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Comparative Analysis of NMPC and Fuzzy PID Controllers for Trajectory Tracking in Omni-Drive Robots: Design, Simulation, and Performance Evaluation

Love Panta

TL;DR

A self-optimizing controller, Type-1 fuzzyPID, is introduced, which leverages dynamic and static system response analysis to overcome the limitations of manual tuning and validate the precision and effectiveness of NMPC over fuzzyPID controllers while trading computational complexity.

Abstract

Trajectory tracking for an Omni-drive robot presents a challenging task that demands an efficient controller design. This paper introduces a self-optimizing controller, Type-1 fuzzyPID, which leverages dynamic and static system response analysis to overcome the limitations of manual tuning. To account for system uncertainties, an Interval Type-2 fuzzyPID controller is also developed. Both controllers are designed using Matlab/Simulink and tested through trajectory tracking simulations in the CoppeliaSim environment. Additionally, a non-linear model predictive controller(NMPC) is proposed and compared against the fuzzyPID controllers. The impact of tunable parameters on NMPC tracking accuracy is thoroughly examined. We also present plots of the step-response characteristics and noise rejection experiments for each controller. Simulation results validate the precision and effectiveness of NMPC over fuzzyPID controllers while trading computational complexity. Access to code and simulation environment is available in the following link: https://github.com/love481/Omni-drive-robot-Simulation.git.

Comparative Analysis of NMPC and Fuzzy PID Controllers for Trajectory Tracking in Omni-Drive Robots: Design, Simulation, and Performance Evaluation

TL;DR

A self-optimizing controller, Type-1 fuzzyPID, is introduced, which leverages dynamic and static system response analysis to overcome the limitations of manual tuning and validate the precision and effectiveness of NMPC over fuzzyPID controllers while trading computational complexity.

Abstract

Trajectory tracking for an Omni-drive robot presents a challenging task that demands an efficient controller design. This paper introduces a self-optimizing controller, Type-1 fuzzyPID, which leverages dynamic and static system response analysis to overcome the limitations of manual tuning. To account for system uncertainties, an Interval Type-2 fuzzyPID controller is also developed. Both controllers are designed using Matlab/Simulink and tested through trajectory tracking simulations in the CoppeliaSim environment. Additionally, a non-linear model predictive controller(NMPC) is proposed and compared against the fuzzyPID controllers. The impact of tunable parameters on NMPC tracking accuracy is thoroughly examined. We also present plots of the step-response characteristics and noise rejection experiments for each controller. Simulation results validate the precision and effectiveness of NMPC over fuzzyPID controllers while trading computational complexity. Access to code and simulation environment is available in the following link: https://github.com/love481/Omni-drive-robot-Simulation.git.
Paper Structure (13 sections, 18 equations, 12 figures, 4 tables)

This paper contains 13 sections, 18 equations, 12 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Kinematics model
  3. Path Planning
  4. Design of tracking Controller
  5. FuzzyPID controller
  6. Non-linear MPC
  7. Simulation setup
  8. Comparison of fuzzyPIDs and NMPC
  9. In Absence of Noise
  10. Random Noise Injection
  11. Step Response Analysis
  12. Conclusions
  13. Limitations and Future Enhancements

Figures (12)

  • Figure 1: Component division of translational velocity of omni wheel
  • Figure 2: Kinematic diagram of four-wheel omni robot
  • Figure 3: Trajectory tracking error for Omni-drive robot
  • Figure 4: The range of triangular membership function are adjusted based on input and output variables. Both input variables $e$ and $de$ are in the range between -1 to 1. For output variables ($kp, ki, kd$), range is adjusted in between -0.1 to 0.1 keeping the structure of membership function similar.
  • Figure 5: Comparison of tracking performance of pose(XY) for both type fuzzyPIDs and NMPC with prediction horizon of 15 with a total tracking time of 30 seconds
  • ...and 7 more figures