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 Path Planning for Autonomous Driving of Mobile Robots using Deep Neural Network based Model Predictive Control

2021 ◽  
Vol 11 (11) ◽  
pp. 31-38
Author(s):  
Kiwon Yeom ◽  
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Model Predictive Control ◽  
Mobile Robot ◽  
Predictive Control ◽  
Deep Neural Network ◽  
Optimal Solution ◽  
Autonomous Driving ◽  
Physical Constraints ◽  
Predictive Controller

A car-like mobile robot is a nonlinear affine system, and the mobile robot has physical constraints such as velocity and acceleration. Thus, no satisfactory solution may not be provided during self-driving under unknown environments. Although Model Predictive Control (MPC) has provided good performance in terms of control strategy, it is difficult to optimize the control parameters due to the uncertainty and non-linearity of a process. In this paper, the Deep Neural Networks (DNN) based Model Predictive Controller (MPC) is derived for tracking the given path during self-driving. The proposed DNN MPC produces the global optimal solution which has better performance than traditional MPC in terms of the errors of position and orientation. This paper verifies that the proposed DNN MPC based controller can track the desired path with high precision for the car-like mobile robot. Keywords—Path planning, autonomous driving, mobile robot, deep neural network, model predictive control.

Nonlinear Parameters and State Estimation for Adaptive Nonlinear Model Predictive Control Design

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Hichem Salhi ◽  
Faouzi Bouani
Keyword(s):  
Model Predictive Control ◽  
State Estimation ◽  
Predictive Control ◽  
Nonlinear Model ◽  
State Space Model ◽  
Nonlinear Model Predictive Control ◽  
Control Law ◽  
Unknown Parameters ◽  
Prediction Horizon ◽  
Predictive Controller

This paper deals with an adaptive nonlinear model predictive control (NMPC) based estimator in cases of mismatch modeling, presence of perturbations and/or parameter variations. Thus, we propose an adaptive nonlinear predictive controller based on the second-order divided difference filter (DDF) for multivariable systems. The controller uses a nonlinear state-space model for parameters and state estimation and for the control law synthesis. Two nonlinear optimization layers are included in the proposed algorithm. The first optimization problem is based on the output error (OE) model with a tuning factor, and it is dedicated to minimize the error between the model and the system at each sample time by estimating unknown parameters when assuming that all system states are available. The second optimization layer is used by the centralized nonlinear predictive controller to generate the control law which minimizes the error between future setpoints and future outputs along the prediction horizon. The proposed algorithm leads to a good tracking performance with an offset-free output and an effectiveness in perturbation attenuation. Practical results on a real setup show the reliability of the proposed approach.

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.

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 Mobile Robot Navigation in a Cluttered Environment via Visual Predictive Control

2020 ◽  
Author(s):  
Adrien Durand-Petiteville ◽  
Viviane Cadenat
Keyword(s):  
Predictive Control ◽  
Mobile Robot Navigation ◽  
Control Law ◽  
Large Set ◽  
Specific System ◽  
Differential Drive ◽  
Cluttered Environment ◽  
Future State ◽  
Predictive Controller ◽  
Differential Drive Robot

This paper presents a Visual Predictive Controller scheme for a differential drive robot navigating in a cluttered environment. We introduce an analytic model predicting the future state for this specific system Moreover, constraints guaranteeing the convergence of the control law, and avoiding occultations and collisions with obstacles are presented. A large set of results obtained in simulations highlights the interest and efficiency of the approach.

Predictive Controller and Estimator for NASA Deep Space Network Antennas

1994 ◽  
Vol 116 (2) ◽  
pp. 241-248 ◽  
Author(s):  
W. Gawronski
Keyword(s):  
Predictive Control ◽  
Tracking Control ◽  
Controller Design ◽  
Control Law ◽  
Deep Space ◽  
Linear Quadratic ◽  
Deep Space Network ◽  
Space Network ◽  
Predictive Controller ◽  
Simulation Results

This paper presents a modified output prediction procedure, and a new controller design based on the predictive control law. Also, a predictive estimator is developed for implementing the controller. The predictive controller was designed and simulated for tracking control of the NASA Deep Space Network 70-m antenna. Simulation results show significant improvement in tracking performance compared to the linear quadratic controller and estimator presently in use.

scholarly journals A modular framework to generate robust biped locomotion: from planning to control

2021 ◽  
Vol 3 (9) ◽  
Author(s):  
Mohammadreza Kasaei ◽  
Ali Ahmadi ◽  
Nuno Lau ◽  
Artur Pereira
Keyword(s):  
Numerical Simulations ◽  
Predictive Control ◽  
Biped Locomotion ◽  
Input Output ◽  
Dynamics Model ◽  
Biped Robots ◽  
The Core ◽  
Simulation Results ◽  
Reference Trajectories ◽  
Modular Framework

AbstractBiped robots are inherently unstable because of their complex kinematics as well as dynamics. Despite many research efforts in developing biped locomotion, the performance of biped locomotion is still far from the expectations. This paper proposes a model-based framework to generate stable biped locomotion. The core of this framework is an abstract dynamics model which is composed of three masses to consider the dynamics of stance leg, torso, and swing leg for minimizing the tracking problems. According to this dynamics model, we propose a modular walking reference trajectories planner which takes into account obstacles to plan all the references. Moreover, this dynamics model is used to formulate the controller as a Model Predictive Control (MPC) scheme which can consider some constraints in the states of the system, inputs, outputs, and also mixed input-output. The performance and the robustness of the proposed framework are validated by performing several numerical simulations using MATLAB. Moreover, the framework is deployed on a simulated torque-controlled humanoid to verify its performance and robustness. The simulation results show that the proposed framework is capable of generating biped locomotion robustly.

Identification and Model Predictive Position Control of Two Wheeled Inverted Pendulum Mobile Robot

2015 ◽  
Vol 73 (6) ◽  
Author(s):  
Amir A. Bature ◽  
Salinda Buyamin ◽  
Mohamad N. Ahmad ◽  
Mustapha Muhammad ◽  
Auwalu A. Muhammad
Keyword(s):  
Mathematical Model ◽  
System Identification ◽  
Mobile Robot ◽  
Inverted Pendulum ◽  
Position Control ◽  
Superior Performance ◽  
System A ◽  
Wheeled Inverted Pendulum ◽  
Simulation Results ◽  
Real Plant

In order to predict and analyse the behaviour of a real system, a simulated model is needed. The more accurate the model the better the response is when dealing with the real plant. This paper presents a model predictive position control of a Two Wheeled Inverted Pendulum robot. The model was developed by system identification using a grey box technique. Simulation results show superior performance of the gains computed using the grey box model as compared to common linearized mathematical model. 

Simulation of a Differential-Drive Wheeled Mobile Lego Robot Mindstorms NXT

2015 ◽  
Vol 776 ◽  
pp. 319-324
Author(s):  
I. Wayan Widhiada ◽  
C.G. Indra Partha ◽  
Yuda A.P. Wayan Reza
Keyword(s):  
Mobile Robot ◽  
3D Model ◽  
Hardware Implementation ◽  
Movement Control ◽  
Common Environment ◽  
Matlab Simulink ◽  
Differential Drive ◽  
Simulation Results ◽  
Mechanical Elements ◽  
Drive Model

The aim of this paper is to model and simulate kinematics motion using the differential drive model of a mobile Lego robot Mindstorm NXT. The author’s use integrated two software as a method to solve the simulation of mobile lego robot mindstorms NXT using Matlab/Simulink and Solidworks software. These softwares are enable easier 3D model creation for both simulation and hardware implementation. A fundamental of this work is the use of Matlab/Simulink Toolboxes to support the simulation and understanding of the various kinematics systems and in particular how the SimMechanics toolbox is used to interface seamlessly with ordinary Simulink block diagrams to enable the mechanical elements and its associated control system elements to be investigated in one common environment. The result of simulation shows the mobile robot movement control based on decentralized point algorithm to follow the precision x and y references that has been specified. The design of the mobile robot is validated in simulation results as proof that this design can achieve the good performance.

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