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Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehicles
Also, few works have addressed the safe RL algorithm design under time-varying safety constraints.
Robust Tube-based Model Predictive Control with Koopman Operators--Extended Version
The finite data-driven approximation of Koopman operators results in a class of linear predictors, useful for formulating linear model predictive control (MPC) of nonlinear dynamical systems with reduced computational complexity.
Deep Neural Networks with Koopman Operators for Modeling and Control of Autonomous Vehicles
Furthermore, a data-driven model predictive controller with the learned Koopman model is designed for path tracking control of autonomous vehicles.
Robust Learning-based Predictive Control for Discrete-time Nonlinear Systems with Unknown Dynamics and State Constraints
Robust model predictive control (MPC) is a well-known control technique for model-based control with constraints and uncertainties.
A Dual-level Model Predictive Control Scheme for Multi-timescale Dynamical Systems--Extended Version
So far, many control algorithms have been developed for singularly perturbed systems.
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