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Journal of Intelligent & Robotic Systems

, Volume 83, Issue 3–4, pp 585–602 | Cite as

Energy Consumption Optimization for Mobile Robots Motion Using Predictive Control

  • Mostafa I. Yacoub
  • Dan S. Necsulescu
  • Jurek Z. Sasiadek
Article
  • 254 Downloads

Abstract

Operation of mobile robots in off-road environment requires the attention to the torque saturation problem that occurs in the wheels DC motors while climbing hills. In the present work, off-road conditions are utilized to benefit while avoiding torque saturation. Energy optimization algorithm using predictive control is implemented on a two-DC motor-driven wheels mobile robot while crossing a ditch. The predictive control algorithm is simulated and compared with the PID control and the open-loop control. Predictive control showed more capability to avoid torque saturation and noticeable reduction in the energy consumption. Furthermore, using the wheels motors armature current instead of the supply voltage as control variable in the predictive control showed more efficient speed control. Simulation results showed that in case of known ditch dimensions ahead of time, the developed algorithm is feasible. Experimental examination of the developed energy optimization algorithm is presented. The experimental results showed a good agreement with the simulation results. The effects of the road slope and the prediction horizon length on the consumed energy are evaluated. The analytical study showed that the energy consumption is reduced by increasing the prediction horizon until it reaches a limit at which no more energy reduction is obtained. This limit is proportional to the width of the ditch in front of the mobile robot. Curve fitting is applied to the obtained results to address further the effect of the parameters on the energy consumption.

Keywords

Mobile robots Off-road conditions Predictive control Energy consumption Torque saturation 

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References

  1. 1.
    Zhang, W., Lu, Y., Hu, J.: Optimal solutions to a class of power management problems in Mobile Robots. Autom. (J. IFAC) 45(4), 989–996 (2009)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Suntharalingam, P., Economou, J.T., Knowles, K.: Gear Locking Mechanism to Extend the Consistent Power Operating Region of the Electric Motor to Enhance Acceleration and Regenerative Braking Efficiency in Hybrid Electric Vehicles. In: IEEE Vehicle Power and Propulsion Conference, pp 103–108, Dearborn, MI (2009)Google Scholar
  3. 3.
    Ngo, T.D., Raposo, H., Schioler, H.: Potentially Distributable Energy: Towards Energy Autonomy in Large Population of Mobile Robots. In: proceedings of the 2007 IEEE International Symposium on Computational Intelligance in Robotics and Automation, pp 208–211, Jacksonville, FL (2007)Google Scholar
  4. 4.
    Spangelo, I., Egeland, O.: Trajectory planning and collision avoidance for underwater vehicles using optimal control. IEEE J. Ocean. Eng. 19(4), 502–511 (1994)CrossRefGoogle Scholar
  5. 5.
    Vanualailai, J., Sharma, B., Nakagiri, S.-I.: An asymptotically stable collision-avoidance system. Int. J. Nonlinear Mech. 43(9), 925–932 (2008)CrossRefMATHGoogle Scholar
  6. 6.
    Chuy, O., Collins, E., Yu, W., Ordonez, C.: Power modeling of a skid steered robotic ground vehicle. In: IEEE International Conference on Robotics and Automation, pp 4118–4123, Kobe (2009)Google Scholar
  7. 7.
    Morales, J., Martinez, J.L., Mandow, A., Pequeno-Boter, A., Garcia-Cerezo, A.: Simplified power consumption modeling and identification for wheeled skid-steer robotic vehicles on hard horizontal ground. In: IEEE/RJ International Conference on Intelligent Robots and Systems, pp 4769–4774, Taiwan (2010)Google Scholar
  8. 8.
    Yu, W., Chuy, O.Y., Collins, E.G., Hollis, P.: Analysis and experimental verification for dynamic modeling of a skid-steered wheeled vehicle. IEEE Trans. Robot. 26(2), 340–353 (2010)CrossRefGoogle Scholar
  9. 9.
    Ordonez, C., Gupta, N., Yu, W., Chuy, O., Collins Jr, E.: Modeling of Skid-Steered Wheeled Robotic Vehicles on Sloped Terrains. In: ASME 2012 5Th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11Th Motion and Vibration Conference, pp 91–99. Fort Lauderdale, FL (2012)Google Scholar
  10. 10.
    Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Eng. Pract. (J. IFAC) 11, 733–764 (2003)CrossRefGoogle Scholar
  11. 11.
    Kamal, M.A.S., Mukai, M., Murata, J., Kawabe, T.: Ecological vehicle control on roads with up-down slopes. IEEE Trans. Intell. Trans. Syst. 12(3), 783–794 (2011)CrossRefGoogle Scholar
  12. 12.
    The Future of Fuel Economy, Freightliner Innovation Truck, 2012. [Online]. Available: http://www.freightlinertrucks.com/innovation/
  13. 13.
    Kamal, M.A.S., Mukai, M., Murata, J., Kawabe, T.: Model predictive control of vehicles on urban roads for improved fuel economy. IEEE Trans. Control Syst. Technol. 21(3), 831–841 (2013)CrossRefGoogle Scholar
  14. 14.
    Liu, C., Lee, C., Hansen, A., Hedrick, J., Ding, J.: A Computationally Efficient Predictive Controller for Lane Keeping of Semi-Autonomous Vehicles. In: proceedings of the ASME 2014 Dynamic Systems and Control Conference, pp 1–6, San Antonio, TX (2014)Google Scholar
  15. 15.
    Strassberger, D., Mercorelli, P., Georgiadis, A.: Using the Flatness of DC-Drives to Emulate a Generator for a Decoupled MPC Using a Geometric Approach for Motion Control for Robotino. In: International Symposium on Power Electronics, Electrical Drives, Automation and Motion, pp 1337–1343, Ischia, Itally (2014)Google Scholar
  16. 16.
    Sharma, K., Honc, D., Dusek, F.: Model predictive control of trajectory tracking of differentially steered mobile robot. Intell. Data Anal. Appl. 370, 85–95 (2015)Google Scholar
  17. 17.
    Liu, J., Jayakumar, P., Overholt, J., Stein, J., Ersal, T.: The Role of Model Fidelity in Model Predictive Control Based Hazard Avoidance in Unmanned Ground Vehicles Using LIDAR Sensors. In: proceedings of the ASME 2013 Dynamic Systems and Control Conference, pp 1–10, Palo Alto, CA (2013)Google Scholar
  18. 18.
    Liu, J., Jayakumar, P., Stein, J., Ersal, T.: A Multi-Stage Optimization Formulation for MPC-Based Obstacle Avoidance in Autonomous Vehicles Using LIDAR Sensor. In: proceedings of the ASME 2014 Dynamic Systems and Control Conference, pp 1–10, San Antonio, TX (2014)Google Scholar
  19. 19.
    Gao, Y., Tseng, E., Lin, T., Hrovat, D., Borrelli, F.: Predictive control of autonomous ground vehicles with obstacle avoidance on slippery roads. In: Proceedings of the ASME 2010 Dynamic Systems and Control Conference, pp 265–272, Cambridge, MA (2010)Google Scholar
  20. 20.
    Yacoub, M.I., Necsulescu, D.S., Sasiadek, J.Z.: Energy Consumption Optimization for Mobile Robots in Three-Dimension Motion Using Predictive Control. In: Asian Control Conference, pp 1–6, Istanbul, Turkey (2013)Google Scholar
  21. 21.
    Yacoub, M.I., Necsulescu, D.S., Sasiadek, J.Z.: Experimetal Evaluation of Energy Optimization Algorithm for Mobile Robots in Three-Dimension Motion Using Predictive Control. In: 21St Mediterranean Conference on Control and Automation, pp 437–443, Crete, Greece (2013)Google Scholar
  22. 22.
    February 2013. [Online]. Available: http://www.drrobot.com/products/itemdownloads/x801.pdf
  23. 23.
  24. 24.
    Astrom, T.H.K.: PID Control. In: PID Controllers; Theory, Design, and Tuning, NC, Instrument Society of America (1995)Google Scholar
  25. 25.
    Marques de Sa, J.P.: Data regression. In: Applied Statiscics: Using SPSS, STATISTICA, MATLAB and R, Heidelberg, Springer, 2007, pp. 275–328Google Scholar
  26. 26.

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Mostafa I. Yacoub
    • 1
  • Dan S. Necsulescu
    • 2
  • Jurek Z. Sasiadek
    • 3
  1. 1.The Military Technical CollegeCairoEgypt
  2. 2.University of OttawaOttawaCanada
  3. 3.Carleton UniversityOttawaCanada

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