Composite adaptive model predictive control for DC–DC boost converters

Li, Po; Li, Ruiyu; Tianying, Shao; Zhang, Jingrui; Fang, Zheng

doi:10.1049/iet-pel.2017.0835

Cited by 39 publications

(13 citation statements)

References 32 publications

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“…en, the mathematical model is discretized and the state space equation is obtained. Based on this, the speed controller of model predictive control and the current controller of beat-free predictive control are designed [19]. Predictive control is used to replace the traditional double-loop PI control.…”

Section: Introductionmentioning

confidence: 99%

A DSP-Controlled Permanent Magnet Synchronous Motor Control System for Hybrid Vehicles

2022

International Journal of Antennas and Propagation

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Compared with other motors, the permanent magnet synchronous motor (PMSM) is small and occupies less space. At the same time, its weight is relatively light, so it is more in line with the development trend of hybrid electric vehicle (EV) drive motor lightweight miniaturization and has been widely used. This article studies a DSP-controlled PMSM control system for hybrid vehicles. Firstly, the motor drive control system is mainly controlled by DSP2812 chip. Then, a maximum torque current ratio control method was proposed to optimize the energy efficiency of hybrid vehicles based on PMSM. The longitudinal dynamic model of the moving hybrid vehicle was obtained by force analysis. Combined with the basic equation of PMSM and the transmission system of the hybrid vehicle, the mathematical model of PMSM-EV was established. The experimental results show that the maximum torque current ratio control method applied to hybrid vehicles can effectively reduce the loss, improve the efficiency and dynamic performance, and solve the endurance problem of hybrid vehicles to a certain extent. This advantage is significant in the dynamic acceleration and deceleration of hybrid vehicles.

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Section: Introductionmentioning

confidence: 99%

A DSP-Controlled Permanent Magnet Synchronous Motor Control System for Hybrid Vehicles

2022

International Journal of Antennas and Propagation

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“…Due to the switching behaviors SW ON and OFF, two working modes can therefore be generated (the ON mode and OFF mode). By multiplying those two modes with the duty cycle d and (1 − d) respectively, and adding them together, the averaged model can be illustrated [14]. For simplicity, the dynamics of the switching behaviors will be ignored in this paper and the reader is referred to [13], [17] for more details.…”

Section: Dc-dc Buck-boost Dynamic System Designmentioning

confidence: 99%

“…And the matrices ℳ p and ℱ p are computed in (14) which are defined by the scheduling parameters state matrices S p . By incrementing the expression (17), the prediction of the scheduling parameters is estimated to keep the AMPC optimization of the proposed objective cost function, and the constraints (11) updated each time instant k over the considered prediction horizon Np.…”

Section: A Adaptive Model Predictive Control (Ampc) Designmentioning

confidence: 99%

Adaptive Model Predictive Control for DC-DC Power Converters With Parameters’ Uncertainties

Albira¹,

Zohdy

2021

IEEE Access

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This research investigates the Adaptive Model Predictive Controller (AMPC) and Linear Parameter-Varying (LPV) control system for a direct current (dc-dc) buck-boost converter, considering the parameters' uncertainty. The LPV model and the AMPC are explicitly constructed to perform a robust control design for the proposed dc-dc converter. The LPV model was created out of a set of linearized systems at different operating conditions to perform Linear Time-Invariant (LTI) models. Due to the dc-dc converter's nonlinear characteristic, the performed LTI models might have declination, which the AMPC can perfectly address by adapting the prediction model for the changes in the operating conditions. The proposed AMPC control system was implemented in a simulation environment as well as in a real-time environment on an Arduino Mega 2560 microcontroller to test its robustness and quality. The proposed AMPC control system works well compared with some existing control system algorithms at different prediction horizons. Also, the comparison considers the designed Gain Scheduling Proportional Integral (G.S-PI) and the regular Model Predictive (reg-MPC) Controllers were implemented without using the LPV model to test their performance against the proposed converter's parameters uncertainties.INDEX TERMS dc-dc buck-boost converter, MPC controller, AMPC controller, LPV model, uncertainty modeling, Quadratic Programming (QP), and Optimization. MOHAMED E. ALBIRA. (Student Member, IEEE) received the H.D degree in electrical and electronics engineering technology from Collage of Technical and Science, Misurata, Libya, in 2002, the M.S.

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“…These controllers cannot ensure robustness in a wide range of operating points, since they work on linearized models [7][8][9]. Hence, nonlinear controllers have been introduced to deal with the robustness, as the linear controllers are found to be inaccurate in the presence of disturbances to the system [10].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of PI and DMRAC Algorithm in Buck–Boost Converter for Voltage Tracking in Electric Vehicle Using Simulation

et al. 2021

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This study introduces a Direct Model Reference Adaptive Control (DMRAC) algorithm in a buck–boost converter in the power distribution of an electric vehicle. In this study, DMRAC was used in order to overcome the system nonlinearity due to load demand variation, in case of different driving modes (such as acceleration, stable and regenerative braking system mode), and the presence of disturbances in the system. DMRAC receives popularity because of its robustness in the presence of nonlinearity and ensuring system stability. To evaluate the efficacy of DMRAC in the current system, its performance was compared with a PI controller in the MATLAB/Simulink environment. The simulation results show the superiority of DMRAC over a conventional PI control approach, in both variable load demand and disturbed system cases that were measured by tracking error. The improvement was seen in the DMRAC response, with smaller tracking error and faster transient and disturbance rejection. The main contribution of this work is in introducing DMRAC, particularly in a buck–boost converter, and its efficacy with a DC–DC converter for an electric vehicle, which has not been studied before.

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Composite adaptive model predictive control for DC–DC boost converters

Cited by 39 publications

References 32 publications

A DSP-Controlled Permanent Magnet Synchronous Motor Control System for Hybrid Vehicles

A DSP-Controlled Permanent Magnet Synchronous Motor Control System for Hybrid Vehicles

Adaptive Model Predictive Control for DC-DC Power Converters With Parameters’ Uncertainties

Performance Analysis of PI and DMRAC Algorithm in Buck–Boost Converter for Voltage Tracking in Electric Vehicle Using Simulation

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