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.

Effect of Short-Term Weather Predictions on Model Predictive Trajectory Tracking Performance of Unmanned Surface Vessels

2020 ◽  
Author(s):  
Benjamin Armentor ◽  
Joseph Stevens ◽  
Nathan Madsen ◽  
Andrew Durand ◽  
Joshua Vaughan
Keyword(s):  
Trajectory Tracking ◽  
Wind Waves ◽  
Tracking Error ◽  
Tracking Performance ◽  
Model Predictive Controller ◽  
Prediction Horizon ◽  
Surface Vessels ◽  
Waves And Currents ◽  
Predictive Controller ◽  
Disturbance Model

Abstract For mobile robots, such as Autonomous Surface Vessels (ASVs), limiting error from a target trajectory is necessary for effective and safe operation. This can be difficult when subjected to environmental disturbances like wind, waves, and currents. This work compares the tracking performance of an ASV using a Model Predictive Controller that includes a model of these disturbances. Two disturbance models are compared. One prediction model assumes the current disturbance measurements are constant over the entire prediction horizon. The other uses a statistical model of the disturbances over the prediction horizon. The Model Predictive Controller performance is also compared to a PI-controlled system under the same disturbance conditions. Including a disturbance model in the prediction of the dynamics decreases the trajectory tracking error over the entire disturbance spectrum, especially for longer horizon lengths.

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 Spacecraft Relative Motion Maneuvers Using the IPA-SQP Approach

2013 ◽  
Author(s):  
Hyeongjun Park ◽  
Ilya Kolmanovsky ◽  
Jing Sun
Keyword(s):  
Performance Evaluation ◽  
Predictive Control ◽  
Relative Motion ◽  
Sequential Quadratic Programming ◽  
Nonlinear Constraints ◽  
Linear Quadratic ◽  
Model Predictive Controller ◽  
Polyhedral Norm ◽  
Predictive Controller ◽  
Performance Evaluation And Comparison

In this paper, a Model Predictive Controller (MPC) based on the Integrated Perturbation Analysis and Sequential Quadratic Programming (IPA-SQP) is designed and analyzed for spacecraft relative motion maneuvering. To evaluate the effectiveness of the IPA-SQP MPC, the results are compared with the linear quadratic MPC algorithm developed in [4, 13–15]. It is shown that the IPA-SQP algorithm can handle directly nonlinear constraints on thrust magnitude without resorting to saturation or polyhedral norm approximations. Spacecraft fuel consumption related metrics are examined for performance evaluation and comparison.

Controlling of Mobile Robot by Using of Predictive Controller

2017 ◽  
Vol 6 (3) ◽  
pp. 207
Author(s):  
Alireza Rezaee
Keyword(s):  
Mobile Robot ◽  
Weighting Factor ◽  
Superior Performance ◽  
Adaptive Controllers ◽  
Prediction Horizon ◽  
Predictive Controller ◽  
Predictive Controllers ◽  
Signal Filter

In this paper implementation of Model Predictive<br />Controller on mobile robot was explained. The conducted<br />experiments show effectiveness of the proposed method on<br />control of the mobile robot. Furthermore the effects of the model<br />parameters such as control horizon, prediction horizon,<br />weighting factor and signal filter band on the controller<br />performance were studied. Finally, a comparison between the<br />designed MPC controller and PID and adaptive controllers was<br />presented demonstrating superior performance of the Model<br />Predictive Controllers.

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