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Found 15 papers, 8 papers with code
NLBAC: A Neural Ordinary Differential Equations-based Framework for Stable and Safe Reinforcement Learning
Reinforcement learning (RL) excels in applications such as video games and robotics, but ensuring safety and stability remains challenging when using RL to control real-world systems where using model-free algorithms suffering from low sample efficiency might be prohibitive.
A contract negotiation scheme for safety verification of interconnected systems
This paper proposes a (control) barrier function synthesis and safety verification scheme for interconnected nonlinear systems based on assume-guarantee contracts (AGC) and sum-of-squares (SOS) techniques.
Rational Neural Network Controllers
However, one prominent issue with these methods is that they use existing neural network architectures tailored for traditional machine learning tasks.
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic Approach
Reinforcement learning (RL) has demonstrated impressive performance in various areas such as video games and robotics.
Distributed Safe Control Design and Safety Verification for Multi-Agent Systems
Both the scenario sampling and safety verification procedures are fully distributed.
Safety-Aware Optimal Control for Motion Planning with Low Computing Complexity
Simulations demonstrates that BRSCA has a higher probability of finding feasible solutions, reduces the computation time by about 17. 4% and the energy cost by about four times compared to other methods in the literature.
Stability of Non-linear Neural Feedback Loops using Sum of Squares
These higher order Lyapunov functions are used in conjunction with higher order multipliers on the inequality and equality constraints that bound the neural network input-output properties.
Sparse Polynomial Optimisation for Neural Network Verification
Depending on the complexity of these bounds, the computational time of the optimisation problem varies, with longer solve times often leading to tighter bounds.
Sparse sum-of-squares (SOS) optimization: A bridge between DSOS/SDSOS and SOS optimization for sparse polynomials
Optimization over non-negative polynomials is fundamental for nonlinear systems analysis and control.
Optimization and Control Systems and Control
Decomposition and Completion of Sum-of-Squares Matrices
We show that a subset of sparse SOS matrices with chordal sparsity patterns can be equivalently decomposed into a sum of multiple SOS matrices that are nonzero only on a principal submatrix.
Optimization and Control Systems and Control
Chordal decomposition in operator-splitting methods for sparse semidefinite programs
We employ chordal decomposition to reformulate a large and sparse semidefinite program (SDP), either in primal or dual standard form, into an equivalent SDP with smaller positive semidefinite (PSD) constraints.
Optimization and Control
Fast ADMM for homogeneous self-dual embedding of sparse SDPs
We propose an efficient first-order method, based on the alternating direction method of multipliers (ADMM), to solve the homogeneous self-dual embedding problem for a primal-dual pair of semidefinite programs (SDPs) with chordal sparsity.
Optimization and Control
Fast ADMM for Semidefinite Programs with Chordal Sparsity
We show that chordal decomposition can be applied to either the primal or the dual standard form of a sparse SDP, resulting in scaled versions of ADMM algorithms with the same computational cost.
Optimization and Control
Stochastic processes and feedback-linearisation for online identification and Bayesian adaptive control of fully-actuated mechanical systems
In contrast to previous work that has used stochastic processes for identification, we leverage the structural knowledge afforded by Lagrangian mechanics and learn the drift and control input matrix functions of the control-affine system separately.
SOSTOOLS Version 4.00 Sum of Squares Optimization Toolbox for MATLAB
Specifically, polynomial and SOS variable declarations made using sossosvar, sospolyvar, sosmatrixvar, etc now return a new polynomial structure, dpvar.
Optimization and Control Mathematical Software Systems and Control
