How do you find this paper introduction, and could you suggest articles as references for each idea, more recent than 2015? : Induction motors have enjoyed great success in industry and transport applications due to their affordability, robustness and low maintenance costs. However, these motors present particular challenges in terms of control due to their non-linear dynamics and sensitivity to parameter variations. Their control requires advanced techniques to guarantee optimum performance.
Direct torque control (DTC) is a control technique widely used in induction motor (IM) control applications, as it offers rapid dynamic response and ease of implementation. Nevertheless, conventional DTC has significant limitations, including high torque and flux ripples, as well as variable switching frequencies. These problems adversely affect system performance, generate mechanical vibrations and lead to increased component resistance.
To overcome these limitations, the Double Power Super Twisting Algorithm (DPSTA) has been proposed, combining the advantages of the Super Twisting Algorithm (STA), which reduces the chattering phenomenon, and those of the Double Power Convergence Law (DPRL), which improves the reaching speed as well as the dynamic quality of the system. As described in our previous work [1], the performance of the proposed controller is evaluated in the context of DTC control of IM driving electric vehicle, and compared with that of PI, fuzzy logic and traditional STA controllers. Numerical results show that DPSTA improves the robustness of the control system and significantly reduces chattering, while maintaining high dynamic performance.
However, since the operating conditions of induction motors vary considerably due to various factors such as load changes, temperature variations and component ageing, determining the optimum sliding-mode gains of the DPSTA controller is a major challenge in maintaining optimum performance. Incorrect setting of these parameters can lead to degraded dynamic performance and even system instability, which is unacceptable in critical industrial environments.
To overcome these challenges, we propose in this paper an extension of the DPSTA algorithm that integrates an adaptive single-neuron approach. The adaptive single neuron method offers an efficient solution for dynamically adjusting controller parameters in real time. This approach exploits the learning and adaptation capabilities of neural networks, even in a simplified form with a single neuron, to optimize controller parameters in response to variations in operating conditions without over-complexifying the controller. Compared with traditional multi-layer neural networks, a single neuron reduces computational complexity and facilitates practical implementation, while retaining effective adaptability.
Recent studies have shown that the use of artificial intelligence techniques, such as neural networks and adaptive algorithms, can lead to significant improvements in dynamic response and stability [10]. IN [1],
With the integration of a single adaptive neuron into the DPSTA algorithm, we aim to improve robustness, torque and flux ripples, as well as dynamic response.
This article is structured as follows: In the second section, we outline the concept of the single adaptive neuron and its integration into the DPSTA controller for the DTC method. The third section presents the system modeling and simulation method. We then present the simulation results and a detailed performance analysis of the proposed system. At the end of the study, we conclude by summarizing the main contributions.