Approximate Stochastic Optimal Control for Linear Time Invariant Systems with Heavy-tailed Disturbances
원문 초록
We propose an open loop control scheme for linear time invariant systems perturbed by multivariate $t$ disturbances through the use of quantile reformulations. The multivariate $t$ disturbance is motivated by heavy tailed phenomena that arise in multi-vehicle planning planning problems through unmodeled perturbation forces, linearization effects, or faulty actuators. Our approach relies on convex quantile reformulations of the polytopic target sets and norm based collision avoidance constraints to enable fast computation. We embed quantile approximations of the Student's $t$ distribution and the beta prime distribution in a difference-of-convex function framework to compute provably safe but likely suboptimal controllers. We demonstrate our method with three satellite rendezvous examples and provide a comparison with particle control.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
ECAM: A Contrastive Learning Approach to Avoid Environmental Collision in Trajectory Forecasting
Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Drone
Risk-Aware Robot Control in Dynamic Environments Using Belief Control Barrier Functions
AUTOBargeSim: MATLAB(R) toolbox for the design and analysis of the guidance and control system for autonomous inland vessels
Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management Problem
High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.