  The performance achieved with traditional model-based control system design
approaches typically relies heavily upon accurate modeling of the motion
dynamics. However, modeling the true dynamics of present-day increasingly
complex systems can be an extremely challenging task; and the usually necessary
practical approximations often render the automation system to operate in a
non-optimal condition. This problem can be greatly aggravated in the case of a
multi-axis magnetically-levitated nanopositioning system where the fully
floating behavior and multi-axis coupling make extremely accurate
identification of the motion dynamics largely impossible. On the other hand, in
many related industrial automation applications, e.g., the scanning process
with the maglev system, repetitive motions are involved which could generate a
large amount of motion data under non-optimal conditions. These motion data
essentially contain rich information; therefore, the possibility exists to
develop an intelligent automation system to learn from these motion data and to
drive the system to operate towards optimality in a data-driven manner. Along
this line then, this paper proposes a data-driven controller optimization
approach that learns from the past non-optimal motion data to iteratively
improve the motion control performance. Specifically, a novel data-driven
multi-objective optimization approach is proposed that is able to automatically
estimate the gradient and Hessian purely based on the measured motion data; the
multi-objective cost function is suitably designed to take into account both
smooth and accurate trajectory tracking. Experiments are then conducted on the
maglev nanopositioning system to demonstrate the effectiveness of the proposed
method, and the results show rather clearly the practical appeal of our
methodology for related complex robotic systems with no accurate model
available.
