Related papers: Comparison of linear observation techniques for ro…
The paper introduces a new estimation method for the standard linear regression model. The procedure is not driven by the optimisation of any objective function rather, it is a simple weighted average of slopes from observation pairs. The…
This paper introduces a novel framework for optimizing observer-based soft sensors through dynamic causality analysis. Traditional approaches to sensor selection often rely on linearized observability indices or statistical correlations…
We propose a novel method for the estimation of rotor position, speed, and torque of a motor-pump system consisting of a progressive cavity pump (PCP) driven by an induction motor which operates under V/f open-loop control. We compute the…
This paper proposes a novel Disturbance Observer, termed the High-Performance Disturbance Observer, which achieves more accurate disturbance estimation compared to the conventional disturbance observer, thereby delivering significant…
Dynamical systems can confront one of two extreme types of disturbances: persistent zero-mean independent noise, and sparse nonzero-mean adversarial attacks, depending on the specific scenario being modeled. While mean-based estimators like…
Effective and causal observable functions for low-order lifting linearization of nonlinear controlled systems are learned from data by using neural networks. While Koopman operator theory allows us to represent a nonlinear system as a…
This paper proposes a novel distributed interval observer design for linear time-invariant (LTI) discrete-time systems subject to bounded disturbances. In the proposed observer algorithm, each agent in a networked group exchanges…
The paper deals with the observer design problem for a wide class of triangular time-varying nonlinear systems, with unobservable linearization. Sufficient conditions are derived for the existence of a Luenberger-type observer, when it is a…
We address the problem of state estimation and attack isolation for general discrete-time nonlinear systems when sensors are corrupted by (potentially unbounded) attack signals. For a large class of nonlinear plants and observers, we…
Proper operation of the Active Disturbance Rejection (ADR) controller requires a precise determination of the so-called total disturbance affecting the considered dynamical system, usually estimated by the Extended State Observer (ESO). The…
In this paper, a novel adaptive-gain Second Order Sliding Mode (SOSM) observer is proposed for multicell converters by considering it as a class of hybrid systems. The aim is to reduce the number of voltage sensors by estimating the…
This paper presents a novel linear robust Youla controller output observation system for tracking vehicle motion trajectories using a simple nonlinear kinematic vehicle model, supplemented with positional data from a radar sensor. The…
In this paper we consider the distributed estimation problem for continuous-time linear time-invariant (LTI) systems. A single linear plant is observed by a network of local observers. Each local observer in the network has access to only…
This paper is devoted to analyzing the observer convergence rate for a class of linear control systems in a Hilbert space. To characterize the polynomial stability of the observer error system, we apply the spectral theory of linear…
Previous research has explored the computational expressivity of Transformer models in simulating Boolean circuits or Turing machines. However, the learnability of these simulators from observational data has remained an open question. Our…
This paper proposes new practical design tools for the robust motion control systems based on disturbance observer (DOB). Although DOB has long been used in several motion control applications, it has insufficient analysis and design tools.…
We consider the problem of distributed state estimation of a linear time-invariant (LTI) system by a network of sensors. We develop a distributed observer that guarantees asymptotic reconstruction of the state for the most general class of…
We consider the problem of learning observation models for robot state estimation with incremental non-differentiable optimizers in the loop. Convergence to the correct belief over the robot state is heavily dependent on a proper tuning of…
An observer based adaptive detection methodology (ADM) is proposed for estimating frequency and its rate of change (RoCoF) of the voltage and/or current measurements acquired from an instrument transformer. With guaranteed convergence and…
This article describes the discrete-time modeling approach for interturn short circuits in interior permanent magnet synchronous motors with concentrated windings that facilitate model-based fault diagnostics and mitigation. A…