A Simplified Deadbeat Based Predictive Torque Control for Three-Level Simplified Neutral Point Clamped Inverter Fed IPMSM Drives Using SVM

Zhang, Xinan; Foo, Gilbert; Jiao, Tifeng; Ngo, Tung; Lee, Christopher

doi:10.1109/tec.2019.2933465

Cited by 28 publications

(14 citation statements)

References 24 publications

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“…Deadbeat control is one of the most well-known predictive control methods in the literature [25][26][27][28]. A typical control scheme for this method is shown in Fig.…”

Section: A Deadbeat Controller Designmentioning

confidence: 99%

A Model-Free Predictive Current Controller for Voltage Source Inverters

Kermadi¹

2022

Preprint

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This work proposes a model-free deadbeat predictive current controller using the input and output data of the controlled system. The proposed controller can be used for electric drives or grid-forming inverters. The proposed control scheme does not require knowledge of the system parameters for its correct operation; thus, all load parameter mismatch issues in conventional model-based controllers are avoided. This is performed using an auto-regressive with exogenous input (ARX) model to represent the physical system with a recursive least square (RLS) estimation algorithm for the ARX model parameters identification. Using the identified system parameters, an observable canonical state-space model is constructed. Based on this system, the inverter voltage references using the space vector modulation (SVM) are calculated using the deadbeat control principle. The inherent sensibility of the deadbeat controller to parameters mismatch is avoided. Experimental results confirm the validity of the proposed Model-Free controller in terms of tracking accuracy and waveform quality.

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“…Deadbeat control is one of the most well-known predictive control methods in the literature [25][26][27][28]. A typical control scheme for this method is shown in Fig.…”

Section: A Deadbeat Controller Designmentioning

confidence: 99%

A Model-Free Predictive Current Controller for Voltage Source Inverters

Kermadi¹

2022

Preprint

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“…Based on a steady-state error and total harmonic distortion (THD) criteria, the system behavior without the weighting coefficients is evaluated to fulfill the control requirements. Finally, by evaluating the system behavior, the simulation started with Λ st � 0 and increases progressively 1) and (2) as follows [43]:…”

Section: Weighting Coefficient Designmentioning

confidence: 99%

Novel Predictive Control for the IPMSM Fed by the 3L-SNPC Inverter for EVAs: Modified Lyapunov Function, Computational Efficiency, and Delay Compensation

Katkout

Nasser

Essadki

2020

Mathematical Problems in Engineering

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This paper proposes a novel predictive strategy based on a model predictive control (MPC) for the interior permanent magnet synchronous motors (IPMSMs) driven by a three-level simplified neutral-point clamped inverter (3L-SNPC) for electric vehicle applications (EVAs). Based on the prediction of the future behavior of the controlled variables, a predefined multiobjective cost function incorporates the control objectives which are evaluated for every sampling period to generate the optimal switching state applied directly to the inverter without the modulation stage. The control objectives in this paper are tracking current capacity, neutral-point voltage balancing, common-mode voltage control, and switching frequency reduction. The principal concepts of the novel scheme are summarized as follows: first, the delay compensation based on the long horizon of prediction is adopted by a multilevel power converter structure. Second, based on the modified Lyapunov candidate function, both stability and recursive feasibility are ensured of the proposed predictive scheme. Third, the practicability of the real-time implementation is improved by the proposed “static voltage vector” (SVV) and “single state variation” (SSV) principles. Finally, the proposed concepts are implemented in the novel predictive control formulation as additional constraints without compromising the complexity and the good performances of the predictive controller. Therefore, only the switching states that guarantee the stability and the reduction of calculation burden criteria are considered in the evaluation of cost function. The proposed predictive scheme based on the “SVV” principle has demonstrated superior performance in simulation compared with the proposed scheme with the “SSV” principle. The computational burden and switching frequency rates are reduced by 35% and 56.22%, respectively.

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“…A reduction of calculation complication can be achieved because there is no use of optimal iteration. The presented inverter unfolds zero following error and expeditious transiency [44]. The closed-loop design of the repetitive method is proposed for power inverters, offering the removal of following error and duration disturbance.…”

Section: Suggested Controllermentioning

confidence: 99%

A Novel Fixed-Time-Convergent Sliding Mode Technology Using Improved Quantum Particle Swarm Optimization for Renewable Energy Inverters

Chang¹

2020

Sustainability

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This paper describes a robust feedback technique involving novel fixed-time-convergent sliding mode technology (NFTCSMT) using improved quantum particle swarm optimization (QPSO) to obtain high-performance renewable energy inverters. Customary SMT encounters long time convergence towards the origin and the influence of the dithering. The NFTCSMT can rapidly impel system-following movement to approach the sliding manifold and effectively accelerate the convergence speed to equilibrium states. However, the NFTCSMT cannot easily select the global optimum of the controller parameters subject to large parameter changes and nonlinear interventions, leading to the dither phenomenon/steady-state error still being caused. The dither inflicts decreased control accuracy, high voltage harmonics, major harm in relation to switching components, and great thermal losses in power electronic converters. The improved QPSO including the unique property of a random compression/expansion factor is used to find optimal parameters of the NFTCSMT in practical applications, for the reason that it importantly mitigates the dither and amends convergent speed as well as guaranteeing global convergence. The presented alliance amid NFTCSMT and improved QPSO achieves faster response time and singularityless, and also yields high-accuracy tracking and dither attenuation. The robust stability using Lyapunov theorem of the suggested system has provided precise mathematical derivations. Simulations show that the suggested controller offers less than 0.1% voltage THD (total harmonic distortion) which exceeds IEEE standard 519 under heavily distorted rectifier loads, and less than 10% voltage dip which surpasses IEEE standard 1159 during step load transients. Experimental tests of an algorithmically controlled laboratory prototype (1 kW, 110 Vrms/60 Hz) of a renewable energy inverter (REI) based on digital signal processing manifest less than 0.05% voltage THD in the face of great inductor-capacitor alterations, and less than 10% voltage dip in the face of transient load scenarios.

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A Simplified Deadbeat Based Predictive Torque Control for Three-Level Simplified Neutral Point Clamped Inverter Fed IPMSM Drives Using SVM

Cited by 28 publications

References 24 publications

A Model-Free Predictive Current Controller for Voltage Source Inverters

A Model-Free Predictive Current Controller for Voltage Source Inverters

Novel Predictive Control for the IPMSM Fed by the 3L-SNPC Inverter for EVAs: Modified Lyapunov Function, Computational Efficiency, and Delay Compensation

A Novel Fixed-Time-Convergent Sliding Mode Technology Using Improved Quantum Particle Swarm Optimization for Renewable Energy Inverters

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