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Comparative Application of Model Predictive Control Strategies to a Wheeled Mobile Robot

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Abstract

Model predictive control strategies refer to a set of methods relying on a process model to determine an optimal control signal by minimising a cost function. This paper reports on the application of predictive control strategies to a wheeled mobile robot. As a first step, friction forces originating from the motor gearboxes and wheels were estimated and a feedforward compensation was applied. Step response tests were then carried out to identify a linear model to design several simple control strategies, such as the Proportional-Integral-Derivative (PID) controller. The PID response constitutes the reference to assess the efficiency of two predictive control strategies: the generalised predictive control (GPC) and the linear quadratic model predictive control (LQMPC) algorithms. These control strategies were tested in simulation with Matlab and EasyDyn (a C++ library for multibody system simulations) and in real life experiments. All three control strategies offer satisfactory reference tracking but MPC allows a reduction of the energy consumption of up to 70 % as a result of set-point anticipation. LQMPC is the best in terms of input activity reduction.

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Authors and Affiliations

  1. Faculty of Engineering of Mons (UMONS), Boulevard Dolez 31, 7000, Mons, Belgium

    Hoai Nam Huynh, Olivier Verlinden & Alain Vande Wouwer

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  1. Hoai Nam Huynh
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  2. Olivier Verlinden
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  3. Alain Vande Wouwer
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Correspondence to Hoai Nam Huynh.

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Huynh, H.N., Verlinden, O. & Vande Wouwer, A. Comparative Application of Model Predictive Control Strategies to a Wheeled Mobile Robot. J Intell Robot Syst 87, 81–95 (2017). https://doi.org/10.1007/s10846-017-0500-2

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  • DOI: https://doi.org/10.1007/s10846-017-0500-2

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