Dinamički odziv nove adaptivne modificirane povratne Legendrove neuronske mreže upravljanja sinkronim motorom s permanentnim magnetima za električni skuter

Because an electric scooter driven by permanent magnet synchronous motor (PMSM) servo-driven system has the unknown nonlinearity and the time-varying characteristics, its accurate dynamic model is difficult to establish for the design of the linear controller in whole system. In order to conquer this difficulty and raise robustness, a novel adaptive modified recurrent Legendre neural network (NN) control system, which has fast convergence and provide high accuracy, is proposed to control for PMSM servo-driven electric scooter under the external disturbances and parameter variations in this study. The novel adaptive modified recurrent Legendre NN control system consists of a modified recurrent Legendre NN control with adaptation law and a remunerated control with estimation law. In addition, the online parameter tuning methodology of the modified recurrent Legendre NN control and the estimation law of the remunerated control can be derived by using the Lyapunov stability theorem and the gradient descent method. Furthermore, the modified recurrent Legendre NN with variable learning rate is proposed to raise convergence speed. Finally, comparative studies are demonstrated by experimental results in order to show the effectiveness of the proposed control scheme.S obzirom da električni skuter pogonjen servo sustavom sa sinkroni motor s permanentnim magnetima ima nelinearnu dinamiku i vremenski promjenjive parametre, njegov dinamički model nije jednostavno odrediti u svrhu dizajniranja linearnog regulatora. Kako bi se riješio taj problem te povećala robusnost predložen je sustav upravljanja korištenjem adaptivne modificirane povratne Legendrove neuronske mreže za upravljanje skuterom pogonjenim servo sustavom sa sinkronim motorom uz prisustvo vanjskog poremećaja i promjenjivih parametara. Predloženo upravljanje ima brzu konvergenciju i visoku preciznost. Sustav upravljanja sastoji se od modificirane povratne Legendrove neuronske moreže s adaptivnim zakonom upravljanja i estimacijom. Dodatno, \u27on-line\u27 podešavanje parametara takvog sustava može se dobiti korištenjem Ljapunovljevog teorema o stabilnosti sustava i gradijente metode. Modificirana povratne Legendrove neuronska mreža s promjenjivim vremenom učenja predložena je za povećanje brzine konvergencije. Ispravnost predložene sheme upravljanja provjerena je eksperimentalno

Terminal sliding mode control strategy design for second-order nonlinear system

This study mainly focuses on the terminal sliding mode control (TSMC) strategy design, including an adaptive terminal sliding mode control (ATSMC) and an exact-estimator-based terminal sliding mode control (ETSMC) for second-order nonlinear dynamical systems. In the ATSMC system, an adaptive bound estimation for the lump uncertainty is proposed to ensure the system stability. On the other hand, an exact estimator is designed for exact estimating system uncertainties to solve the trouble of chattering phenomena caused by a sign function in ATSMC law in despite of the utilization of a fixed value or an adaptive tuning algorithm for the lumped uncertainty bound. The effectiveness of the proposed control schemes can be verified in numerical simulations.<br /

A novel adaptive PD-type iterative learning control of the PMSM servo system with the friction uncertainty in low speeds

High precision demands in a large number of emerging robotic applications strengthened the role of the modern control laws in the position control of the Permanent Magnet Synchronous Motor (PMSM) servo system. This paper proposes a learning-based adaptive control approach to improve the PMSM position tracking in the presence of the friction uncertainty. In contrast to most of the reported works considering the servos operating at high speeds, this paper focuses on low speeds in which the friction stemmed deteriorations become more obvious. In this paper firstly, a servo model involving the Stribeck friction dynamics is formulated, and the unknown friction parameters are identified by a genetic algorithm from the offline data. Then, a feedforward controller is designed to inject the friction information into the loop and eliminate it before causing performance degradations. Since the friction is a kind of disturbance and leads to uncertainties having time-varying characters, an Adaptive Proportional Derivative (APD) type Iterative Learning Controller (ILC) named as the APD-ILC is designed to mitigate the friction effects. Finally, the proposed control approach is simulated in MATLAB/Simulink environment and it is compared with the conventional Proportional Integral Derivative (PID) controller, Proportional ILC (P-ILC), and Proportional Derivative ILC (PD-ILC) algorithms. The results confirm that the proposed APD-ILC significantly lessens the effects of the friction and thus noticeably improves the control performance in the low speeds of the PMSM

Adaptivno slijedno upravljanje u kliznom režimu rada s valićnom neuronskom mrežom za sustav s PMSM elektromotornim pogonom

This paper presents a wavelet neural network backstepping sliding mode controller (WNNBSSM) for permanent-magnet synchronous motor (PMSM) position servo control system. Backstepping sliding mode (BSSM) is utilized to guarantee favorable tracking performance and stability of the whole system, meanwhile, wavelet neural network (WNN) is used for approximating nonlinear uncertainties. The designed controller combined the merits of the backstepping sliding mode control with robust characteristics and the WNN owning the capability of artificial neural networks for online learning and the capability of wavelet decomposition for identification. An observed error compensator is developed to compensate the estimated error of the WNN and the adaptive law is derived according to Lyapunov theorem. The effectiveness of the proposed controller is investigated in simulation under different operating conditions. The simulation results demonstrate the proposed WNNBSSM controller can provide precise tracking performance and robust characteristics despite unknown parameter uncertainties and load disturbance. Moreover, an implemental wavemaker system is established to verify the effectiveness of the proposed control algorithm.U radu je predstavljen bacsktepping regulator u kliznom režimu rada za sustav upravljanja servo pozicioniranja motoromom s permanentnim magnetima (PMSM) zasnovan na valićnim neuronskim mrežama (WNNBSSM). Backstepping klizni režim rada (BSSM) korišten je za jamčenje željenih performansi slijeđenja i stabilnost cijelog sustava, dok je valićna neuronska mreža (WNN) korištena za aproksimaciju nelinearnih nesigurnosti. Sintetizirani regulator povezuje prednosti i robusne karakteristike backstepping kliznog režima upravljanja te WNN sa sposobnostima umjetnih neuronskih mreža za online učenje i mogućnost valićne dekompozicije za identifikaciju. Razvijen je kompenzator pogreške estimacije WNN i adaptivni upravljački zakon prema Ljapunovljevom teoremu. Djelotvornost predloženog regulatora istražena je kroz simulacije u različitim uvjetima rada. Rezultati simulacija pokazuju da predloženi WNNBSSM može osigurati precizna svojstva slijeđenja i robusne karakteristike unatoč nepoznatim nesigurnostima parametara i poremećaja tereta. Uz to, razvijen je izvedbeni sustav generatora valova za provjeru djelotvornosti predloženog upravljačkog algoritma

Wavelet-fuzzy speed indirect field oriented controller for three-phase AC motor drive – Investigation and implementation

Three-phase voltage source inverter driven induction motor is used in many medium- and high-power applications. Precision in speed of the motor play vital role, i.e. popular methods of direct/indirect field-oriented control (FOC) are applied. FOC is employed with proportional–integral (P-I) or proportional–integral–derivative (P-I-D) controllers and they are not adaptive, since gains are fixed at all operating conditions. Therefore, it needs a robust speed controlling in precision for induction motor drive application. This research paper articulates a novel speed control for FOC induction motor drive based on wavelet-fuzzy logic interface system. In specific, the P-I-D controller of IFOC which is actually replaced by the wavelet-fuzzy controller. The speed feedback (error) signal is composed of multiple low and high frequency components. Further, these components are decomposed by the discrete wavelet transform and the fuzzy logic controller to generate the scaled gains for the indirect FOC induction motor. Complete model of the proposed ac motor drive is developed with numerical simulation Matlab/Simulink software and tested under different working conditions. For experimental verification, a hardware prototype was implemented and the control algorithm is framed using TMS320F2812 digital signal processor (dsp). Both simulation and hardware results presented in this paper are shown in close agreement and conformity about the suitability for industrial applications

Performance monitoring of aircraft PMSM based on soft computing technique

Rigorous fault analysis (FA) referring to fault detection its level is important for maintenances to simplify as well as improves performance. The purpose of this study was to present a technique for the electronic diagnosis of switch defects in regular magnetic concurrent motors (PMSM) For Aircraft application. The performance level of the bridges of the thyristor and the device excitation diode is tracked in healthy and defective service processes. Extracted functionality in various procedures that use Decomposition of the Multi-Scale Wavelet (MSWD) to remove useful functionality. The functionalities of the MSWD are utilized to train the Autoregressive of the Nonlinear with Exogenous Model (NARX) that was Sequence of the method controlled to determine the level of the fault in an open circuit forming through a switch. Both models were evaluated and built based on simulated data, where the findings demonstrated a preferable efficacy in the diagnosis of different kinds of fault

Computer Simulation of PMSM Motor with Five Phase Inverter Control using Signal Processing Techniques

The signal processing techniques and computer simulation play an important role in the fault diagnosis and tolerance of all types of machines in the first step of design. Permanent magnet synchronous motor (PMSM) and five phase inverter with sine wave pulse width modulation (SPWM) strategy is developed. The PMSM speed is controlled by vector control. In this work, a fault tolerant control (FTC) system in the PMSM using wavelet switching is introduced. The feature extraction property of wavelet analysis used the error as obtained by the wavelet de-noised signal as input to the mechanism unit to decide the healthy system. The diagnosis algorithm, which depends on both wavelet and vector control to generate PWM as current based manage any parameter variation. An open-end phase PMSM has a larger range of speed regulation than normal PMSM. Simulation results confirm the validity and effectiveness of the switching strategy

Parameter estimation for VSI-Fed PMSM based on a dynamic PSO with learning strategies

© 1986-2012 IEEE.A dynamic particle swarm optimization with learning strategy (DPSO-LS) is proposed for key parameter estimation for permanent magnet synchronous machines (PMSMs), where the voltage-source inverter (VSI) nonlinearities are taken into account in the parameter estimation model and can be estimated simultaneously with other machine parameters. In the DPSO-LS algorithm, a novel movement modification equation with variable exploration vector is designed to effectively update particles, enabling swarms to cover large areas of search space with large probability and thus the global search ability is enhanced. Moreover, a Gaussian-distribution-based dynamic opposition-based learning strategy is developed to help the pBest jump out local optima. The proposed DPSO-LS can significantly enhance the estimator model accuracy and dynamic performance. Finally, the proposed algorithm is applied to multiple parameter estimation including the VSI nonlinearities of a PMSM. The performance of DPSO-LS is compared with several existing PSO algorithms, and the comparison results show that the proposed parameters estimation method has better performance in tracking the variation of machine parameters effectively and estimating the VSI nonlinearities under different operation conditions

Disturbance/uncertainty estimation and attenuation techniques in PMSM drives–a survey

This paper gives a comprehensive overview on disturbance/uncertainty estimation and attenuation (DUEA) techniques in permanent magnet synchronous motor (PMSM) drives. Various disturbances and uncertainties in PMSM and also other alternating current (AC) motor drives are first reviewed which shows they have different behaviors and appear in different control loops of the system. The existing DUEA and other relevant control methods in handling disturbances and uncertainties widely used in PMSM drives, and their latest developments are then discussed and summarized. It also provides in-depth analysis of the relationship between these advanced control methods in the context of PMSM systems. When dealing with uncertainties,it is shown that DUEA has a different but complementary mechanism to widely used robust control and adaptive control. The similarities and differences in disturbance attenuation of DUEA and other promising methods such as internal model control and output regulation theory have been analyzed in detail. The wide applications of these methods in different AC motor drives (in particular in PMSM drives) are categorized and summarized. Finally the paper ends with the discussion on future directions in this area