A safe and learning-based control framework for model predictive control is proposed to optimize nonlinear systems with a non-differentiable objective function under uncertain environmental disturbances. The control framework integrates a learning-based MPC with an auxiliary controller in a way of minimal intervention. The proposed approach provides a promising solution to the propagation of stochastic state distributions in MPC, allowing for improved task performance while ensuring high levels of safety.
Enforcing safety on precise trajectory tracking is critical for aerial robotics subject to wind disturbances. In this paper, we present a learning-based safety-preserving cascaded quadratic programming control for safe trajectory tracking under wind disturbances.
A learning-based MPFC control paradigm for nonlinear systems under uncertain disturbances, coupling a high-level model predictive path following controller for proactivity with a low-level learning-based feedback linearization controller for adaptivity. Following that, nonlinear systems can rapidly rejoin their reference trajectory after sudden wind disturbances with stability guarantees.
A learning-based safety-stability-driven control algorithm is presented to guarantee the safety and tracking stability for nonlinear safety-critical systems subject to control input constraints under model uncertainties.
To achieve [high-speed autonomous flight](https://www.youtube.com/watch?v=-P86aRglDjE) of aerial vehicles and realize high-performance precision spraying in precision farming. Trajectories must be generated in real-time to avoid collision and be close to the reference spraying path. Because of the high navigation speed, short sensing range, and unknown environments, response time is extremely limited, making generating high-quality trajectories a significant challenge.