Application Study of Newton-Raphson-Type Nonlinear MPC for Hydrostatic Transmissions Under Disturbance and System Uncertainty

Authors

  • Ngoc Danh Dang Chair of Mechatronics, University of Rostock
  • Harald Aschemann Chair of Mechatronics, University of Rostock

DOI:

https://doi.org/10.52846/stccj.2021.1.1.7

Keywords:

Multivariable Systems, Hydrostatic Transmission, Model Predictive Control, Real-Time Implementation

Abstract

Model predictive control is well established and has a huge practical relevance in many industrial applications, especially for chemical or thermal plants. This paper presents the design and the implementation of a nonlinear model predictive control aiming at an accurate tracking control of desired output trajectories under disturbances and uncertainties for a nonlinear hydrostatic transmission system with multiple control inputs, which represents a fast mechatronic system. The benefit of this solution is that it can be easily adapted to either velocity tracking control or torque tracking control -- which is not the case with alternative model-based approaches. The control design is based on a numerical optimization within a moving horizon using the Newton-Raphson method in combination with the optimization-over-some variables technique. The unmeasurable system state variables as well as the system disturbances are reconstructed by an unscented Kalman filter which is well suited for nonlineaer systems subject to process and measurement noise. The proposed control scheme is investigated by simulations and experimentally validated on a test rig at the Chair of Mechatronics, University of Rostock. The results indicates the robustness of the proposed control structure by a high tracking accuracy despite system disturbances and uncertainties.

References

A. Hitchcox, Engineering essentials: Hydrostatic transmissions. Hydraulics & Pneumatics, 2016.

H. Aschemann and H. Sun, “Decentralised flatness-based control of a hydrostatic drive train subject to actuator uncertainty and disturbances,” In Proc. of Conf. on Methods and Models in Automation and Robotics (MMAR), pp. 759–764, 2013.

H. Schulte, “Control-oriented modeling of hydrostatic transmission using Takagi-Sugeno fuzzy systems,” In Proc. of IEEE Intern. Fuzzy Systems Conference, pp. 1–6, 2007.

H. Schulte, “Control-oriented description of large scale wind turbines with hydrostatic transmission using Takagi-Sugeno models. In Proc. of Conf. on Control Applications (CCA), pp. 664–668, 2014.

J. Kwa´sniewski, J. Pluta and A. Piotrowska, “Research on the properties of a hydrostatic transmission with different controllers,” In Acta Montanistica Slovaca, vol. 8, pp. 240–244, 2003.

H. W. Wu and C. B. Lee, “Self-tuning adaptive speed control for hydrostatic transmission systems,” In Intern. Journal of Computer Applications in Technology, vol. 9, pp. 18–33, 1996.

J. Lennevi and J. O. Palmberg, “Application and implementation of LQ design method for the velocity control of hydrostatic transmissions,” In Proc. of Institution of Mechanical Engineers, vol. 209, pp. 255–268, 1995.

H. Aschemann J. Ritzke and H. Schulte, “Model-based nonlinear trajectory control of a drive chain with hydrostatic transmission,” In Proc. of Conf. on Methods and Models in Automation and Robotics (MMAR), 2009.

J. Ritzke and H. Aschemann, “Design and experimental validation of nonlinear trajectory control of a drive chain with hydrostatic transmission,” In Proc. of Scandinavian Int. Conf. on Fluid Power (SICFP), 2011.

R. K. Arora, Optimization: Algorithms and Applications. Taylor & Francis Group, New York, USA, 2015.

E. Wan and R. Van der Merwe, “The unscented Kalman filter for nonlinear estimation,” In Proc. of IEEE Conf. on Adaptive Systems for Signal Processing, Communications, and Control Symposium, pp. 153–158, 2000.

W. H. Chen, “Stability analysis of classic finite horizon model predictive control,” In International Journal of Control, Automation, and Systems, vol. 8(2), Springer, pp. 187-197, 2010.

A. A. Ahmadi and P. A. Parrilo,“Non-monotonic Lyapunov functions for stability of discrete time nonlinear and switched systems,” In Proc. of IEEE Conf. on Decision and Control, 2008.

S. J. Julier and J. K. Uhlmann, “Unscented filtering and nonlinear estimation,” In Proc. of the IEEE, vol. 92(3), pp. 401–422, 2004.

S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, 2009.

H. Sun, Decentralized nonlinear control for a hydrostatic drive train with unknown disturbances. PhD thesis, University of Rostock, Shaker, 2015.

N. D. Dang and H. Aschemann, “Real-time implementation of model predictive multivariable tracking control for hydrostatic transmissions,” In Proc. of Intern. Conf. on System Theory, Control and Computing (ICSTCC), pp. 343–348, 2020.

N. D. Dang and H. Aschemann, “Discrete-time state-dependent proportional-integral control for torque tracking of hydrostatic transmissions,” IFAC PapersOnLine, vol 53(2), pp. 6591–6596, 2020.

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Published

2021-06-30

How to Cite

[1]
N. D. Dang and H. Aschemann, “Application Study of Newton-Raphson-Type Nonlinear MPC for Hydrostatic Transmissions Under Disturbance and System Uncertainty”, Syst. Theor. Control Comput. J., vol. 1, no. 1, pp. 21–29, Jun. 2021, doi: 10.52846/stccj.2021.1.1.7.
Received 2021-03-31
Accepted 2021-06-24
Published 2021-06-30