Search Results for author: Shaoru Chen

Found 13 papers, 6 papers with code

Domain Adaptive Safety Filters via Deep Operator Learning

no code implementations18 Oct 2024 Lakshmideepakreddy Manda, Shaoru Chen, Mahyar Fazlyab

Learning-based approaches for constructing Control Barrier Functions (CBFs) are increasingly being explored for safety-critical control systems.

Operator learning

Verification-Aided Learning of Neural Network Barrier Functions with Termination Guarantees

1 code implementation12 Mar 2024 Shaoru Chen, Lekan Molu, Mahyar Fazlyab

With a convex formulation of the barrier function synthesis, we propose to first learn an empirically well-behaved NN basis function and then apply a fine-tuning algorithm that exploits the convexity and counterexamples from the verification failure to find a valid barrier function with finite-step termination guarantees: if there exist valid barrier functions, the fine-tuning algorithm is guaranteed to find one in a finite number of iterations.

Self-Supervised Learning valid

Safety Filter Design for Neural Network Systems via Convex Optimization

1 code implementation16 Aug 2023 Shaoru Chen, Kong Yao Chee, Nikolai Matni, M. Ani Hsieh, George J. Pappas

With the increase in data availability, it has been widely demonstrated that neural networks (NN) can capture complex system dynamics precisely in a data-driven manner.

One-Shot Reachability Analysis of Neural Network Dynamical Systems

no code implementations23 Sep 2022 Shaoru Chen, Victor M. Preciado, Mahyar Fazlyab

The arising application of neural networks (NN) in robotic systems has driven the development of safety verification methods for neural network dynamical systems (NNDS).

Robust Model Predictive Control of Time-Delay Systems through System Level Synthesis

no code implementations23 Sep 2022 Shaoru Chen, Ning-Yuan Li, Victor M. Preciado, Nikolai Matni

In the proposed method, a time-varying feedback control policy is optimized such that the robust satisfaction of all constraints for the closed-loop system is guaranteed.

Model Predictive Control

Differentiable Safe Controller Design through Control Barrier Functions

no code implementations20 Sep 2022 Shuo Yang, Shaoru Chen, Victor M. Preciado, Rahul Mangharam

Learning-based controllers, such as neural network (NN) controllers, can show high empirical performance but lack formal safety guarantees.

Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesis

2 code implementations21 Mar 2022 Shaoru Chen, Victor M. Preciado, Manfred Morari, Nikolai Matni

However, it is challenging to design LTV state feedback controllers in the face of model uncertainty whose effects are difficult to bound.

model Model Predictive Control

System Level Synthesis-based Robust Model Predictive Control through Convex Inner Approximation

1 code implementation10 Nov 2021 Shaoru Chen, Nikolai Matni, Manfred Morari, Victor M. Preciado

We propose a robust model predictive control (MPC) method for discrete-time linear time-invariant systems with norm-bounded additive disturbances and model uncertainty.

Model Predictive Control

Learning Region of Attraction for Nonlinear Systems

no code implementations2 Oct 2021 Shaoru Chen, Mahyar Fazlyab, Manfred Morari, George J. Pappas, Victor M. Preciado

Estimating the region of attraction (ROA) of general nonlinear autonomous systems remains a challenging problem and requires a case-by-case analysis.

DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting

1 code implementation16 Jun 2021 Shaoru Chen, Eric Wong, J. Zico Kolter, Mahyar Fazlyab

Analyzing the worst-case performance of deep neural networks against input perturbations amounts to solving a large-scale non-convex optimization problem, for which several past works have proposed convex relaxations as a promising alternative.

image-classification Image Classification

Learning Lyapunov Functions for Hybrid Systems

no code implementations22 Dec 2020 Shaoru Chen, Mahyar Fazlyab, Manfred Morari, George J. Pappas, Victor M. Preciado

By designing the learner and the verifier according to the analytic center cutting-plane method from convex optimization, we show that when the set of Lyapunov functions is full-dimensional in the parameter space, our method finds a Lyapunov function in a finite number of steps.

Optimization and Control

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