Advances in Observer Design and Control of Nonlinear Systems

The field of control systems is witnessing significant developments in observer design and control of nonlinear systems. Recent research has focused on bridging the gap between centralized and distributed frameworks, enabling more flexible and practical applications. Additionally, data-driven approaches are being explored for robust observer synthesis and model reduction, allowing for the handling of complex systems with uncertainties. The use of lifting techniques for nonlinear sampled-data systems is also being investigated, providing a new framework for dealing with these systems. Furthermore, researchers are working on the stabilization of abstract nonlinear systems, designing nonlinear stabilizers that guarantee finite- or fixed-time stability. Noteworthy papers in this area include:

  • The paper on EDMD-Based Robust Observer Synthesis for Nonlinear Systems, which presents a data-driven framework for designing robust state observers.
  • The paper on On Finite- and Fixed-Time Stabilization of Abstract Nonlinear Systems with Well-Posedness Guarantees, which synthesizes state-feedback controllers for infinite-dimensional systems.
  • The paper on Learning Constraints from Stochastic Partially-Observed Closed-Loop Demonstrations, which presents an algorithm for learning unknown parametric constraints from locally-optimal input-output trajectory data.

Sources

Bridging Centralized and Distributed Frameworks in Unknown Input Observer Design

EDMD-Based Robust Observer Synthesis for Nonlinear Systems

Data-driven balanced truncation for linear systems with quadratic outputs

Nonlinear Sampled-data Systems--A Lifting Framework

On Finite- and Fixed-Time Stabilization of Abstract Nonlinear Systems with Well-Posedness Guarantees

On Uniformly Time-Varying Control Barrier Functions

Learning Constraints from Stochastic Partially-Observed Closed-Loop Demonstrations

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