Search Results for author:
Found 7 papers, 1 papers with code
Efficient model predictive control for nonlinear systems modelled by deep neural networks
This paper presents a model predictive control (MPC) for dynamic systems whose nonlinearity and uncertainty are modelled by deep neural networks (NNs), under input and state constraints.
Real-Time Safe Control of Neural Network Dynamic Models with Sound Approximation
Safe control of neural network dynamic models (NNDMs) is important to robotics and many applications.
Runtime Monitoring and Fault Detection for Neural Network-Controlled Systems
There is an emerging trend in applying deep learning methods to control complex nonlinear systems.
Data-Driven Sliding Mode Control for Partially Unknown Nonlinear Systems
This paper introduces a new design method for data-driven control of nonlinear systems with partially unknown dynamics and unknown bounded disturbance.
Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
In this work, we propose Provably RObust and PLAusible Counterfactual Explanations (PROPLACE), a method leveraging on robust optimisation techniques to address the aforementioned limitations in the literature.
Data-driven dual-loop control for platooning mixed human-driven and automated vehicles
This paper considers controlling automated vehicles (AVs) to form a platoon with human-driven vehicles (HVs) under consideration of unknown HV model parameters and propulsion time constants.
Data-Driven Cooperative Adaptive Cruise Control for Unknown Nonlinear Vehicle Platoons
This paper studies cooperative adaptive cruise control (CACC) for vehicle platoons with consideration of the unknown nonlinear vehicle dynamics that are normally ignored in the literature.
