Yang Guangyi, Bekiros Stelios, Yao Qijia, Mou Jun, Aly Ayman A, Sayed Osama R
Information Center, Hunan Institute of Metrology and Test, Changsha 410014, China.
Department of Management, University of Turin (UniTo), 10134 Turin, Italy.
Entropy (Basel). 2024 Dec 10;26(12):1078. doi: 10.3390/e26121078.
Many existing control techniques proposed in the literature tend to overlook faults and physical limitations in the systems, which significantly restricts their applicability to practical, real-world systems. Consequently, there is an urgent necessity to advance the control and synchronization of such systems in real-world scenarios, specifically when faced with the challenges posed by faults and physical limitations in their control actuators. Motivated by this, our study unveils an innovative control approach that combines a neural network-based sliding mode algorithm with fuzzy logic systems to handle nonlinear systems. This proposed controller is further enhanced with an intelligent observer that takes into account potential faults and limitations in the control actuator, and it integrates a fuzzy logic engine to regulate its operations, thus reducing system chatter and increasing its adaptability. This strategy enables the system to maintain regulation in the face of control input constraints and faults and ensures that the closed-loop system will achieve convergence within a finite-time frame. The detailed explanation of the control design confirms its finite-time stability. The robust performance of the proposed controller applied to autonomous and non-autonomous systems grappling with control input limitations and faults demonstrates its effectiveness.
文献中提出的许多现有控制技术往往忽视系统中的故障和物理限制，这严重限制了它们在实际现实世界系统中的适用性。因此，迫切需要推进此类系统在现实场景中的控制与同步，特别是当面对其控制执行器中的故障和物理限制所带来的挑战时。受此启发，我们的研究揭示了一种创新的控制方法，该方法将基于神经网络的滑模算法与模糊逻辑系统相结合来处理非线性系统。所提出的控制器通过一个智能观测器得到进一步增强，该观测器考虑了控制执行器中的潜在故障和限制，并集成了一个模糊逻辑引擎来调节其操作，从而减少系统抖动并提高其适应性。这种策略使系统能够在面对控制输入约束和故障时保持调节，并确保闭环系统在有限时间内实现收敛。控制设计的详细解释证实了其有限时间稳定性。所提出的控制器应用于面临控制输入限制和故障的自治和非自治系统时的鲁棒性能证明了其有效性。
