Nonsingular terminal sliding mode control for the speed regulation of permanent magnet synchronous motor with parameter uncertainties

The drive performance of permanent magnet synchronous motor (PMSM) can be deteriorated due to various disturbances. In this paper, the problem of speed control for a PMSM system with parameter uncertainties is investigated. A new control algorithm based on nonsingular terminal sliding mode control (NTSMC) is proposed, where the controller is developed for speed regulation. Compared with conventional strategies, this new controller provides improved performance for speed regulation of PMSM when subject to parameter uncertainties, in that it achieves fast dynamic response and strong robustness. Simulation studies are conducted to verify the effectiveness of this proposed method

High performance position control for permanent magnet synchronous drives

In the design and test of electric drive control systems, computer simulations provide a useful way to verify the correctness and efficiency of various schemes and control algorithms before the final system is actually constructed, therefore, development time and associated costs are reduced. Nevertheless, the transition from the simulation stage to the actual implementation has to be as straightforward as possible. This document presents the design and implementation of a position control system for permanent magnet synchronous drives, including a review and comparison of various related works about non-linear control systems applied to this type of machine. The overall electric drive control system is simulated and tested in Proteus VSM software which is able to simulate the interaction between the firmware running on a microcontroller and analogue circuits connected to it. The dsPIC33FJ32MC204 is used as the target processor to implement the control algorithms. The electric drive model is developed using elements existing in the Proteus VSM library. As in any high performance electric drive system, field oriented control is applied to achieve accurate torque control. The complete control system is distributed in three control loops, namely torque, speed and position. A standard PID control system, and a hybrid control system based on fuzzy logic are implemented and tested. The natural variation of motor parameters, such as winding resistance and magnetic flux are also simulated. Comparisons between the two control schemes are carried out for speed and position using different error measurements, such as, integral square error, integral absolute error and root mean squared error. Comparison results show a superior performance of the hybrid fuzzy-logic-based controller when coping with parameter variations, and by reducing torque ripple, but the results are reversed when periodical torque disturbances are present. Finally, the speed controllers are implemented and evaluated physically in a testbed based on a brushless DC motor, with the control algorithms implemented on a dsPIC30F2010. The comparisons carried out for the speed controllers are consistent for both simulation and physical implementation

High performance position control of permanent magnet synchronous drives

In the design and test of electric drive control systems, computer simulations provide a useful way to verify the correctness and efficiency of various schemes and control algorithms before the final system is actually constructed, therefore, reducing development time and associated costs. Nevertheless, the transition from the simulation stage to the actual implementation has to be as straightforward as possible. This paper presents the design and implementation of a position control system for permanent magnet synchronous drives using the dsPIC33FJ32MC204 microcontroller as the target processor to implement the control algorithms. The overall system is simulated and tested in Proteus VSM software which is able to simulate the interaction between the firmware running on the microcontroller and the analogue circuits connected to it. The electric drive model is developed using elements present in the Proteus VSM library. As in any high-performance AC electric drive system, field oriented control is applied. The complete control system is distributed in three control loops, namely torque, speed and position. A standard PID control system, and a hybrid control system based on fuzzy logic, are implemented and tested. The natural variation of motor parameters, such as winding resistance and magnetic flux, are also simulated. Comparisons between the two control schemes are carried out for speed and position control using different error measurements, such as, integral square error, integral absolute error and root mean squared error. Comparison results show a superior performance of the fuzzy-logic-based controller when coping with parameter variations, and by reducing torque ripple, but the results are reversed when periodical torque disturbances are present.N/

A comparative real-time speed control of PMSM with Fuzzy Logic and ANN based vector controller

This paper presents, analyzed real-time speed control of Permanent Magnet Synchronous Motor (PMSM) under constant load by using Fuzzy Logic (FL) controller and recurrent Artificial Neural Network (ANN) controller. A closed loop PMSM drive system is improved using the mathematical model of the PMSM in Matlab / Simulink. Two types of controllers are used; the first controller is the real-time FL controller and the second controller is a real-time recurrent ANN controller in terms of smoother speed response. Whole drive systems is simulated in Matlab/Simulink program. The simulation results show that the focused ANN controller produce considerable control performance compare to the FL controller on controlling speed reference variation

Design, simulation and implementation of a PID vector control for EHVPMSM for an automobile with hybrid technology

This work proposes a Model design simulation and implementation of a novel engine of an Electric Hybrid Vehicle of Permanent Magnet Synchronous Motor (EHVPMSM) based on field oriented vector control. The experimental analysis was carried out using: automotive motor control MTRCKTSPS5604P, 3-Phase PMSM coded of a single Motor Control Kit with MPC5604P MCU and simulation with Simulink. Therefore, the direct torque control can be obtained by adjusting the magnitude and phase angle of the stator flux linkage to match the vector torque required by the load as fast as possible. This eradicates the stress of charging the vehicle battery. It automatically charges when it is connected to the main supply of the EHVPMSM. The electromagnetic torque can be increased from 0 Nm to 6.7 Nm in approximately 340 μs. The response of speed transient was from −2100 rpm to +2100 rpm in 100 ms of 6.7 Nm torque limit. This is a novel way of conserving the energy consumption in a vehicle, which conserves space and weight and minimizes cost as it is simply done with low-cost materials. In this research, a new mathematical model is proposed for the direct and quadrature axis of the current to control the speed mechanism for the engine. Computer simulation ensures experimental validation of the system with a percentage error of 4.5%. The methodology employed to control the system was with the use of various sensors and software controller, this can be easily implemented in industry and institutional laboratory of learning. Keywords: Permanent magnet machines, PID, EHVPMSM, Vector control, Hybrid vehicl

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

Neuro-Controller Design by Using the Multifeedback Layer Neural Network and the Particle Swarm Optimization

In the present study, a novel neuro-controller is suggested for hard disk drive (HDD) systems in addition to nonlinear dynamic systems using the Multifeedback-Layer Neural Network (MFLNN) proposed in recent years. In neuro-controller design problems, since the derivative based train methods such as the back-propagation and Levenberg-Marquart (LM) methods necessitate the reference values of the neural network’s output or Jacobian of the dynamic system for the duration of the train, the connection weights of the MFLNN employed in the present work are updated using the Particle Swarm Optimization (PSO) algorithm that does not need such information. The PSO method is improved by some alterations to augment the performance of the standard PSO. First of all, this MFLNN-PSO controller is applied to different nonlinear dynamical systems. Afterwards, the proposed method is applied to a HDD as a real system. Simulation results demonstrate the effectiveness of the proposed controller on the control of dynamic and HDD systems

Nonlinear Decoupling Sliding Mode Control of Permanent Magnet Linear Synchronous Motor Based on α-th Order Inverse System Method

AbstractIn this paper, a nonlinear dynamic decoupling controller is proposed for the permanent magnet linear synchronous motor (PMLSM) servo system to improve dynamic operating performance. Firstly, the reversibility of the PMLSM mathematical model is analyzed, and it is proved that the system is reversible. Then an inverse system method is applied to the PMLSM servo system, and it is decoupled into a linear velocity subsystem and a linear current subsystem based on the α-th order inverse system method. Considering the both ideal linear subsystems are sensitive to parameter disturbances and various disturbances, a variable rate reaching law approach based subsystem sliding mode controller for higher system stability and robustness is proposed. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control method