Predictive Error Model-Based Enhanced Observer for PMSM Deadbeat Control Systems

This article proposes an improved model-free deadbeat predictive current control (MFCC) method for permanent magnet synchronous motors (PMSMs) based on the ultralocal model and H∞ norm. Firstly, the traditional deadbeat predictive current control (DPCC) method is introduced and a theoretical analysis is conducted on its sensitivity to parameters. Building upon this, the limitations of model dependence and the limited robustness of the deadbeat predictive current control method based on the extended state observer (ESO-DPCC) are theoretically analyzed. Furthermore, an improved MFCC method based on the ultralocal model is proposed, and the influence of the observer on MFCC is theoretically analyzed. This study combined the proposed method with the H∞ norm, and the optimal coefficients of the observer were tuned to enhance the robustness and dynamic performance of the current loop. Finally, the proposed algorithms were validated on a 400 W PMSM platform.

To address the issue of speed synchronization deterioration in dual permanent magnet synchronous motor (PMSM) systems under uncertain disturbances, this study proposes a power-law-enhanced nonsingular fast terminal sliding mode (NFTSM) control strategy and introduces an innovative reaching law. This synchronization architecture embeds the enhanced sliding mode algorithm within a cross-coupled control framework, where the redesigned reaching law is specifically tailored to optimize steady-state operating characteristics. For individual motor regulation, an improved deadbeat predictive current control (DPCC) method incorporating a super-twisting sliding mode observer (STSMO) resolves inherent one-step delay limitations and parameter model discrepancies. This observer architecture converts discontinuous control variables into higher-order sliding surface derivatives, significantly mitigating the characteristic chattering phenomenon prevalent in classical first-order sliding mode implementations. The STSMO also adopts a predictive current substitution method to eliminate time-delay effects. Comprehensive simulation analysis shows that compared to traditional PI control, there are significant improvements in three operational aspects: system anti-interference capability, speed trajectory tracking accuracy, and inter-motor synchronization consistency.

Model predictive current control has high control accuracy and good control performance, and has been widely applied in the control strategy of dual three-phase permanent magnet synchronous motors. This article proposes an improved deadbeat model predictive current control (DMPCC) method by analyzing the mathematical model of a dual three-phase open-winding permanent magnet synchronous motor. One inverter predicts and calculates the optimal voltage vector through the deadbeat model, while the other inverter calculates the optimal voltage vector through the value function rolling optimization. This method reduces the computational workload of the controller, avoids the delay problem of the traditional model predictive current control (MPCC) method, improves the system's response speed, effectively suppresses the current in the harmonic sub-plane, and reduces torque ripple. The simulation results have verified the accuracy and speed of the above control method.

For multiobjective model predictive current control (MPCC) method with penalties, the penalty weights are introduced into the cost function to determine the relative importance of control targets. However, the dimensions and value ranges of each control target are different. Thus, the penalty coefficient is difficult to determine. To avoid the difficulty of dimensionless parameter design, this article proposes a multiobjective control method called MPCC with multiple current bounds. In this article, the switching frequency and the common-mode voltage (CMV) are selected as the control target. Their control is converted into the limitation of the current ripple so that the control parameters have a clear physical meaning. On the one hand, the proposed method can realize the cooperative control of reference current tracking, switching frequency suppression, and CMV suppression. On the other hand, the key control parameters have the same dimension and strong correlation with others. Therefore, appropriate parameter design can be realized. Experimental investigations for permanent magnet synchronous motor prove the effectiveness of the control method.

Switching losses minimization and performance improvement of PCC and PTC methods of model predictive direct torque control drives with 15-level inverter

To address the complexity of vector selection in the conventional predictive current control strategy and the reduction of control system robustness when the motor parameters change, this paper proposes a finite-set model-free three-vector predictive current control method for permanent magnet synchronous motor based on the improved sliding-mode perturbation observer. According to the mathematical model of a permanent magnet synchronous motor under perturbation, a new hyperlocal model is established, and the conventional vector traversal optimization method is optimized to obtain the necessary basic voltage vector by analyzing the function with one judgment. Meanwhile, the sliding mode perturbation observer is designed using the fast exponential convergence law to observe the unknown part. Finally, by experimentally contrasting the proposed technique with the conventional three-vector model predictive current control, the method suggested in this paper can successfully reduce the current pulsation and inhibit the system perturbation caused by the parameter change, ensuring the steady state performance of the motor, as demonstrated by the experimental comparison with the conventional three-vector model predictive current control method.

Composite control based on FNTSMC and adaptive neural network for PMSM system

This article analyzes the conventional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -frame model-based deadbeat predictive current control (DBPCC) methods for high-speed permanent magnet synchronous machine (PMSM) drives with low switching-to-fundamental frequency ratios (SFRs). It shows that the state-of-the-art compensation schemes of control delay and rotor movement effect can improve the control performance but the problem still arises at very high speeds with very low SFRs. Therefore, this article presents a novel DBPCC method for high-speed PMSM drives. The proposed method tracks the machine stator flux vector in the stationary frame to achieve deadbeat control of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis currents. The control delay and rotor movement effect are both precisely considered. Consequently, the control performance and stability of the proposed DBPCC can be guaranteed at high speeds. Extensive simulations and experiments have been performed on a prototype high-speed PMSM drive. The effectiveness of the proposed method and its superiorities against the field-oriented control and the conventional DBPCCs have all been demonstrated.

During the past few years, finite control set predictive current control (PCC) method has attracted more and more attention in research and industry applications. However, PCC method could be improved by considering two points. First, the current reference used in the cost function of PCC control scheme is usually produced by a proportional and integration speed controller. Confront with load torque and time-varying system parameters, it is a better solution to design a disturbance estimation based feed-forward compensated controller. In this way, the current reference could be generated faster and more accurate. Second, the PCC method is model-based method which means the accuracy of the model parameters are essential. In real system, time-varying parameters existed almost always. This paper investigates a generalized proportional integral observer based PCC approach for dealing with load torque disturbance, time-varying parameter uncertainties. The effectiveness of the proposed method has also been confirmed by simulation and a lab-constructed experimental prototype.

In this article, a deadbeat predictive current control (DPCC) method based on gain factor self-tuning technology is proposed, which can effectively improve the dynamic response performance and robustness of the control system. Since the performance of DPCC is highly dependent on parameter accuracy, this article establishes a closed-loop transfer function model of the DPCC method and analyzes in detail the impact of parameter mismatch on the current dynamic response performance. Then, the current error caused by parameter mismatch is suppressed by designing the gain factor, on this basis, a parameter self-tuning algorithm based on gradient descent optimization method is constructed. Compared with traditional parameter identification algorithms, the proposed method has the advantages of strong versatility and greatly reduces computational burden. Finally, the accuracy of the tuning results of the proposed method is proved by experimental results, which effectively suppresses the influence of parameter mismatch and improves the current response performance.

Deadbeat predictive current control (DPCC) has become a research hotspot for permanent magnet synchronous motor (PMSM) drive systems due to its advantages of short dynamic response time and superior current tracking performance. However, the speed controller in traditional deadbeat current control usually consists of a linear proportional-integral (PI) controller, which cannot quickly limit uncertain disturbances from internal or external sources. Moreover, DPCC is more sensitive to model parameters. In order to reduce the parameter sensitivity of the DPCC method and improve the fast-limiting disturbance capability of the speed controller, a sliding-mode-based deadbeat predictive control method is presented in this article. First, the traditional DPCC is introduced; then, a variable-speed sliding-mode reaching law is designed, and a sliding-mode speed controller is developed using this law to improve the antidisturbance capability of the speed controller. Moreover, in order to suppress the negative impact of the parameter mismatches on the control performance, the parameter mismatches are considered when building the system model, and a sliding-mode-based deadbeat current controller based on the same sliding-mode reaching law as the speed controller is presented. Finally, the experimental results show that the proposed method has a better control performance.

Multi-vector model predictive control of permanent magnet synchronous motor can effectively overcome the shortcomings of conventional single-vector model predictive control such as large motor current ripples and limited control accuracy, but its control performance is more susceptible to the influence of motor parameter perturbation. To improve the robustness of the multi-vector predictive control algorithm under the motor parameter perturbation, a novel nonparametric three-vector model predictive current control method for permanent magnet synchronous motors is proposed in this paper. Through constructing an ultra-local predictive current model based on the input and output signals of the permanent magnet synchronous motor, the influence of motor parameter perturbation on current prediction is avoided, and a disturbance observer is designed to estimate the non-modeled and perturbed parts of the ultra-local model. In addition, the direct calculation method of vector duty cycles based on current error is introduced to suppress the influence of motor parameter uncertainty on the duty cycle calculation link, which further improves the system robustness. Finally, simulations and experiments of the proposed method are demonstrated to compare with the conventional parametric three-vector model predictive current control, and the results show that the proposed control strategy can effectively suppress the disturbances caused by the motor parameters and ensure the steady-state performance during motor operation.

A Tent-map chaotic Newton-Raphson optimization based neural network predictive control (TCNR-NPC) is developed to apply to the long-delay permanent magnet synchronous motor (PMSM) system in this paper. Due to a nonlinear model utilized in the predictive controller, nonlinear optimization methods turn into an important issue. To overcome the shortcoming of the conventional nonlinear programming on the initial condition sensitivity and maintain the accuracy of optimal solution, chaos optimization algorithm (COA) and Newton-Raphson (NR) are combined. With the comparison of COA and NR based optimization methods, our approach, the Tent-map chaotic Newton-Raphson (TCNR) optimization, is easier to reach the global optimum, thus, it would be employed in neural network predictive control. It is found that TCNR-NPC has a better performance than those of GPC, modified GPC, adaptive extended PSO based NPC, and PSO based PI controllers in real experiments.

To improve the dynamic and steady-state control performance of permanent magnet synchronous motors under the three-vector model predictive current control method, this study proposes a switching current predictive control method based on the exponential moving average algorithm, which evaluates the magnitude of the change of the q-axis current slope in real time to discriminate the motor’s operating conditions and selects the optimal control method for different operating conditions. Meanwhile, the traditional three-vector model predictive current control method is improved by introducing a comparison mechanism for the q-axis current slope to select the second effective voltage vector, avoiding the secondary optimization calculation of the value function and reducing the computational complexity of the traditional method. By comparing the proposed method with the traditional three-vector model predictive current control method, the experimental results prove that the proposed method improves the system’s dynamic response and steady-state performance.

Deadbeat Predictive Current Control (DPCC) for Permanent Magnet Synchronous Motors (PMSM) offers advantages such as fast dynamic response and excellent stability, but it is highly sensitive to motor parameters-parameter errors can easily exacerbate current tracking errors. To address this issue, an online Particle Swarm Optimization (PSO)-based parameter identification method is proposed. First, a variable inertia weight strategy is introduced into the PSO algorithm to enhance its global search capability. Second, a particle self-check mechanism is proposed: by comprehensively evaluating the quality of particle positions, the probability of the algorithm falling into local optima is reduced. Finally, the proposed algorithm is invoked online for parameter identification, and the identification results are updated to the controller to mitigate the impact of parameter mismatch. Simulation results demonstrate that the online PSO algorithm can effectively identify motor inductance and flux linkage parameters, significantly improving control robustness. Moreover, its identification accuracy is notably superior to that of the traditional PSO algorithm and the Recursive Least Squares (RLS) algorithm.