Linear Parameter Varying Observer-Based Adaptive Dynamic Surface Sliding Mode Control for PMSM

This article introduces an adaptive fault-tolerant control method for non-repetitive trajectory tracking of linear motor-driven gantry platforms under state constraints. It provides a comprehensive solution to real-world issues involving state constraints and actuator failures in gantry platforms, alleviating the challenges associated with precise modeling. Through the integration of iterative learning and backstepping cooperative design, this method achieves system stability without requiring a priori knowledge of system dynamic models or parameters. Leveraging a barrier composite energy function, the proposed controller can effectively regulate the stability of the controlled system, even when operating under state constraints. Instability issues caused by actuator failures are properly addressed, thereby enhancing controller robustness. The design of a trajectory correction function further extends applicability. Experimental validation on a linear motor-driven gantry platform serves as empirical evidence of the effectiveness of the proposed method.

Section: Introductionmentioning

confidence: 99%

Adaptive Iterative Learning Constrained Control for Linear Motor-Driven Gantry Stage with Fault-Tolerant Non-Repetitive Trajectory Tracking

2024

“…The design concept of SMC aims to ensure optimal performance and stability of the system, even in the presence of uncertainties such as external disturbances and inaccuracies in modeling. Due to these advantages of SMC, it has been an extensive application in diverse and intricate systems [11][12][13][14]. In recent years, the SMC technology has seen a wide range of application and has made some key progress.…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Control of a Class of Hybrid-Switched Systems with Disturbances

Li,

Wang,

Zhang

2024

This paper investigates the problem of sliding mode control for a class of hybrid switched systems with matching disturbances. Firstly, a sliding mode surface is designed, and the corresponding sliding mode equation for the switched system is derived. Then, we analyzed the stability of the sliding mode equation using the Lyapunov function and average dwell time. Moreover, a sliding mode control law is designed with the approach law method to drive the system state to a bounded sliding mode region and then maintain it there subsequently. Finally, an illustrative example is given to demonstrate the efficiency of the approach.

On Neural Observer in Dynamic Sliding Mode Control of Permanent Magnet Synchronous Wind Generator

Karami-Mollaee,

Barambones

2024

The captured energy of a wind turbine (WT) can be converted into electricity by a generator. Therefore, to improve the efficiency of this system, both the structures of WTs and generators should be considered for control. But the present challenge is WT uncertainty, while the input signals to the generator should be smooth. In this paper, a permanent magnet synchronous generator (PMSG) is considered. The dynamics of the PMSG can be described using two axes, named d-q reference frameworks, with an input in each framework direction. To obtain the maximum power and to overcome the uncertainty by means of a smooth signal, the dynamic sliding mode controller (D-SMC) is implemented. In the D-SMC, an integrator is placed in the control scheme in order to suppress the chattering, because it acts like a low-pass filter. To estimate the state added by the integrator, a new observer-based neural network (ONN) is proposed. The proof of the stability of the D-SMC and ONN is based on Lyapunov theory. To prove the advantages of the D-SMC, a comparison was also carried out by traditional sliding mode control (T-SMC) with a similar ONN. From this comparison, we know that the advantages of the D-SMC are clear in terms of real implementation, concept, and chattering suppression.