Present a real-time parallel trajectory optimization method for the autonomous vehicles to achieve high travel efficiency in dynamic and congested environments.
We propose a learning-based tracking control scheme based on a feedback linearization controller in which uncertain disturbances are approximated online using Gaussian Processes. Using the predicted distribution of disturbances given by GPs, a Control Lyapunov Function and Control Barrier Function based Quadratic Program is applied, with which probabilistic stability and safety are guaranteed.
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.