3 citations · 13 across the 26 of their papers we have counts for
26 papers
Estimation of Unknown Parameters in Presence of Perturbations and Noises with Application to GPEBO Design
Anton Glushchenko, Konstantin Lastochkin
A problem of online estimation of unknown parameters is considered for a linear regression equation, which is affected by an additive perturbation that can be caused by measurement…
Composite Adaptive Disturbance Rejection in Robotics via Instrumental Variables based DREM
Anton Glushchenko, Konstantin Lastochkin
In this paper we consider trajectory tracking problem for robotic systems affected by unknown external perturbations. Considering possible solutions, we restrict our attention to c…
Adaptive Reconstruction of Nonlinear Systems States via DREM with Perturbation Annihilation
Anton Glushchenko, Konstantin Lastochkin
A new adaptive observer is proposed for a certain class of nonlinear systems with bounded unknown input and parametric uncertainty. Unlike most existing solutions, the proposed app…
Unbiased Parameter Estimation via DREM with Annihilators
Anton Glushchenko, Konstantin Lastochkin
In adaptive control theory, the dynamic regressor extension and mixing (DREM) procedure has become widespread as it allows one to describe major of adaptive control problems in uni…
Instrumental Variables based DREM for Online Asymptotic Identification of Perturbed Linear Systems
Anton Glushchenko, Konstantin Lastochkin
Existing online continuous-time parameter estimation laws provide exact (asymptotic/exponential or finite/fixed time) identification of dynamical linear/nonlinear systems parameter…
Exact Asymptotic Estimation of Unknown Parameters of Perturbed LRE with Application to State Observation
Anton Glushchenko, Konstantin Lastochkin
Most identification laws of unknown parameters of linear regression equations (LRE) ensure only boundedness of a parametric error in the presence of additive perturbations, which i…
Parameter Estimation-Based States Reconstruction of Uncertain Linear Systems with Overparameterization and Unknown Additive Perturbations
Anton Glushchenko, Konstantin Lastochkin
The problem of state reconstruction is considered for uncertain linear time-invariant systems with overparameterization, arbitrary state-space matrices and unknown additive perturb…
Sensorless Adaptive Vibration Suppression in Two-Mass Systems via Joint Estimation of Controller Parameters and System States
Anton Glushchenko, Konstantin Lastochkin
The scope of this study is to develop a novel sensorless adaptive vibration suppression controller for two-mass systems with joint estimation of states and controller parameters. U…
Parameter Estimation-Based Extended Observer for Linear Systems with Polynomial Overparameterization
Anton Glushchenko, Konstantin Lastochkin
We consider a class of uncertain linear time-invariant overparametrized systems affected by bounded disturbances, which are described by a known exosystem with unknown initial cond…
Parameter Estimation-Based Observer for Linear Systems with Polynomial Overparametrization
Anton Glushchenko, Konstantin Lastochkin
An adaptive state observer is proposed for a class of overparametrized uncertain linear time-invariant systems without restrictive requirement of their representation in the observ…
Extended Adaptive Observer for Linear Systems with Overparametrization
Anton Glushchenko, Konstantin Lastochkin
Exponentially stable extended adaptive observer is proposed for a class of linear time-invariant systems with unknown parameters and overparameterization. It allows one to reconstr…
Monotonous Parameter Estimation of One Class of Nonlinearly Parameterized Regressions without Overparameterization
Anton Glushchenko, Konstantin Lastochkin
The estimation law of unknown parameters vector is proposed for one class of nonlinearly parametrized regression equations $y\left( t \right) = Ω\left( t \right)Θ\left( θ\right…
Exponentially Stable Adaptive Observation for Systems Parameterized by Unknown Physical Parameters
Anton Glushchenko, Konstantin Lastochkin
The method to design exponentially stable adaptive observers is proposed for linear time-invariant systems parameterized by unknown physical parameters. Unlike existing adaptive so…
Exponentially Stable Adaptive Control Under Semi-PE Condition
Anton Glushchenko, Konstantin Lastochkin
A novel method of exponentially stable adaptive control to compensate for matched parametric uncertainty under a mild condition of semi-persistent excitation (s-PE) of a regressor…
Exponentially Stable MRAC of MIMO Switched Systems with Matched Uncertainty and Completely Unknown Control Matrix
Anton Glushchenko, Konstantin Lastochkin
In this paper an attempt is made to extend the concept of the exponentially stable adaptive control to one class of multi-input-multi-output (MIMO) plants with matched nonlinearity…
Exponentially Stable Adaptive Optimal Control of Uncertain LTI Systems
Anton Glushchenko, Konstantin Lastochkin
A novel method of an adaptive linear quadratic (LQ) regulation of uncertain continuous linear time-invariant systems is proposed. Such an approach is based on the direct self-tunin…
Unknown Piecewise Constant Parameters Identification with Exponential Rate of Convergence
Anton Glushchenko, Konstantin Lastochkin
The scope of this research is the identification of unknown piecewise constant parameters of linear regression equation under the finite excitation condition. Compared to the known…
Exponentially Stable Adaptive Control of MIMO Systems with Unknown Control Matrix
Anton Glushchenko, Konstantin Lastochkin
The scope of this research is a problem of the direct model reference adaptive control of linear time-invariant multi-input multi-output (MIMO) plants without any a priori knowledg…
Exponentially Convergent Direct Adaptive Pole Placement Control of Plants with Unmatched Uncertainty under FE Condition
Anton Glushchenko, Konstantin Lastochkin
A new method of direct adaptive pole placement control (APPC) is developed for plants with unmatched uncertainty, which linearly depends on a state vector. It guarantees the expone…
Relaxation of Conditions for Convergence of Dynamic Regressor Extension and Mixing Procedure
Anton Glushchenko, Konstantin Lastochkin
A generalization of the dynamic regressor extension and mixing procedure is proposed, which, unlike the original procedure, first, guarantees a reduction of the unknown parameter i…
Robust Time-Varying Parameters Estimation Based on I-DREM Procedure
Anton Glushchenko, Konstantin Lastochkin
We consider a class of systems with time-varying parameters, which are written as linear regressions with bounded disturbances. The task is to estimate such parameters under the co…
Exponential Convergence of Piecewise-Constant Parameters Identification under Finite Excitation Condition
Anton Glushchenko, Vladislav Petrov, Konstantin Lastochkin
A problem of identification of piecewise-constant unknown parameters of a linear regression equation (LRE) is considered. Such parameters change their values over the interval of t…
Normalization of regressor excitation as a part of dynamic regressor extension and mixing procedure
Anton Glushchenko, Vladislav Petrov, Konstantin Lastochkin
The method of excitation normalization of the regressor, which is used in the estimation loop to solve the plant identification problem, is proposed. It is based on the dynamic reg…
I-DREM MRAC with Time-Varying Adaptation Rate & No A Priori Knowledge of Control Input Matrix Sign to Relax PE Condition
Anton Glushchenko, Vladislav Petrov, Konstantin Lastochkin
The known dynamic regressor extension and mixing method (DREM) is combined with the proposed filter of a new type, which uses the integration operation with forgetting, and the rec…