Variance-Adaptive Approximated Model Predictive Control

Hybrid Feedback Sampling for Sample-Efficient Model Predictive Control

This paper investigates the instability of shooting methods in sampling-based MPC and shows that the optimal sampling proposal distribution can be realized by sampling with an optimized feedback policy, and develops a hybrid sampling design which balances local and global search based on the system stability and the av...

Koopman-based model predictive control for nonlinear systems with bounded model uncertainty

Stochastic Model Predictive Control Using Indirect Feedback With a Modified Terminal Constraint

The main contribution is to explore the time‐varying definition of the constraints in the indirect feedback SMPC (IF‐SMPC) to improve the feasible domain and explore the time‐varying definition of the constraints in the indirect feedback SMPC to improve the feasible domain.

Strong Tracking Iterative Learning Control for Linear Parameter-Varying Systems*

This paper presents an advanced predictive Iterative Learning Control (ILC) methodology tailored for discretetime Linear Parameter-Varying (LPV) systems subject to strict input and state constraints, as well as bounded process and measurement disturbances. The proposed framework operates through a dual-phase architectu...

Stabilizer Design for Policy Iteration in Stochastic Linear Quadratic Control: A Spectrum-Assignment Approach

A novel spectrum assignment method is proposed to obtain an initial stabilizer for PI in continuous-time indefinite stochastic linear quadratic control with the help of the Lyapunov-type operator's spectrum, which is gradually approximated from the stable auxiliary system by adjusting a cumulative factor, thereby obtai...

Inverse optimal control of general nonlinear systems based on adaptive dynamic programming