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

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.