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Remote UGV Control via Practical Wireless Channels: A Model Predictive Control Approach

inghao Cao, Subhan Khan, Wanchun Liu, Yonghui Li, Branka Vucetic

TL;DR

This paper presents a practical Model Predictive Control (MPC) based control scheme with considerations of of packet dropouts, latency, process noise and measurement noise, and implements the proposed MPC algorithm on a simulated Unmanned Ground Vehicle (UGV) and conducts a series of experiments to evaluate the performance.

Abstract

In addressing wireless networked control systems (WNCS) subject to unexpected packet loss and uncertainties, this paper presents a practical Model Predictive Control (MPC) based control scheme with considerations of of packet dropouts, latency, process noise and measurement noise. A discussion of the quasi-static Rayleigh fading channel is presented herein to enhance the realism of the underlying assumption in a real-world context. To achieve a desirable performance, the proposed control scheme leverages the predictive capabilities of a direct multiple shooting MPC, employs a compensation strategy to mitigate the impact of wireless channel imperfections. Instead of feeding noisy measurements into the MPC, we employ an Extended Kalman Filter (EKF) to mitigate the influence of measurement noise and process disturbances. Finally, we implement the proposed MPC algorithm on a simulated Unmanned Ground Vehicle (UGV) and conduct a series of experiments to evaluate the performance of our control scheme across various scenarios. Through our simulation results and comparative analyses, we have substantiated the effectiveness and improvements brought about by our approach through the utilization of multiple metrics.

Remote UGV Control via Practical Wireless Channels: A Model Predictive Control Approach

TL;DR

This paper presents a practical Model Predictive Control (MPC) based control scheme with considerations of of packet dropouts, latency, process noise and measurement noise, and implements the proposed MPC algorithm on a simulated Unmanned Ground Vehicle (UGV) and conducts a series of experiments to evaluate the performance.

Abstract

In addressing wireless networked control systems (WNCS) subject to unexpected packet loss and uncertainties, this paper presents a practical Model Predictive Control (MPC) based control scheme with considerations of of packet dropouts, latency, process noise and measurement noise. A discussion of the quasi-static Rayleigh fading channel is presented herein to enhance the realism of the underlying assumption in a real-world context. To achieve a desirable performance, the proposed control scheme leverages the predictive capabilities of a direct multiple shooting MPC, employs a compensation strategy to mitigate the impact of wireless channel imperfections. Instead of feeding noisy measurements into the MPC, we employ an Extended Kalman Filter (EKF) to mitigate the influence of measurement noise and process disturbances. Finally, we implement the proposed MPC algorithm on a simulated Unmanned Ground Vehicle (UGV) and conduct a series of experiments to evaluate the performance of our control scheme across various scenarios. Through our simulation results and comparative analyses, we have substantiated the effectiveness and improvements brought about by our approach through the utilization of multiple metrics.
Paper Structure (19 sections, 14 equations, 10 figures, 6 tables, 1 algorithm)

This paper contains 19 sections, 14 equations, 10 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Contributions
  4. Outline
  5. Problem formulation
  6. UGV kinematic model
  7. Mobile robot measurement model
  8. Wireless Communication Channel
  9. Communication Latency
  10. Packet loss model
  11. Controller design
  12. Extended Kalman filter
  13. Results and discussions
  14. Overview
  15. Experiment 1: Circular curve reference tracking
  16. ...and 4 more sections

Figures (10)

  • Figure 1: The schematic diagram of the proposed WNCS with communication imperfections illustrated in red.
  • Figure 2: Delay and packet loss sequences of the S-C and C-A channels
  • Figure 3: (a) Avoid the first MO by predicting its motion. (b) Avoid the second MO by fast orientation steering. (c) Avoid the first SO and catching up with the reference. (d), (e) Avoid two more SO and get back to the track. (f) Reach the goal point safely.
  • Figure 4: Experiment 1: Comparing of the performance of different methods
  • Figure 5: (a) Avoiding the rear of the first MO and the first SO. (b) Avoiding the second MO by predicting the motion of it. (c) Avoid the second SO and slow down to avoid collision with the third MO. (d), (e) Avoid the third and forth SO and get back to the reference track. (f) Safely reach the goal
  • ...and 5 more figures