Parameter estimation of permanent magnet synchronous motor

The problem of estimating the parameters of non-salient synchronous motor with surface-mounted permanent magnets is considered. A parameterization of a nonlinear motor model is proposed, which allows obtaining a linear regressor equation using measured (estimated) values of current and voltage in the stator windings and the angular rotor position. Using the method of dynamic regressor extension and mixing, an algorithm for estimating the desired parameters in finite time is designed

Monocular depth estimation for 2D mapping of simulated environments

This article addresses the problem of constructing maps for 2D simulated environments. An algorithm based on monocular depth estimation is proposed achieving comparable accuracy to methods utilizing expensive sensors such as RGBD cameras and LIDARs. To solve the problem, we employ a multi-stage approach. First, a neural network predicts a relative disparity map from an RGB flow provided by RGBD camera. Using depth measurements from the same camera, two parameters are estimated that connect the relative and absolute displacement maps in the form of a linear regression relation. Based on a simpler RGB camera, by applying a neural network and estimates of scaling parameters, an estimate of the absolute displacement map is formed, which allows to obtain an estimate of the depth map. Thus, a virtual scanner has been designed providing Cartographer SLAM with depth information for environment mapping. The proposed algorithm was evaluated on a ROS 2.0 simulation of a simple mobile robot. It achieves faster depth prediction compared to other depth estimation algorithms. Furthermore, maps generated by our approach demonstrated a high overlap ratio with those obtained using an ideal RGBD camera. The proposed algorithm can find applicability in crucial tasks for mobile robots, like obstacle avoidance, and path planning. Moreover, it can be used to generate accurate cost maps, enhancing safety and adaptability in mobile robot navigation

Adaptive observer design for time-varying nonlinear systems with unknown polynomial parameters

Many control methods involve the use of real-time values of the vector of state variables or its estimates. The article considers the problem of state variables observer design for a nonlinear non-stationary plant of a wider class compared to the known analogs. To solve the problem, some assumptions are introduced and assume that the plant parameters are partially unknown functions of time that have a polynomial form. Each unknown parameter is polynomial functions of time with unknown coefficients. The problem of observer design is solved in a class of identification methods that involve the transformation of the original nonlinear mathematical model of the plant to a linear static regression. In this problem, instead of the usual unknown constant parameters, there are unknown functions of time which are estimated. To recover variables of unknown parameters, the method of dynamic regressor extension and mixing (DREM) is used. The method allows getting monotone estimates, as well as accelerating the convergence of estimates to true values. The proposed approach allows obtaining accurate parametrizations of a nonlinear nonstationary system, including exponentially decaying terms associated with using dynamic filters. The resulting regression equations explicitly depend on the tuning parameters and changing the values of these parameters yields a system of linearly independent regression equations, which can be decomposed then into scalar regression equations. An observer of the parameters and state variables of the system is designed on the basis of scalar regression equations and considered assumptions about models of non-stationary parameters. The application of the proposed approach allows solving the problems of restoring unmeasured variables and signals of real control systems and also makes it possible to identify unknown time-varying parameters, which in turn is an actual self-contained problem. The approach can be applied in control of chemical processes, electrical converters, as well as in a number of other technical applications

An Adaptive Observer-Based Controller Design for Active Damping of a DC Network with a Constant Power Load

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis article explores a nonlinear, adaptive controller aimed at increasing the stability margin of a direct-current (dc), small-scale, electrical network containing an unknown constant power load (CPL). Due to its negative incremental impedance, this load reduces the effective damping of the network, which may lead to voltage oscillations and even to voltage collapse. To overcome this drawback, we consider the incorporation of a controlled dc-dc power converter in parallel with the CPL. The design of the control law for the converter is particularly challenging due to the existence of unmeasured states and unknown parameters. We propose a standard input-output linearization stage, to which a suitably tailored adaptive observer is added. The good performance of the controller is validated through experiments on a small-scale network.Peer ReviewedPostprint (author's final draft

CONTROL APPROACH FOR NONLINEAR PLANT WITH PARAMETRIC UNCERTAINTIES AND INPUT DELAY

Stabilization problem for unstable nonlinear plant with parametric uncertainties and input delay is considered. A new approach is proposed that makes it possible to identify plant parameters and design the stabilization algorithm with state-feedback predictor

Frequency estimation of a sinusoidal signal with time-varying amplitude

International audienc

Robust Adaptive Sensorless Control for Permanent-Magnet Synchronous Motors

A sensorless algorithm is developed on the basis of a rotor flux observer in the stationary frame. In particular, it involves a parameter adaptive algorithm for an initial rotor flux like the observer, which was recently proposed by Bobtsov et al. (Automatica, vol. 61, Nov. 2015). In the proposed method, the flux observer is linked to the parameter estimator via a compensating term that results from parameter error. This method has a robust property against dc bias errors, i.e., it cures the inherent weakness of the pure integrator (flux observer) to dc offsets that frequently occur in current measurements and voltage estimates. The robust performance is demonstrated through simulations and experimental results. ? 1986-2012 IEEE.119sciescopu