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Found 8 papers, 1 papers with code
MHE under parametric uncertainty -- Robust state estimation without informative data
In this paper, we study state estimation for general nonlinear systems with unknown parameters and persistent process and measurement noise.
Nonlinear Functional Estimation: Functional Detectability and Full Information Estimation
Together, we present a unified framework to study functional estimation with a detectability condition, which is necessary and sufficient for the existence of a stable functional estimator, and a corresponding functional estimator design.
A Stiffness-Oriented Model Order Reduction Method for Low-Inertia Power Systems
The fast modes are then truncated in the rotated coordinate system to obtain a lower-order model with reduced stiffness.
LQG for Constrained Linear Systems: Indirect Feedback Stochastic MPC with Kalman Filtering
We present an output feedback stochastic model predictive control (SMPC) approach for linear systems subject to Gaussian disturbances and measurement noise and probabilistic constraints on system states and inputs.
A Lyapunov function for robust stability of moving horizon estimation
We provide a novel robust stability analysis for moving horizon estimation (MHE) using a Lyapunov function.
Learning-based Moving Horizon Estimation through Differentiable Convex Optimization Layers
In a numerical example of estimating temperatures of a group of manufacturing machines, we show the performance of tuning the unknown system parameters and the benefits of integrating physical state constraints in the MHE formulation.
Data-Driven Distributed Stochastic Model Predictive Control with Closed-Loop Chance Constraint Satisfaction
Distributed model predictive control methods for uncertain systems often suffer from considerable conservatism and can tolerate only small uncertainties due to the use of robust formulations that are amenable to distributed design and optimization methods.
Distributed Model Predictive Safety Certification for Learning-based Control
While distributed algorithms provide advantages for the control of complex large-scale systems by requiring a lower local computational load and less local memory, it is a challenging task to design high-performance distributed control policies.
