Publications by Muhammad Zakwan

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Neural Networks (NNs), renowned for their universal approximation capabilities, have proven well-suited for parametrizing nonlinear control policies and modeling complex processes from available data. However, several NN architectures are physics-agnostic, ...

Deep neural networks can be fragile and sensitive to small input perturbations that might cause a significant change in the output. In this letter, we employ contraction theory to improve the robustness of neural ODEs (NODEs). A dynamical system is contrac ...

Large-scale cyber-physical systems require that control policies are distributed, that is, that they only rely on local real-time measurements and communication with neighboring agents. Optimal Distributed Control (ODC) problems are, however, highly intrac ...

The energy Casimir method is an effective controller design approach to stabilize port-Hamiltonian systems at a desired equilibrium. However, its application relies on the availability of suitable Casimir and Lyapunov functions, whose computation are gener ...

This letter investigates the universal approximation capabilities of Hamiltonian Deep Neural Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary Differential Equations. Recently, it has been shown that HDNNs enjoy, by design, ...

Controlling large-scale cyber-physical systems necessitates optimal distributed policies, relying solely on local real-time data and limited communication with neighboring agents. However, finding optimal controllers remains challenging, even in seemingly ...

This manuscript details and extends the system identification methods leveraging the backpropagation (SIMBa) toolbox presented in previous work, which uses well-established machine learning tools for discrete-time linear multistep-ahead state-space system ...

This paper proposes a novel learning-based approach for achieving exponential stabilization of nonlinear control-affine systems. We leverage the Control Contraction Metrics (CCMs) framework to co-synthesize Neural Contraction Metrics (NCMs) and Neural Netw ...

Dwell-time based stability conditions for a class of LPV systems with piecewise constant parameters under time-varying delay are derived using clock-dependent Lyapunov-Krasovskii functional. Sufficient synthesis conditions for clock-dependent gain-schedule ...

This article proposes a new clock-dependent gain-scheduled dynamic output feedback controller for delayed linear parameter varying systems with piecewise constant parameters. The proposed controller guarantees Script capital L2-performance. By employing a ...

Muhammad Zakwan

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Dependable learning and control through Neural ODEs

Robust Classification Using Contractive Hamiltonian Neural ODEs

Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach

Neural Energy Casimir Control for Port-Hamiltonian Systems

Universal Approximation Property of Hamiltonian Deep Neural Networks

Neural Distributed Controllers with Port-Hamiltonian Structures

SIMBa: System Identification Methods Leveraging Backpropagation

Neural Exponential Stabilization of Control-affine Nonlinear Systems

Dwell-time based stability analysis and L-2 control of LPV systems with piecewise constant parameters and delay

L2 output feedback control of delayed linear parameter varying systems with piecewise constant parameters: A clock-dependent L-K approach

Physically Consistent Neural ODEs for Learning Multi-Physics Systems