To reduce the steady-state torque ripples for permanent-magnet synchronous motor drives with predictive torque control (PTC), this paper proposes a modified PTC algorithm based on the extended control set (ECS-PTC). In the proposed algorithm, more candidate voltage vectors are extended to the control set, which forms the ECS, and then, the torque control precision could be improved and the torque ripples will be reduced. More attractive thing is that a new voltage vector synthesis method is deigned in ECS-PTC, and the corresponding predictive model and cascaded predictive algorithm are set up on the basis of this method. With the help of them, the optimal vector can be selected from ECS in a shorter control period of time, and its amplitude is also optimized at the same time to achieve high-precision control effects for torque and flux. In addition, the three-phase duty ratios of the output vector can be determined in ECS-PTC directly, without the help of space vector pulse width modulation. The experimental results show that ECS-PTC has excellent dynamic torque performance, and it also has more stable torque control performance and better execution efficiency at the same time.

Torque Ripple Minimization of Predictive Torque Control for PMSM With Extended Control Set

Three-phase interior permanent magnet synchronous motor (IPMSM) drive systems have gotten widely used recently in various applications due to their speed regulation characteristics and high power density. Compared with classical field-oriented control (FOC) and flux-weakening control, model predictive control (MPC) technique is more efficient and effective in achieving excellent performance without complicated controller tuning, but with explicit constraints. Yet, the optimum application of the MPC algorithm in a machine control system is still in the exploratory stage. In order to lower the current and torque ripples, this paper retains a modulator, but replaces all the proportional integrate control loops, which are contained in the conventional FOC systems with a single MPC controller. Moreover, we adopt a brand-new linearization approach to tackle the strong coupled nonlinear IPMSM mathematical model, obtaining an improved linear plant model, which is suitable for the motor with constant load torque. When it comes to flux-weakening control, the required d -axis current is calculated, after which it is used as the input of the proposed MPC controller, abandoning the previous natural field weakening method. Finally, for the purpose of lowering the dynamic speed and current overshoot, a further constraint in the change rate of manipulated variables is discussed. The improved control algorithm has been verified in both simulation and experiment.

IPMSM Model Predictive Control in Flux-Weakening Operation Using an Improved Algorithm

When an emergency happens to electric vehicles, the voltage of the dc-bus capacitor, which is an important part of the permanent magnet synchronous machine (PMSM) drive system, requires to be reduced as quickly as possible. Recently, a new idea of directly using the windings to discharge the capacitor has come forth. This paper proposes a physical energy flow model (EFM) to explain explicitly the winding-based discharge mechanism first. In the EFM, the performance characteristics of the classical winding-based discharge scheme are evaluated, but it is found that the discharge time will not be qualified when the machine rotor inertia is large and the system safe current is small. In order to reject intense voltage surge, a current control algorithm is proposed to bleed the capacitor voltage. Moreover, the proposed current control method shortens the discharge period to below 3 s for the system studied. The proposed discharge algorithm is verified by the experiment that is conducted on a three-phase PMSM drive system.

A DC-Bus Capacitor Discharge Strategy for PMSM Drive System With Large Inertia and Small System Safe Current in EVs

A robust adaptive backstepping sliding mode control (ABSMC) with recurrent wavelet fuzzy neural network (RWFNN) is proposed for the speed regulation of a six-phase permanent magnet synchronous motor (PMSM) demonstrating parameter perturbations and load disturbances. First, a motor drive system model with lumped uncertainty is developed. Then, a nonlinear robust speed controller using ABSMC and $H_{\infty }$ theory is presented. In this technique, ABSMC is employed to guarantee the speed tracking and parameter perturbation suppression; meanwhile, nonlinear $H_{\infty }$ is utilized to minimize the influence of dynamic load disturbances on its tracking output. In addition, an uncertainty observer based on the RWFNN is designed to estimate the unknown and improve the robustness of motor drive system further. Ultimately, simulations and AppSIM simulator-based experimental results both indicate that the proposed control scheme can perfectly compensate the parameter perturbations and load disturbances while maintaining speed tracking precision.

Robust Adaptive Backstepping Sliding Mode Control for Six-Phase Permanent Magnet Synchronous Motor Using Recurrent Wavelet Fuzzy Neural Network

Model predictive control (MPC) is a very attractive candidate to replace standard field-oriented control algorithms for electrical motors. We demonstrate that it is possible to implement an MPC algorithm for continuous control set (CCS-MPC), with both inputs and states constraints, in which the associated quadratic programming (QP) problem is solved online, even on the computationally limited platforms used in control of electrical motors. We detail the implementation of an active-set algorithm to solve efficiently the associated QP problem. Moreover, by exploiting recent results on active-set solver certification, we are able to assess the computational complexity of the online optimization algorithm, providing the exact worst-case solution time. The controller is experimentally tested on an embedded control unit for the torque regulation of a permanent magnet synchronous motor and benchmarked against explicit MPC. Computational feasibility, low-memory occupancy, and worst-case certification are achieved, fulfilling all the requirements of embedded control.

Embedded Model Predictive Control With Certified Real-Time Optimization for Synchronous Motors

Permanent-magnet synchronous machine (PMSM) drives have become popular for motion control applications due to their performance and high torque-to-weight ratio. The complex task of PMSM control in high-performance applications is currently usually resolved with classical vector control. Modern control techniques such as model predictive control (MPC) can provide significant benefits over field-oriented control, especially with straightforward controller tuning and constraints handling. Unfortunately, these new algorithms usually suffer from problems with their computational complexity. In this paper, we present a PMSM control method based on an explicit MPC with a novel linearization and constraints handling method, allowing natural field weakening. The algorithm was designed with respect to computationally feasible implementation in the control hardware. The proposed control algorithm has been proved and successfully verified in both simulation and implementation on a real motor.

PMSM Model Predictive Control With Field-Weakening Implementation

The synchronous reluctance motor is becoming a very attractive alternative to the ac induction machine. This is due to the lack of rare-earth metals in their construction and a higher efficiency, which was brought about by recent progress in rotor design. However, in order to achieve an efficient and low-cost operation of the synchronous reluctance motor drive, an adaptive sensorless algorithm should be utilized to cope with machine nonlinearities. This article describes an adaptive observer, which can provide an estimation of rotor position and speed, as well as core loss and inductance parameters. A modified pulsewidth modulation (PWM) switching scheme and a current derivative measurement method are proposed, together with an extended Kalman Filter design. Experimental results are shown to demonstrate the method performance and feasibility.

/pdf/synchronous-reluctance-motor-parameter-and-state-estimation-2lzeifu3g5.pdf

Synchronous Reluctance Motor Parameter and State Estimation Using Extended Kalman Filter and Current Derivative Measurement

This paper shows implementation of an explicit linear model predictive control (MPC) of surface mounted permanent magnet synchronous motor (SMPMSM) on field-programmable gate array (FPGA) and dual-core processor. The most notable advantage of an MPC algorithm, in comparison with standard field oriented control (FOC), is the correct constraints handling and natural way of control objective definition. The selected MPC algorithm, described in first part of this article, also achieves field weakening ability, thanks to the modified linearization and constraints handling. General disadvantage of an MPC is relatively high computational complexity. The requirement of high-performing hardware is met by implementation on FPGA and dual-core processor. The experimental validation of this approach is the focus of the second part of this paper.

Zbynek Mynar

Papers

PMSM Model Predictive Control With Field-Weakening Implementation

Synchronous Reluctance Motor Parameter and State Estimation Using Extended Kalman Filter and Current Derivative Measurement

Model predictive control of SPMSM based on FPGA and processor