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Found 11 papers, 2 papers with code
Probabilistic reachable sets of stochastic nonlinear systems with contextual uncertainties
The existing methods of computing probabilistic reachable sets normally assume that the uncertainties are independent of the state.
Long-term Safe Reinforcement Learning with Binary Feedback
Our theoretical results show that LoBiSaRL guarantees the long-term safety constraint, with high probability.
Synthesis of Event-triggered Controllers for SIRS Epidemic Models
To synthesize the event-triggered controller, we leverage the notion of a symbolic model, which represents an abstracted expression of the transition system associated with the SIRS model under the event-triggered control strategy.
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.
STLCCP: An Efficient Convex Optimization-based Framework for Signal Temporal Logic Specifications
Signal Temporal Logic (STL) is capable of expressing a broad range of temporal properties that controlled dynamical systems must satisfy.
Signal Temporal Logic Meets Convex-Concave Programming: A Structure-Exploiting SQP Algorithm for STL Specifications
Our framework includes a new robustness decomposition method that decomposes the robustness function into a set of constraints, resulting in a form of difference of convex (DC) program that can be solved efficiently.
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).
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes
This paper investigates the design of self-triggered control for networked control systems (NCS), where the dynamics of the plant is unknown apriori.
STL2vec: Signal Temporal Logic Embeddings for Control Synthesis With Recurrent Neural Networks
In this paper, a method for learning a recurrent neural network (RNN) controller that maximizes the robustness of signal temporal logic (STL) specifications is presented.
Collaborative rover-copter path planning and exploration with temporal logic specifications based on Bayesian update under uncertain environments
An exploration policy for the copter is then synthesized by employing the notion of an entropy that is evaluated based on the environmental beliefs of the atomic propositions, and a path that the rover intends to follow according to the optimal policy.
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.
