Machine learning-based methodologies have recently been adapted to solve control
problems. The Neural Lyapunov Control (NLC) method is one such example. This approach …

This paper presents a novel approach to safety verification based on neural barrier
certificates synthesis for continuous dynamical systems. We construct the synthesis …

Performance and closed-loop stability of control systems can be jeopardised by actuator
faults. Actuator redundancy in combination with appropriate control laws can increase the …

This paper presents Fossil 2.0, a new major release of a software tool for the synthesis of
certificates (eg, Lyapunov and barrier functions) for dynamical systems modelled as ordinary …

A barrier certificate often serves as an inductive invariant that isolates an unsafe region from
the reachable set of states, and hence is widely used in proving safety of hybrid systems …

An emerging branch of control theory specialises in certificate learning, concerning the
specification of a desired (possibly complex) system behaviour for an autonomous or control …

Barrier certificates are inductive invariants that provide guarantees on the safety and
reachability behaviors of continuous dynamical systems. For stochastic dynamical systems …

Safety verification of dynamical systems via barrier certificates has recently gained
considerable attention. A barrier certificate is typically a real-valued function over states of …

We present the invariant barrier-certificate condition that witnesses unbounded-time safety
of differential dynamical systems. The proposed condition is the weakest possible one to …

Stability is required for real world controlled systems as it ensures that those systems can
tolerate small, real world perturbations around their desired operating states. This paper …