  Motion control is essential for all autonomous mobile robots, and even more
so for spherical robots. Due to the uniqueness of the spherical robot, its
motion control must not only ensure accurate tracking of the target commands,
but also minimize fluctuations in the robot's attitude and motors' current
while tracking. In this paper, model predictive control (MPC) is applied to the
control of spherical robots and an MPC-based motion control framework is
designed. There are two controllers in the framework, an optimal velocity
controller ESO-MPC which combines extend states observers (ESO) and MPC, and an
optimal orientation controller that uses multilayer perceptron (MLP) to
generate accurate trajectories and MPC with changing weights to achieve optimal
control. Finally, the performance of individual controllers and the whole
control framework are verified by physical experiments. The experimental
results show that the MPC-based motion control framework proposed in this work
is much better than PID in terms of rapidity and accuracy, and has great
advantages over sliding mode controller (SMC) for overshoot, attitude
stability, current stability and energy consumption.
