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.

Wavelet neural network-based H∞trajectory tracking for robot manipulators using fast terminal sliding mode control

Robotica ◽  
2016 ◽  
Vol 35 (7) ◽  
pp. 1488-1503 ◽  
Author(s):  
Vikas Panwar
Keyword(s):  
Trajectory Tracking ◽  
Finite Time ◽  
Sliding Mode ◽  
Tracking Error ◽  
Tracking Performance ◽  
Robot Dynamics ◽  
Terminal Sliding Mode Control ◽  
Sliding Surface ◽  
Terminal Sliding Mode ◽  
Fast Terminal Sliding Mode

SUMMARYThis paper focuses on fast terminal sliding mode control (FTSMC) of robot manipulators using wavelet neural networks (WNN) with guaranteed H∞tracking performance. The FTSMC for trajectory tracking is employed to drive the tracking error of the system to converge to an equilibrium point in finite time. The tracking error arrives at the sliding surface in finite time and then converges to zero in finite time along the sliding surface. To deal with the case of uncertain and unknown robot dynamics, a WNN is proposed to fully compensate the robot dynamics. The online tuning algorithms for the WNN parameters are derived using Lyapunov approach. To attenuate the effect of approximation errors to a prescribed level, H∞tracking performance is proposed. It is shown that the proposed WNN is able to learn the system dynamics with guaranteed H∞tracking performance and finite time convergence for trajectory tracking. Finally, the simulation results are performed on a 3D-Microbot manipulator to show the effectiveness of the 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.

scholarly journals Development of Model Predictive Controller for a Tail-Sitter VTOL UAV in Hover Flight

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2859 ◽  
Author(s):  
Boyang Li ◽  
Weifeng Zhou ◽  
Jingxuan Sun ◽  
Chih-Yung Wen ◽  
Chih-Keng Chen
Keyword(s):  
High Efficiency ◽  
Position Control ◽  
State Space Model ◽  
Nonlinear Dynamic Model ◽  
Model Predictive Controller ◽  
Position Controller ◽  
Predictive Controller ◽  
Hover Flight ◽  
Disturbance Model ◽  
Six Degree Of Freedom

This paper presents a model predictive controller (MPC) for position control of a vertical take-off and landing (VTOL) tail-sitter unmanned aerial vehicle (UAV) in hover flight. A ‘cross’ configuration quad-rotor tail-sitter UAV is designed with the capabilities for both hover and high efficiency level flight. The six-degree-of-freedom (DOF) nonlinear dynamic model of the UAV is built based on aerodynamic data obtained from wind tunnel experiments. The model predictive position controller is then developed with the augmented linearized state-space model. Measured and unmeasured disturbance model are introduced into the modeling and optimization process to improve disturbance rejection ability. The MPC controller is first verified and tuned in the hardware-in-loop (HIL) simulation environment and then implemented in an on-board flight computer for real-time indoor experiments. The simulation and experimental results show that the proposed MPC position controller has good trajectory tracking performance and robust position holding capability under the conditions of prevailing and gusty winds.

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