7 papers
Free Parametrization of L_2-Bounded Structured State-Space Controllers for Nonlinear Control with Stability Guarantees
Muhammad Zakwan, Leonardo Massai, Efe C. Balta +1
Designing stabilizing control policies for nonlinear systems while optimizing complex objectives remains a formidable challenge. Neural networks (NNs), despite their expressive pow…
Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
Yu Tang, Muhammad Zakwan, Efe Balta +2
The Flexible Job Shop Scheduling Problem (FJSP) is the optimal allocation of a set of jobs to machines. Two primary challenges persist in FJSP: the unpredictable arrival of future…
L2RU: a Structured State Space Model with prescribed L2-bound
Leonardo Massai, Muhammad Zakwan, Giancarlo Ferrari-Trecate
Structured state-space models (SSMs) have recently emerged as a powerful architecture at the intersection of machine learning and control, featuring layers composed of discrete-tim…
Controller Design for Structured State-space Models via Contraction Theory
Muhammad Zakwan, Vaibhav Gupta, Alireza Karimi +2
This paper presents an indirect data-driven output feedback controller synthesis for nonlinear systems, leveraging Structured State-space Models (SSMs) as surrogate models. SSMs ha…
Robust Convolution Neural ODEs via Contractivity-promoting regularization
Muhammad Zakwan, Liang Xu, Giancarlo Ferrari-Trecate
Neural networks can be fragile to input noise and adversarial attacks. In this work, we consider Convolutional Neural Ordinary Differential Equations (NODEs), a family of continuou…
Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach
Muhammad Zakwan, Giancarlo Ferrari-Trecate
The control of large-scale cyber-physical systems requires optimal distributed policies relying solely on limited communication with neighboring agents. However, computing stabiliz…