  Despite great successes, model predictive control (MPC) relies on an accurate
dynamical model and requires high onboard computational power, impeding its
wider adoption in engineering systems, especially for nonlinear real-time
systems with limited computation power. These shortcomings of MPC motivate this
work to make such a control framework more practically viable for real-world
applications. Specifically, to remove the required accurate dynamical model and
reduce the computational cost for nonlinear MPC (NMPC), this paper develops a
unified online data-driven predictive control pipeline to efficiently control a
system with guaranteed safety without incurring large computational complexity.
The new aspect of this idea is learning not only the real system but also the
control policy, which results in a reasonable computational cost for the
data-driven predictive controllers. More specifically, we first develop a
spatial temporal filter (STF)-based concurrent learning scheme to
systematically identify system dynamics for general nonlinear systems. We then
develop a robust control barrier function (RCBF) for safety guarantees in the
presence of model uncertainties and learn the RCBF-based NMPC policy.
Furthermore, to mitigate the performance degradation due to the existing model
uncertainties, we propose an online policy correction scheme through
perturbation analysis and design of an ancillary feedback controller. Finally,
extensive simulations on two applications, cart-inverted pendulum and
automotive powertrain control, are performed to demonstrate the efficacy of the
proposed framework, which shows comparable performance with much lower
computational cost in comparison with several benchmark algorithms.
