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Found 10 papers, 1 papers with code
Autonomous Emergency Braking With Driver-In-The-Loop: Torque Vectoring for Active Learning
Autonomous Emergency Braking (AEB) potentially brings significant improvements in automotive safety due to its ability to autonomously prevent collisions in situations where the driver may not be able to do so.
A hierarchical control framework for autonomous decision-making systems: Integrating HMDP and MPC
We model the decision making system in complex environments as a hybrid system consisting of a controlled MDP and autonomous (i. e. uncontrolled) continuous dynamics.
An Exploration-Exploitation Approach to Anti-lock Brake Systems
Anti-lock Brake System (ABS) is a mandatory active safety feature on road vehicles with analogous systems for aircraft and locomotives.
Dual Control of Exploration and Exploitation for Auto-Optimisation Control with Active Learning
Formal properties of the proposed DCEE framework like convergence are established.
A distributionally robust optimization approach to two-sided chance constrained stochastic model predictive control with unknown noise distribution
In this work, we propose a distributionally robust stochastic model predictive control (DR-SMPC) algorithm to address the problem of two-sided chance constrained discrete-time linear system corrupted by additive noise.
Multi-step dual control for exploration and exploitation in autonomous search with convergence guarantee
Motivated by the recently proposed dual control for exploration and exploitation (DCEE) concept, this paper presents a Multi-Step DCEE (MS-DCEE) framework with guaranteed convergence for autonomous search of a source of airborne dispersion.
Model Predictive Control with Preview: Recursive Feasibility and Stability
This paper proposes a stabilising model predictive control (MPC) scheme with preview information of disturbance for nonlinear systems.
Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation
In this setting, the control action not only minimises the tracking error between future agent's position and estimated source location, but also the uncertainty of predicted estimation.
Stability of Finite Horizon Optimisation based Control without Terminal Weight
Numerical examples are presented to demonstrate the effectiveness of the proposed stability condition and corresponding CMPC algorithm.
Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems
Rapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning.
