Existing model-free predictive control (MFPC) methods are primarily based on mature controller design formulas, with various observers added to these controllers. Currently, there is a lack of modifications to the mechanism of model-free predictive control itself. This letter starts with the original formulation of the ultra-local model and redesigns a model-free predictive control method from the fundamental formula level. First, a term based on an exponential-like function is proposed, modifying the proportion of the controller's error term, called exponential MFPC. Subsequently, a switching control concept is introduced into the controller design, facilitating better integration of the existing method with the new one, ensuring system performance across different error intervals. Ultimately, the effectiveness of the proposed switched ultra-local MFPC is demonstrated by the experimental result of the superiority in the loading aspect with smaller speed overshoot and speed oscillations.

Aiming to address the lack of effective parameter-tuning strategies for model-free predictive controllers, this letter proposes an online self-learning approach using a tiny neural network. First, an incremental model-free predictive control scheme is introduced that incorporates a tiny neural network module. By analyzing the physical relationship between motor voltage and current, the input and output values for neural network learning are determined. Subsequently, an online learning strategy is proposed, leveraging the ultra-local model principle from physics, model-free control theory principles, and the neural network structure itself. Experimental results demonstrate that the proposed method has a short computation time, fully supports real-time operation in the motor current loop, and can autonomously optimize controller parameters when the initial settings of the ultra-local model-free predictive control are not ideal.

Gradient-based flux observers are promising for encoderless control of surface-permanent magnet synchronous motors (SPMSMs) for their computational efficiency and adaptability, yet suffer from noise sensitivity and gain tuning. To this end, this article proposes a robust optimized flux observer combining the huber loss function (HLF) and root mean square propagation (RMSProp) algorithm to overcome these limitations. First, a robust nonlinear flux observer is developed with the HLF to enhance the steady state performance of the encoderless control by leveraging its smoothness and noise suppression capabilities. Second, the RMSProp algorithm is employed to optimize the gradient search process, dynamically adjusting the step size in real-time based on historical gradient information to ensure improved dynamic response. Additionally, the Lyapunov stability criterion is utilized to analyze the stability of the proposed controller and prove the bounds of its observation error. Finally, experimental results confirm that the proposed control strategy achieves high-precision position estimation. Angular estimation errors under no-load and load conditions are reduced to below −0.09 rad and −0.08 rad, respectively, compared to −0.22 rad and −0.15 rad with conventional controller, due to flexible gain tuning and strong noise robustness.

Smooth and robust speed regulation is important for permanent magnet synchronous motor (PMSM) drives. This article proposes a novel PMSM speed control design methodology that explicitly incorporates target performance specifications into the controller synthesis, achieving nonovershoot and oscillation-free speed response. Particularly, compared with the traditional prescribed performance control (PPC), the proposed method is based on a refined tube-type performance function, which achieves unilateral convergence and has a simple structure. It has faster convergence speed and nonovershoot characteristics. Furthermore, the idea of the monotonous tube performance function is extended to the case of abrupt reference variation, in which the original PPC may lead to singularity problems and controller crashes. Besides, the inherent static error and oscillation problem of the PPC method is alleviated with an integral term combined with an antiwindup scheme. The controller effectively improves the system’s robustness and ensures the same predefined nonovershoot and oscillation-free performance under different load and speed variations. Additionally, rigorous theoretical analysis is studied via the Lyapunov stability theory. Comparative experiments are conducted on a PMSM platform. Despite diverse load conditions and references, the proposed controller delivers a smooth, overshoot-free, and robust speed response.

Encoderless algorithms for surface-mounted permanent magnet synchronous motors addresses the installation limitations of encoders in harsh or confined environments. However, existing encoderless control methods suffer from challenges in dynamic response speed and parameter sensitivity issues. To solve these issues, a modified reaching law for nonlinear flux observer with improved response speed is proposed in this study. First, a logarithmic function-based reaching law is designed for the nonlinear angle observer to achieve faster response speed. The Lyapunov function is developed to prove the stability of this reaching law. Second, an accurate mathematical model of the motor is designed and a dataset is constructed based on the deviation between actual values and reference values. Adjustable weight factors are used in this model to fit factors, such as inductance saturation and unmodeled disturbances. In addition, the sparse Bayesian learning algorithm is employed to analyze the features of historical data within the dataset, which helps reduce the impact of parameter variations on angle observation accuracy. The experimental results indicate that the proposed algorithm recovers from speed reversal in only 53.2 ms, significantly faster compared to 177.2 ms in conventional methods.