Algebraic Parameter Identification Research Articles

Fast Terminal Synergetic Speed Control for SMPMSM Drive System

For the surface‐mounted permanent magnet synchronous motor (SMPMSM) drive system, a fast terminal synergetic speed control (FTSSC) is proposed to improve its speed dynamic performance and robustness. In the proposed control scheme, the ultra‐local model of SMPMSM drive system, which considers motor parameter uncertainties and unknown disturbances, is set up based on the algebraic parameter identification method. Then, the nonlinear terminal manifold is designed to achieve speed rapid convergence in finite time, moreover, the fast synergetic speed control law that satisfies the voltage and current constraints of SMPMSM drive system is derived. Meanwhile, the Lyapunov function is designed to prove the stability of the proposed fast terminal synergetic speed‐controlled SMPMSM drive system. Finally, the effectiveness and feasibility of proposed control is verified through the experimental research of SMPMSM drive system. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Sensorless control of SMPMSM drive system integrated the differential algebra with the algebraic parameter identification

The differential algebraic sensorless control has the technical features of clear concept and low calculation amount. However, this method is particularly sensitive to motor parameters. Therefore, a differential algebraic sensorless control strategy combined with algebraic parameter identification is proposed and applied to surface mounted permanent magnet synchronous motor (SMPMSM) drive system in this paper. Without the signal injection, the proposed method can promptly and accurately realize the online simultaneous identification of stator resistance and inductance based on the different torque conditions of system, and then the motor parameters used in the differential algebraic sensorless control are updated in time. In addition, the tracking differentiator is designed to accurately obtain the first-order differential of the stator current signal with noises for rotor position estimation. Finally, the effectiveness of the proposed sensorless control method is validated by the simulation results.

Performance optimisation of discrete time linear active disturbance rejection control approach

In the framework of the linear active disturbance rejection control (LADRC) approach, all the uncertainties, including the perturbed internal model parameters and time-varying external disturbances, can be estimated by constructing an extended state observer, and then cancelled in real time. However, the parameter tuning of the approach is an extremely challenging mission. In this paper, the model parameters of the controlled servo motor control system are identified by employing the algebraic parameter identification approach. Afterward, the bacteria foraging optimisation (BFO) algorithm, and the particle swarm optimisation (PSO) algorithm are both proposed to optimise the performance of the system driven by the LADRC approach in light of the identified model of the servo motor. The BFO and PSO algorithms and LADRC approach have been extensively applied in optimised control and networked systems. Extensive simulation results and experimental tests are given to demonstrate that the proposed approaches are effective and efficient for the performance optimisation of the LADRC approach.

Frequency-shifting-based stable on-line algebraic parameter identification of linear systems

In this paper a new approach to algebraic parameter identification of the linear SISO systems is proposed. The standard approach to the algebraic parameter identification is based on the algebraic derivatives in Laplace domain as the main tool for algebraic manipulations like elimination of the initial conditions and generation of linearly independent equations. This approach leads to the unstable time-varying state-space realization of the filters for the on-line parameter estimation. In this paper, the finite difference and shift operators in combination with the frequency-shifting property of Laplace transform is applied instead of algebraic derivatives. Resulting state-space realization of the estimator filters is asymptotically stable and doesn’t require switch-of mechanism to prevent overflow of the estimator variables. The proposed method is especially suitable for applications in closed-loop on-line identification where the stable behavior of the estimators is a necessary requirement. The efficiency of the proposed algorithm is illustrated on three simulation examples.

Adaptive Passivity-Based Control of a Photovoltaic/Battery Hybrid Power Source via Algebraic Parameter Identification

This paper proposes a new adaptive passivity-based controller (APBC) for hybrid power sources (HPSs). The HPS considered in this paper consists of a photovoltaic (PV) array, a battery, a boost converter, and a bidirectional boost converter. Dynamic equations of the system are derived based on the state-space averaging method. Afterward, a new adaptive passivity-based controller is designed using the energy shaping and damping injection method. An algebraic parameter identifier is used for estimation of the unknown parameters of the HPS, i.e., PV array voltage, battery voltage, and load resistance. Asymptotic stability of the proposed system is ensured. Subsequently, a conventional APBC is considered in order to compare the results of the proposed controller. The robustness of the proposed system toward variations of the input voltages and load resistance is tested by simulations and experiments.

On the algebraic parameter identification of vibrating mechanical systems

This paper proposes an on-line algebraic parameter identification method in time domain for multiple degrees-of-freedom linear mass–spring–damper mechanical systems using position measurements. The constant parameters of mass, stiffness and damping are algebraically estimated from transient real-time measurements. Analytical and numerical results prove the accuracy and efficiency of the proposed identification approach for reference trajectory tracking control tasks on multiple-input–multiple-output vibrating mechanical systems with unknown constant parameters.

Robust Nonlinear Adaptive Control of a “Boost” Converter via Algebraic Parameter Identification

This paper presents the design of a robust nonlinear adaptive controller for trajectory tracking maneuvers of the nonminimum phase output voltage on a dc-to-dc “boost” power converter with uncertain time-varying parameters. The unknown parameter variations concern both the resistive load and the input voltage supply value. A generalized proportional-integral (GPI) indirect control, exploiting the flatness property of the system, performs fast adaptations on the feedback controller and on the desired output reference trajectory owing to a fast online algebraic parameter identification procedure. The required updating of the algebraic parameter identification process is periodically triggered to cope with the time variations of the unknown plant parameters. The proposed control scheme is shown to be quite effective for handling the significant plant uncertainties when tested through experiments on a laboratory prototype. An adaptive linear quadratic regulator optimal control based on feedback linearization was designed in order to compare its performance against that of the proposed GPI adaptive scheme. An integral square error index was used for the evaluation.

Active Unbalance Control of Rotor Systems Using On‐Line Algebraic Identification Methods

AbstractAn active control scheme based on compensation of perturbation force signals is proposed to balance rotating machinery with unknown system parameters and variable operation speed. The algebraic parameter identification methodology is applied to estimate the mass, stiffness, damping, rotor eccentricity, and on‐line reconstruction of the unknown centrifugal forces induced by rotor unbalance. In addition, an asymptotic estimation scheme of perturbation signals, which requires position measurements only, is proposed as another alternative to estimate such unbalance forces. Our design methodology considers that a disturbance signal can be locally approximated by a family of Taylor polynomials. Some simulation results are included to show the dynamic and robust performance of the active balancing scheme and perturbation force estimation methods proposed for a rotor‐magnetic bearing system.

Algebraic parameters identification of DC motors: methodology and analysis

A fast, non-asymptotic, algebraic parameter identification method is applied to an uncertain DC motor to estimate the uncertain parameters: viscous friction coefficient and inertia. In this work, the methodology is developed and analysed, its convergence, a comparative study between the traditional recursive least square method and the algebraic identification method is carried out, and an analysis of the estimator in a noisy system is presented. Computer simulations were carried out to validate the suitability of the identification algorithm.

Algebraic Parameter Identification and Asymptotic Estimation of the Load of a Boost Converter

A comparison between an algebraic parameter identification algorithm for the load of a boost converter and classical asymptotic observers for the same purpose is provided. Two asymptotic observers are presented, and an algebraic identification algorithm is derived. For the latter, two implementations in the software for a digital signal processor are discussed, and experimental results are given which highlight the properties of both approaches.