Search Results for author:
Found 15 papers, 0 papers with code
Deep Reinforcement Learning for Traveling Purchaser Problems
The traveling purchaser problem (TPP) is an important combinatorial optimization problem with broad applications.
Harnessing Data for Accelerating Model Predictive Control by Constraint Removal
Model predictive control (MPC) solves a receding-horizon optimization problem in real-time, which can be computationally demanding when there are thousands of constraints.
Standoff Tracking Using DNN-Based MPC with Implementation on FPGA
This work studies the standoff tracking problem to drive an unmanned aerial vehicle (UAV) to slide on a desired circle over a moving target at a constant height.
Optimal $(0,1)$-Matrix Completion with Majorization Ordered Objectives (To the memory of Pravin Varaiya)
We propose and examine two optimal $(0, 1)$-matrix completion problems with majorization ordered objectives.
Minimum Input Design for Direct Data-driven Property Identification of Unknown Linear Systems
In a direct data-driven approach, this paper studies the {\em property identification(ID)} problem to analyze whether an unknown linear system has a property of interest, e. g., stabilizability and structural properties.
An Exact Method for the Daily Package Shipment Problem with Outsourcing
The package shipment problem requires to optimally co-design paths for both packages and a heterogeneous fleet in a transit center network (TCN).
Innovation Compression for Communication-efficient Distributed Optimization with Linear Convergence
Information compression is essential to reduce communication cost in distributed optimization over peer-to-peer networks.
A Distributed Implementation of Steady-State Kalman Filter
This paper studies the distributed state estimation in sensor network, where $m$ sensors are deployed to infer the $n$-dimensional state of a linear time-invariant (LTI) Gaussian system.
Primal-dual Learning for the Model-free Risk-constrained Linear Quadratic Regulator
Risk-aware control, though with promise to tackle unexpected events, requires a known exact dynamical model.
Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning
Then we develop two algorithms for optimizing the energy efficiency of train operation.
Fully Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks
This paper proposes a \emph{fully asynchronous} scheme for the policy evaluation problem of distributed reinforcement learning (DisRL) over directed peer-to-peer networks.
Coordinate-free Circumnavigation of a Moving Target via a PD-like Controller
This paper proposes a coordinate-free controller for a nonholonomic vehicle to circumnavigate a fully-actuated moving target by using range-only measurements.
A selected review on reinforcement learning based control for autonomous underwater vehicles
This paper provides a selected review on RL based control for AUVs with the focus on applications of RL to low-level control tasks for underwater regulation and tracking.
Robotics
Distributed Dual Gradient Tracking for Resource Allocation in Unbalanced Networks
Our key idea is the novel use of the distributed push-pull gradient algorithm (PPG) to solve the dual problem of the resource allocation problem.
Decentralized Stochastic Gradient Tracking for Non-convex Empirical Risk Minimization
We explicitly evaluate the convergence rate of DSGT with respect to the number of iterations in terms of algebraic connectivity of the network, mini-batch size, gradient variance, etc.
