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Found 7 papers, 0 papers with code
Introducing Graph Learning over Polytopic Uncertain Graph
This extended abstract introduces a class of graph learning applicable to cases where the underlying graph has polytopic uncertainty, i. e., the graph is not exactly known, but its parameters or properties vary within a known range.
Bayesian Meta-Learning on Control Barrier Functions with Data from On-Board Sensors
The proposed scheme realizes efficient online synthesis of the controller as shown in the simulation study and provides probabilistic safety guarantees on the resulting controller.
Greedy Synthesis of Event- and Self-Triggered Controls with Control Lyapunov-Barrier Function
This paper addresses the co-design problem of control inputs and execution decisions for event- and self-triggered controls subject to constraints given by the control Lyapunov function and control barrier function.
Neural Controller Synthesis for Signal Temporal Logic Specifications Using Encoder-Decoder Structured Networks
In this paper, we propose a control synthesis method for signal temporal logic (STL) specifications with neural networks (NNs).
Instabilizability Conditions for Continuous-Time Stochastic Systems Under Control Input Constraints
In this paper, we investigate constrained control of continuous-time linear stochastic systems.
Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning
Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation.
Learning-based Symbolic Abstractions for Nonlinear Control Systems
Symbolic models or abstractions are known to be powerful tools for the control design of cyber-physical systems (CPSs) with logic specifications.
