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Found 10 papers, 2 papers with code
A Control Barrier Function Composition Approach for Multi-Agent Systems in Marine Applications
The agents within a multi-agent system (MAS) operating in marine environments often need to utilize task payloads and avoid collisions in coordination, necessitating adherence to a set of relative-pose constraints, which may include field-of-view, line-of-sight, collision-avoidance, and range constraints.
Task-Oriented Koopman-Based Control with Contrastive Encoder
We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operator, and associated linear controller within an iterative loop.
Sub-Optimal Moving Horizon Estimation in Feedback Control of Linear Constrained Systems
Moving horizon estimation (MHE) offers benefits relative to other estimation approaches by its ability to explicitly handle constraints, but suffers increased computation cost.
Stability Bounds for Learning-Based Adaptive Control of Discrete-Time Multi-Dimensional Stochastic Linear Systems with Input Constraints
We consider the problem of adaptive stabilization for discrete-time, multi-dimensional linear systems with bounded control input constraints and unbounded stochastic disturbances, where the parameters of the true system are unknown.
Knowledge Distillation for Feature Extraction in Underwater VSLAM
This paper proposes a cross-modal knowledge distillation framework for training an underwater feature detection and matching network (UFEN).
Learning-Based Adaptive Control for Stochastic Linear Systems with Input Constraints
We propose a certainty-equivalence scheme for adaptive control of scalar linear systems subject to additive, i. i. d.
Real-Time Distributed Model Predictive Control with Limited Communication Data Rates
The cyclic-small-gain theorem is used to derive sufficient conditions on the quantization parameters for guaranteeing the stability of the system under a limited data rate.
Data-driven Predictive Tracking Control based on Koopman Operators
Constraint handling during tracking operations is at the core of many real-world control implementations and is well understood when dynamic models of the underlying system exist, yet becomes more challenging when data-driven models are used to describe the nonlinear system at hand.
MBB: Model-Based Baseline for Global Guidance of Model-Free Reinforcement Learning via Lower-Dimensional Solutions
On the other end, "classical methods" such as optimal control generate solutions without collecting data, but assume that an accurate model of the system and environment is known and are mostly limited to problems with low-dimensional (lo-dim) state spaces.
A Sequential Approximation Framework for Coded Distributed Optimization
As an application of the results, we demonstrate solving optimization problems using a sequential approximation approach, which accelerates the algorithm in a distributed system with stragglers.
