CAFE-MPC: Cascaded-Fidelity Model Predictive Control Framework with Whole-Body Control
The author introduces a novel optimization-based control framework, CAFE-MPC, that strategically relaxes planning constraints for computational efficiency while unifying whole-body MPC and whole-body QPs.
Actor-Critic Model Predictive Control: Bridging RL and MPC for Agile Flight
Actor-Critic Model Predictive Control combines the benefits of RL and MPC for agile flight control.
CAFE-MPC: A Cascaded-Fidelity Model Predictive Control Framework with Whole-Body Control
複雑な動作の合成を可能にするCAFE-MPCの核メッセージは、高度なモデル予測制御フレームワークである。
Lattice Piecewise Affine Approximation of Explicit Model Predictive Control for Satellite Attitude Control
Lattice PWA approximation effectively stabilizes satellite attitude control systems.
Adaptive Economic Model Predictive Control for Linear Systems with Performance Guarantees
Adaptive Economic Model Predictive Control ensures performance guarantees for linear systems.
Goal-Conditioned Terminal Value Estimation for Real-time Multi-task Model Predictive Control Using a Surrogate Robot Model
This paper introduces a novel hierarchical Model Predictive Control (MPC) framework that leverages goal-conditioned terminal value learning and a surrogate robot model to achieve real-time, multi-task control of complex robotic systems.
Robust Model Predictive Control for Large-Scale Distributed Parameter Systems Under Uncertainty: A Polynomial Chaos Expansion, Proper Orthogonal Decomposition, and Recurrent Neural Network-Based Approach
This paper proposes a novel framework for robust nonlinear model predictive control (NMPC) of large-scale distributed parameter systems (DPS) under uncertainty, employing a powerful combination of polynomial chaos expansion (PCE), proper orthogonal decomposition (POD), and recurrent neural networks (RNNs) to handle uncertainty, high dimensionality, and non-convexity.
Multi-Objective Learning Model Predictive Control for Iterative Performance Improvement of Linear Systems
This paper presents a novel data-driven control scheme called Multi-Objective Learning Model Predictive Control (MO-LMPC) that leverages iterative task executions to enhance the closed-loop performance of linear systems with respect to multiple, potentially competing, control objectives.