Junhao Cao

Bio: Junhao Cao is an academic researcher from Jiangsu University. The author has contributed to research in topics: Flux linkage & Rotor (electric). The author has an hindex of 3, co-authored 5 publications receiving 82 citations.

A Robust Deadbeat Predictive Controller With Delay Compensation Based on Composite Sliding-Mode Observer for PMSMs

Abstract: This article proposes an improved deadbeat predictive controller for permanent-magnet synchronous motor drive systems. It can eliminate the influence of the parameter mismatch of inductance, resistance, and flux linkage. First, the performance of the conventional predictive current method is investigated to analyze sensitivities of the electric parameters. Then, a composite sliding-mode disturbance observer (SMDO) based on the stator current and lumped disturbance is proposed, which can simultaneously estimate the future current value and lumped disturbance caused by the parameter mismatch of inductance, resistance, and flux linkage. Based on the discrete-time SMDO, currents are estimated and used to replace the sampled values to compensate one-step delay caused by the calculation and sampling delay. Both simulation and experimental performances of the proposed method have been validated and compared with the conventional control methods under different conditions. The comparison results show the superiority of the proposed predictive current control method based on the composite SMDO.

Speed Sensorless Control for Permanent Magnet Synchronous Motors Based on Finite Position Set

Abstract: This article presents a novel method for the sensorless control of interior permanent-magnet synchronous motors. An iterative search strategy based on dichotomy is proposed to provide a finite number of rotor position angles with good accuracy. These position angles are used to calculate the back electromotive force (EMF) in d -axis. The optimal rotor position angle is the one that yields a back EMF minimizing the defined cost function. With the increase of the iterations, the accuracy of rotor position angle increases geometrically. To effectively extract the back EMF signal under the low-speed condition, the high-frequency signal injection method is used to realize the low-speed operation of the motor. A hybrid control strategy is adopted to achieve the smooth switching from the low-speed to high-speed. The performance of the proposed method has been validated experimentally and compared with that of the conventional phase locked loop under different conditions.

A Composite Sliding Mode Control for SPMSM Drives Based on a New Hybrid Reaching Law With Disturbance Compensation

Abstract: This article presents a composite sliding mode control (CSMC) method for speed control of surface-mounted permanent magnet synchronous motors (SPMSMs). The proposed CSMC consists of a new sliding mode control (SMC) based on a novel hybrid reaching law (HRL) and an extended sliding mode disturbance observer (ESMDO). The new HRL is composed of two parts: a terminal reaching part and an exponential plus proportional reaching part. It can effectively suppress the chattering and reduce the reaching time compared with the conventional constant plus proportional rate reaching law (CPRL). The ESMDO is designed based on CPRL. It can estimate the extra chattering produced by the drive system’s lumped disturbance and compensate for the controller’s output. Based on the proposed new SMC and ESMDO, an antidisturbance sliding mode speed controller is designed to improve the performance of SPMSM drive systems. The performance of the proposed method has been validated experimentally and compared with the CPRL-based SMC methods under different conditions.

Speed Sensorless Control for IPMSMs Using a Modified MRAS with Grey Wolf Optimization Algorithm

Abstract: This paper represents modified model reference adaptive system (MRAS) method for speed sensorless control of interior permanent magnet synchronous motor (IPMSM), which can estimate rotor speed and position information. In order to suppress the adverse effect of parameter variation on control performance, the stator resistance and the permanent magnet flux linkage are estimated and continuously updated in reference and adjustable model. Then, proportional-integral (PI) controller parameters of speed adaptive law obtained by model reference adaptive system are optimized by grey wolf optimization (GWO) algorithm. In order to get the smallest possible speed following error and reference current error, the objective function is designed with discretized rotor speed error and current error as variables. The simulation results show the better performance of rotor speed estimation is obtained with GWO algorithm.

Optimal control strategy of state feedback control for surface-mounted PMSM drives based on auto-tuning of seeker optimization algorithm

Abstract: This paper focus on a state feedback controller (SFC)-based optimal control scheme for surface-mounted permanent-magnet synchronous motor (SPMSM) with auto-tuning of controller built on seeker optimization algorithm (SOA). First, based on the nonlinear state-space model of SPMSM, voltage feedforward compensation is used to design a linear SFC. Then in order to guarantee the steady performance in speed and current, integral models considering the errors of rotor speed and current response in d-axis are added in the state space model of SPMSM. Furthermore, by statically decoupling the load torque in the state equation, feedforward compensation is implemented on the load torque to improve the dynamic performance of the controller. The load torque is estimated by using disturbance observer with reasonable parameter selection. Then, with the consideration of the search capacity of seeker optimization algorithm (SOA), it is adopted to acquire matrix coefficient of the presented controller. Furthermore, in order to suppress the speed overshoot, a penalty term is introduced to the fitness index. The performance of the proposed method has been validated experimentally and compared with the conventional method under different conditions.

An Improved Model Predictive Current Control for PMSM Drives Based on Current Track Circle

Abstract: Model predictive current control (MPCC) is a high-performance control strategy for permanent-magnet synchronous motor (PMSM) drives, with the features of quick response and simple computation. However, the conventional MPCC results in high torque and current ripples. This article proposes an improved MPCC scheme for PMSM drives. In the proposed scheme, the back electromotive force is estimated from the previous stator voltage and current, and it is used to predict the stator current for the next period. To further improve the steady state and dynamic performance, the proposed MPCC selects the optimal voltage vector based on a current track circle instead of a cost function. Compared with the calculation of cost function, the prediction of the current track circle is simple and quick. The proposed MPCC is compared with conventional MPCC and a duty-circle based MPCC by simulation and experiment in the aspect of converter output voltage and sensitivity analysis. Results prove the superiority of the proposed MPCC and its effectiveness in reducing the torque and current ripples of PMSM drives.

Driving-Cycle-Oriented Design Optimization of a Permanent Magnet Hub Motor Drive System for a Four-Wheel-Drive Electric Vehicle

Abstract: The electrical drive system is crucial to the drive performance and safety of electric vehicles (EVs). In contrast to the traditional two-wheel-driven EVs, the hub motor four-wheel-drive system can steer the vehicle by controlling the torque and speed of each wheel independently, yielding a very simple distributed drivetrain with high efficiency and reliability. This article presents a system-level design optimization method for a permanent magnet hub motor drive system for a campus patrol EV based on a practical driving cycle. An outer rotor permanent-magnet synchronous hub motor (PMSHM) and an improved model predicate current control are proposed for the drive system. Due to the lack of reducers, the direct-drive PMSHM needs to face more complex working conditions and design constraints. In the implementation, the motor design requirements are obtained through the collection of practical EV driving cycles on the campus. Based on these requirements, two models are proposed as the preliminary designs for the PMSHM. To improve their performance, an efficient multiobjective optimization method is employed to the motor considering different operational conditions. The finite-element model and thermal network model are employed to verify the performance of the optimized PMSHM. An optimal design scheme is selected by comparing the comprehensive performance of the two optimized motors. In addition, a duty-cycle model predictive current control is adopted to drive the motor. Finally, a prototype is developed and tested, and the experimental results are presented.

A Robust Deadbeat Predictive Controller With Delay Compensation Based on Composite Sliding-Mode Observer for PMSMs

Abstract: This article proposes an improved deadbeat predictive controller for permanent-magnet synchronous motor drive systems. It can eliminate the influence of the parameter mismatch of inductance, resistance, and flux linkage. First, the performance of the conventional predictive current method is investigated to analyze sensitivities of the electric parameters. Then, a composite sliding-mode disturbance observer (SMDO) based on the stator current and lumped disturbance is proposed, which can simultaneously estimate the future current value and lumped disturbance caused by the parameter mismatch of inductance, resistance, and flux linkage. Based on the discrete-time SMDO, currents are estimated and used to replace the sampled values to compensate one-step delay caused by the calculation and sampling delay. Both simulation and experimental performances of the proposed method have been validated and compared with the conventional control methods under different conditions. The comparison results show the superiority of the proposed predictive current control method based on the composite SMDO.

Torque Ripple Reduction of SRM Drive Using Improved Direct Torque Control With Sliding Mode Controller and Observer

Abstract: The industrial application of the switched reluctance motor (SRM) is limited by its high torque ripples caused by the doubly salient structure. In this article, an improved direct torque control (DTC) with sliding mode controller and observer is developed to reduce the torque ripples of a four-phase SRM. First, a sliding mode controller based on a new reaching law is developed for designing a sliding mode speed controller (SMSC) for the DTC system. An antidisturbance sliding mode observer (ADSMO) is then proposed and combined with the SMSC to build a composite antidisturbance speed control strategy. Moreover, detailed simulation validations are carried out to reveal the effectiveness of the new reaching law, SMSC and ADSMO. Finally, experiments are conducted to verify the performance of the proposed SMSC–ADSMO in a DTC system with a four-phase SRM prototype.

An ANFIS-Based ECMS for Energy Optimization of Parallel Hybrid Electric Bus

Abstract: The fuel economy of hybrid electric vehicles is very closely associated with the energy management strategy (EMS). In this paper, a practicality-oriented adaptive EMS for a parallel hybrid electric bus is presented, which combines the adaptive neuro-fuzzy inference system (ANFIS) and equivalent consumption minimization strategy (ECMS). Considering the regular and fixed route of the city bus, the optimal control trajectories can be attained by the dynamic programming in advance. Using the rolling optimization method, a group of optimal equivalent factors is extracted from aforementioned control trajectories and used as the training samples. Then, a trained ANFIS model that produces the optimal equivalent factor online is constructed, showcasing striking superiority in self-learning and inference. By applying the derived equivalent factor in the framework of the ECMS, an adaptive energy management controller is available to achieve desirable power distribution online. Finally, the simulation and hardware in the loop (HIL) tests are used to validate the effectiveness and feasibility of the controller. The results demonstrate that, compared with other strategies, the fuel economy with the proposed strategy can be effectively improved.