Teaching Model Predictive Control Algorithm Using Starter Kit Robot.

Advanced control concepts present a teaching challenge, where even at master level students benefit from these concepts being implemented and demonstrated on real hardware, rather than simply modelling the plant, applying control strategy and tuning. This paper describes one of a series of three experiments demonstrating the implementation of different control strategies using adaptive cruise control (ACC) on robot models and real robots. The experiment described here utilises the model predictive control (MPC) strategy implemented in ACC. The algorithm is realised using the graphical programming language (LabVIEW) as the design environment and National Instruments Robotics Starter Kit robot as the target hardware, with the code being deployed on a field programmable gate array (FPGA), included in the robot's architecture. Two robotic vehicles, 'the leader' and 'the follower' are programmed to execute ACC: the velocity of the leader robot and the distance between the robots are augmented into the robot's state-space equation, to design the controller (MPC), which was then tuned for both velocity and distance tracking modes. The experiment offers a novel idea on how to deliver this advanced control strategy in an applied and visual manner with laboratory experimentation supporting the theoretical aspects of learning. It brings to life some often stated theoretical qualities of an MPC controller, including quick rise time, minor fluctuation and a small distance tracking error, in line with current scientific papers. Thus, it demonstrates to students a clear correlation between theoretical expectations and real-life system performance whilst challenging their ability to work with real hardware. [ABSTRACT FROM AUTHOR]