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Found 14 papers, 2 papers with code
Synchronisation-Oriented Design Approach for Adaptive Control
In the context of adaptation, model reference adaptive control methods make the state response of the actual plant follow a reference model.
Automatic Optimisation of Normalised Neural Networks
The first algorithm utilises automatic differentiation of the objective function along the update curve defined on the combined manifold of spheres.
Dynamic deep-reinforcement-learning algorithm in Partially Observed Markov Decision Processes
Reinforcement learning has been greatly improved in recent studies and an increased interest in real-world implementation has emerged in recent years.
A Passivity-Based Method for Accelerated Convex Optimisation
This study presents a constructive methodology for designing accelerated convex optimisation algorithms in continuous-time domain.
Domain-Knowledge-Aided Airborne Ground Moving Targets Tracking
This paper investigates the problem of traffic surveillance using an unmanned aerial vehicle (UAV) and proposes a domain-knowledge-aided airborne ground moving targets tracking algorithm.
An Auction-based Coordination Strategy for Task-Constrained Multi-Agent Stochastic Planning with Submodular Rewards
In many domains such as transportation and logistics, search and rescue, or cooperative surveillance, tasks are pending to be allocated with the consideration of possible execution uncertainties.
Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction
This study presents incremental correction methods for refining neural network parameters or control functions entering into a continuous-time dynamic system to achieve improved solution accuracy in satisfying the interim point constraints placed on the performance output variables.
Bayesian Learning Approach to Model Predictive Control
High-level frameworks have been developed separately in the earlier studies on Bayesian learning and sampling-based model predictive control.
Optimisation of Structured Neural Controller Based on Continuous-Time Policy Gradient
This study presents a policy optimisation framework for structured nonlinear control of continuous-time (deterministic) dynamic systems.
A Learning-Based Computational Impact Time Guidance
This paper investigates the problem of impact-time-control and proposes a learning-based computational guidance algorithm to solve this problem.
Target Detection, Tracking and Avoidance System for Low-cost UAVs using AI-Based Approaches
An onboard target detection, tracking and avoidance system has been developed in this paper, for low-cost UAV flight controllers using AI-Based approaches.
Improving Learning Effectiveness For Object Detection and Classification in Cluttered Backgrounds
The performance of the proposed framework is investigated through empirical tests and compared with that of the model trained with the COCO dataset.
A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design
This paper aims to examine the potential of using the emerging deep reinforcement learning techniques in flight control.
Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System
This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents.
