Evaluation of Interval Type-2 Fuzzy Neural Super-Twisting Control Applied to Single-Phase Active Power Filters  
[PERSON]  
1 College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, China 12 College of Artificial Intelligence and Automation, Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213200, China 2  
[PERSON]  
1 College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, China 1  
[PERSON]  
1 College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, China 12 College of Artificial Intelligence and Automation, Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213200, China 2  
Footnote 1: email: {jachengwang2013200, jchengwei20  
and robustness, and effectively reduce chattering issues [8; 9; 10]. However, fractional-order sliding mode control involves fractional-order calculus, leading to increased complexity in mathematical models and algorithms, which in turn increases the difficulty of computation and implementation. Additionally, parameter tuning for fractional-order sliding mode control is relatively challenging. Super-twisting sliding mode control is also commonly used to suppress chattering in controllers, which generates continuous control signals, effectively reducing chattering [11; 12; 13]. A new composite controller integrating STSMC was devised for speed and altitude tracking control in air-breathing hypersonic vehicles [14]. This illustrates the versatility and applicability of STSMC in addressing chattering-related challenges across various control domains. The great approximation performance of neural networks has been verified in many studies [15; 16; 17]. In [18], [PERSON] et al. incorporated an RBF neural network into a primary controller, achieving the faster and more accurate recovery of unknown disturbances, thereby addressing the issue of system uncertainty. Fuzzy logic rules are considered as one of the effective approaches to deal with nonlinear problems. Incorporating fuzzy rules into neural networks proves highly advantageous for enhancing their performance [19; 20; 21; 22]. The implementation of an adaptive universal type-2 fuzzy neural network control system enhances the anti-interference capability in the motion balance of power line inspection robots [23]. For neural networks designed for dynamic pattern recognition, the introduction of a self-feedback structure enables the storage and utilization of historical information, thereby enhancing the network's memory capacity and better adapting to changes and uncertainties [24; 25; 26; 27]. While the theoretical validation of the method has been conducted in previous studies, the feasibility and superiority of the algorithm in simulations are primary concerns. Hence, the simulation environment is an ideal experimental setting, allowing for the selection of high sampling frequencies to achieve rapid data acquisition [28]. However, in practical applications, constraints on the computational capabilities of control devices often prevent the realization of the ideal sampling frequencies observed in simulations. Therefore, the designed method needs to be validated for its feasibility on a hardware experimental platform. Inspired by the preceding studies, this paper proposes an adaptive super-twisting sliding mode control method, incorporating an interval type-2 fuzzy neural network with a self-feedback recursive structure (IT2 FNN-SFR STSMC), aiming to enhance the compensation accuracy of the APF system. The novel IT2 FNN-SFR consolidates the advantages of IT2 FNN and RNN, leading to superior dynamic approximation capacity. The primary contributions of this article are outlined as follows: (1) A novel structure named IT2 FNN-SFR has been presented, which amalgamates the robustness inherent in IT2 FNN with the dynamic response characteristics of Recurrent Neural Networks (RNNs).