In the double-vector-based model predictive torque control (MPTC), two voltage vectors are applied in one control period. Due to a large number of possible combinations among voltage vectors, the determination of optimal voltage vector pair is often complicated. This article proposes a new approach to reduce the number of candidate active voltage vectors. The concept is to preselect the active voltage vectors according to the stator flux vector error. The proposed MPTC is implemented in a stationary frame and avoids the complicated coordinate transformation as well as the tangent inverse calculations. Furthermore, a promising approach is conceived for calculating the duty cycle of active voltage vector, which can contribute to the less dependence on the system model, thus alleviating the sensitivity to motor parameter variations. To further improve the steady-state performance, a modified three-vector-based MPTC is newly put forward. Meanwhile, the proposed duty cycle calculation method is used to achieve the deadbeat control of torque and stator flux. Theoretical analyses and experimental results are given to verify the effectiveness of the proposed MPTC methods.

Low-Complexity Multivector-Based Model Predictive Torque Control for PMSM With Voltage Preselection

Time delay of the finite control set model predictive current control caused by a large amount of calculations will degrade the control performance of the permanent magnet synchronous machine system. This letter first demonstrates the delay issue at length. Then, a new direct compensation method, by predicting the current variation within the delay time, is proposed. In comparison with the traditional two-step prediction strategy, the novel method is also easy to implement and can single out the best switching state in each period for optimal control. Simulation and experimental results are presented to verify the effectiveness of the proposed method.

Novel Compensation Strategy for Calculation Delay of Finite Control Set Model Predictive Current Control in PMSM

In conventional model predictive torque control (MPTC), only one voltage vector (VV) is applied during a whole control period, thus causing a large torque ripple. To improve the steady-state performance, some two-vector-based control schemes have been proposed. However, the selection of the optimal VV pair is complex as well as has a large computational burden, and the improvement of performance is still limited by the direction and amplitude of the output VV. This article proposes a low-complexity three-vector-based MPTC for SPMSM drives, which can precisely determine the appropriate active voltage vectors (AVVs) with the predicted torque error. Then, a modified switching table is developed to directly select the AVVs, thus greatly reducing the complexity and computational burden of the algorithm. To obtain a better steady-state performance, a duty cycle calculation method based on torque and stator flux difference parameters is newly proposed to achieve the deadbeat control of torque and stator flux. And then, the experimental comparisons with the double-vector-based MPTC are conducted. The results show that the proposed MPTC can effectively reduce the steady-state torque ripple while maintaining a good dynamic performance as well as almost a fixed switching frequency for all speed ranges.

A Low-Complexity Three-Vector-Based Model Predictive Torque Control for SPMSM

This article proposes a fault-tolerant finite control set model predictive control for a five-phase permanent-magnet (PM) motor drive, which offers reduced computation burden and simplified control model. The virtual voltage vectors synthesized from two basic vectors are used to reduce the computation burden. Meanwhile, the steady-state performance is improved. Combining with a reduced decoupling matrix, the discrete model of the five-phase PM motor before and after fault remains unchanged. So, the reconfiguration of the control structure is minimal. Then, a control set and corresponding switching sequence are proposed, which are very suitable for the real-time system. Finally, the validity of the proposed fault-tolerant control is proved by experiments.

Simplified Fault-Tolerant Model Predictive Control for a Five-Phase Permanent-Magnet Motor With Reduced Computation Burden

Modular multilevel converters (MMCs) have been widely used in recent years due to their superior advantages in medium/high-voltage applications. The finite control set model predictive control (FCS-MPC) has shown high dynamic responses for the control of the MMC. However, with the existing FCS-MPC algorithms, the steady-state performances are degraded due to the irregular pulse patterns, especially under unbalanced grid conditions. In this paper, based on the discrete state-space model of the MMC, a modulated MPC (M2PC) algorithm is proposed to overcome the aforementioned drawback of the FCS-MPC. Compared to FCS-MPC algorithms, the M2PC accurately calculates and tracks the reference circulating current and adopts an effective modulation method, and, in turn, the proposed M2PC can obtain enhanced steady-state ac-side/circulating currents control performances under unbalanced grid conditions, with the high dynamic-state performances of the conventional FCS-MPC algorithms reserved. Experimental results regarding one typical FCS-MPC algorithm and the proposed M2PC algorithm are comparatively evaluated in detail for the steady/dynamic-state performances and pulse patterns by a downscaled prototype. The effectiveness and feasibility of the proposed M2PC are clearly validated by the experimental results.

Modulated Model Predictive Control for MMC-Based Active Front-End Rectifiers Under Unbalanced Grid Conditions

This paper proposes an alternative strategy of finite-control-set model-predictive torque control (MPTC) to reduce the computational burden and the torque ripple and decouple the switching frequency from the controller sampling time. An improved discrete space-vector modulation (DSVM) technique is utilized to synthesize a large number of virtual voltage vectors. The deadbeat (DB) technique is used to optimize the voltage vector selection process, avoiding enumerating all the feasible voltage vectors. With this proposed method, only three voltage vectors are tested in each predictive step. Based on the improved DSVM method, the three candidate voltage vectors are calculated by using a novel algebraic way. This new strategy has the benefits of both the MPTC method and the DB method. The effectiveness of the proposed strategy is validated based on a test bench.

Deadbeat Model-Predictive Torque Control With Discrete Space-Vector Modulation for PMSM Drives

Finite set model predictive torque control (FCSMPTC) of induction machines has received widespread attention in recent years due to its fast dynamic response, intuitive concept, and ability to handle nonlinear constraints. However, FCSMPTC essentially belongs to the open-loop control paradigm, and unmatched parameters inevitably cause electromagnetic torque tracking error. In addition, the outer loop (i.e., the speed loop) based on a proportional-integral (PI) regulator cannot achieve optimal control between speed dynamic response and torque tracking error compensation. The traditional control paradigm is abbreviated as PI-MPTC. In order to solve the aforementioned problem, this paper proposes active disturbance-rejection-based model predictive torque control (ADR-MPTC). First, the influence mechanism of mismatched parameters on torque prediction error in PI-MPTC is studied, and then the performance of a traditional PI regulator used to compensate for torque prediction error is analyzed. Second, this paper introduces several parts of the proposed ADR-MPTC, including the design of the torque prediction error observer, nonlinear prediction error compensation strategies, an enhanced predictive torque control, and a simplified full-order flux observer. Finally, PI-MPTC and ADR-MPTC are studied experimentally. The experimental results show that compared with PI-MPTC, ADR-MPTC performs better in dynamic and steady states, and has stronger robustness.

Active Disturbance-Rejection-Based Speed Control in Model Predictive Control for Induction Machines

During transient process, it may take plenty of steps for PMSM to reach the reference torque and flux linkage. Multistep model predictive torque and flux control (MPTC) can select the best sequence of voltage vectors from the initial state to the reference state to achieve the fastest torque response. However, the computation burden is a significant challenge for real-time implementation. This paper proposes a novel fast-response MPTC strategy for PMSM drives. The reference torque is converted to flux linkage in d -axis and q -axis to eliminate the weighting factor in cost function. During the transient process, Pontryagin's maximum principle is used to design minimum-time flux linkage trajectories from the initial state to the reference state. In each control period, along the reference flux linkage trajectories, one-step MPTC is used to select the best voltage vector. The computational burden and dynamic performance of the proposed method are verified by experiments.

Fast Response Model Predictive Torque and Flux Control With Low Calculation Effort for PMSMs

Deadbeat Current Control (DCC) is an inverse-model based solution for Permanent Magnet Synchronous Motor (PMSM). The desired voltage vector is calculated by the current references. Thus, the command currents can be achieved by the end of each control period. DCC has the advantages of precise current tracking, constant switching frequency, non-overshoot, etc. However, if the desired current is physically infeasible to be obtained in one sampling period, it is a big challenge for DCC to select the best voltage vector to achieve the fastest current response. To deal with this problem, a novel technique is proposed in this paper. The best trajectory from the initial state to the reference is planned by using time-optimal algorithm. Along the best trajectory, the PMSM can reach the command currents in the minimum-time. The effectiveness of the proposed method is verified by simulation

Fast Response Deadbeat Current Control for PMSM

The invention discloses a dynamic testing platform for leg joints of a foot-type bionic robot. The dynamic testing platform is composed of a platform bracket, a sliding rail, simulation flat plates, aposition sensor, a balancing weight and tested robot joints, wherein the upper simulation flat plate simulates the upper part of a body and connected with a hip joint electric driving module, and thehip joint electric driving module can be detachably replaced with a rotating shaft, so that the motion characteristics of a single joint are tested; the lower simulation flat plate simulates the feetand makes contact with the ground, and an ankle joint electric driving module is connected with the lower simulation flat plate; and thigh bars of tested legs, a knee joint electric driving module and shank bars of the tested legs are connected between the upper simulation flat plate and the lower simulation flat plate. The demands of various motion modes such as squating, standing-up, walking, running and jumping of robots with different weights for the torque, the rotating speed and the power trajectory of the electric driving modules can be accurately analyzed, the overall motion state andthe load capability situation of the leg electric driving module of the robot can be tested before assembling, and a lot of the time and costs are saved.

Manfeng Dou

Papers

Deadbeat Model-Predictive Torque Control With Discrete Space-Vector Modulation for PMSM Drives

Active Disturbance-Rejection-Based Speed Control in Model Predictive Control for Induction Machines

Fast Response Model Predictive Torque and Flux Control With Low Calculation Effort for PMSMs

Fast Response Deadbeat Current Control for PMSM

Dynamic testing platform for leg joints of foot-type bionic robot