Sep 2026· 2026 IEEE 7th International Conference on Advanced Electrical and Energy Systems (AEES)· pp. 399-403· 0 citations· 8 references
Abstract
To address the degradation of speed-loop control performance caused by external disturbances and parameter uncertainties, this paper proposes a learning-enhanced predictive PI auto-tuning method for PMSM drives. A discrete-time speed-loop model and a finite-horizon cost function are constructed to balance tracking accuracy and control effort. The PI gains are updated online using a numerical-gradient-descent method combined with projection constraints, thereby ensuring bounded parameter adaptation. Furthermore, Lyapunov-based stability analysis demonstrates that, under bounded disturbances and finite-difference approximation errors, and with a sufficiently small learning rate, all closed-loop signals remain uniformly ultimately bounded. Simulation results show that, compared with conventional fixed-gain PI control, the proposed method achieves faster tracking response, stronger disturbance rejection, and improved robustness against inertia mismatch. The present validation is simulation-based; hardware implementation and comparison with additional adaptive baselines remain subjects of future work.
Servo control systems are frequently subjected to various uncertainties during operation, including load torque fluctuations, internal parameter variations, etc. These factors inevitably degrade system performance, necessitating the implementation of advanced control strategies to ensure stable operation. To address th...
Zhe-Xi Xu, Shi-Tao Song, Jun Gu et al.· 2026 IEEE 7th International...· 0 citations
To fulfill the dynamic and steady-state performance requirements of permanent magnet synchronous motors (PMSMs) in speed regulation, a practical prescribed-time fuzzy adaptive control approach is investigated. First, a novel continuously differentiable time-varying scaling function is constructed, which avoids the prob...
Tong Li, Jia-Peng Liu, Xin-Kai Chen et al.· IEEE Transactions on Automat...· 0 citations
The article presents a nonlinear adaptive control strategy for a class of feedback-linearizable systems subject to process faults and disturbance. The controller is derived through Lyapunov analysis and incorporates an adaptive proportional gain within a PD-like structure, preserving simplicity while providing nonlinea...
Alessio Dallabona, Vicenç Puig, Mogens Blanke et al.· 0 citations
In this paper, a computationally efficient robust adaptive Lyapunov-based model predictive control (RALMPC) framework is proposed for a class of constrained nonlinear systems subject to uncertainties and external disturbances. A radial basis function neural network (RBFNN), introduced to approximate non-parametric unce...
This paper investigates bilateral teleoperation under time-varying communication delays and dynamic uncertainties. A role-specific asymmetric master–slave control architecture is proposed, in which impedance control is assigned to the master side for interaction-dynamics shaping and force feedback, while adaptive slidi...
Thi-Ly Tong, M. Hồ, Q. Tran et al.· Automation· 0 citations
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