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Found 11 papers, 0 papers with code
A Policy Iteration Approach for Flock Motion Control
An online model-free policy iteration mechanism is developed here to guide a flock of agents to follow an independent command generator over a time-varying graph topology.
A Data-Driven Model-Reference Adaptive Control Approach Based on Reinforcement Learning
Model-reference adaptive systems refer to a consortium of techniques that guide plants to track desired reference trajectories.
An Adaptive Fuzzy Reinforcement Learning Cooperative Approach for the Autonomous Control of Flock Systems
The flock-guidance problem enjoys a challenging structure where multiple optimization objectives are solved simultaneously.
Real-Time Measurement-Driven Reinforcement Learning Control Approach for Uncertain Nonlinear Systems
For concept demonstration, a trajectory-following optimization problem of a Kinova robotic arm is solved using an integral reinforcement learning approach with guaranteed stability for slowly varying dynamics.
Neural-adaptive Stochastic Attitude Filter on SO(3)
This paper proposes a novel stochastic nonlinear neural-adaptive-based filter on $SO(3)$ for the attitude estimation problem.
Geometric Stochastic Filter with Guaranteed Performance for Autonomous Navigation based on IMU and Feature Sensor Fusion
A computationally cheap geometric nonlinear stochastic navigation filter is proposed on $\mathbb{SE}_{2}(3)$ with guaranteed transient and steady-state performance.
Online Model-Free Reinforcement Learning for the Automatic Control of a Flexible Wing Aircraft
An online control mechanism based on a value iteration reinforcement learning process is developed for flexible wing aerial structures.
Responding to Illegal Activities Along the Canadian Coastlines Using Reinforcement Learning
This article elaborates on how machine learning (ML) can leverage the solution of a contemporary problem related to the security of maritime domains.
Data-Driven Optimized Tracking Control Heuristic for MIMO Structures: A Balance System Case Study
The heuristic decides on the control gains of multiple interacting PID control loops.
Trajectory Tracking of Underactuated Sea Vessels With Uncertain Dynamics: An Integral Reinforcement Learning Approach
In addition, the underlying trajectory-tracking control problems grow in complexity in order to decide the optimal rudder and thrust control signals.
Guidance Mechanism for Flexible Wing Aircraft Using Measurement-Interfaced Machine Learning Platform
This work leverages ideas from instrumentation and measurements, machine learning, and optimization fields in order to develop an autonomous navigation system for a flexible-wing aircraft.
