an input–output linearization technique is used, and, based on the obtained linear model, an MPC was developed. The predictive control law gains were acquired by minimizing a quadratic criterion. In addition, to enable better tuning of the obtained predictive controller gains, torques and settling time graphs were used. To show the efficiency of the proposed approach, some simulation results are provided.

scholarly journals Motion Planning for Mobile Robot with Modified BIT* and MPC

2021 ◽  
Vol 11 (1) ◽  
pp. 426
Author(s):  
Puyong Xu ◽  
Ning Wang ◽  
Shi-Lu Dai ◽  
Lei Zuo
Keyword(s):  
Model Predictive Control ◽  
Mobile Robot ◽  
Motion Planning ◽  
Objective Function ◽  
Free Path ◽  
Predictive Control ◽  
Planning Method ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Simulation Results

In this paper, a mobile robot motion planning method with modified BIT* (batch informed trees) and MPC (Model Predictive Control) is presented. The conventional BIT* was modified here by integrating a stretch method that improves the path points connections, to get a collision-free path more quickly. After getting a reference path, the MPC method is employed to determine the motion at each moment with a given objective function. In the objective function, a repulsive function based on the direction and distance of the obstacles is introduced to avoid the robot being too close to the obstacle, so the safety can be ensured. Simulation results show the good navigation performance of the whole framework in different scenarios.

Motion Control of Mobile Robot Based on Immune Clonal Algorithm Evolved by Virus

2011 ◽  
Vol 143-144 ◽  
pp. 269-273
Author(s):  
Yi Shen ◽  
Qi Wang ◽  
Zhao Li Ye ◽  
Juan Chen ◽  
Ming Xin Yuan
Keyword(s):  
Virus Infection ◽  
Local Search ◽  
Mobile Robot ◽  
Motion Control ◽  
Control Strategy ◽  
Pid Control ◽  
Population Diversity ◽  
Robot Motion ◽  
Control Precision ◽  
Simulation Results

To solve the motion control of mobile robot, a PID control optimized by improved immune clonal algorithm is presented. On the basis of immune clonal algorithm, a new virus evolutionary clonal algorithm (VECA) is provided firstly. The VECA focuses on the virus infection of population after immune mutation, which improves the population diversity and strengths the local search ability of immune clonal algorithm. Then the proposed VECA is used to optimize the algorithm parameters of PID control strategy for the robot motion. The simulation results show that VECA can realize the optimization of PID parameters and improve the control precision of path track.

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