Designing an optimal iterative learning control is a huge challenge for linear and nonlinear dynamic systems. For such complex systems, standard Norm optimal iterative learning control (NOILC) is an important consideration. This paper presents a novel NOILC error convergence technique for a discrete-time method. The primary effort of the controller is to converge the error efficiently and quickly in an optimally successful way. A new iterative learning algorithm based on feedback based on reliability against input disruption was proposed in this paper. The illustration of the simulations authenticates the process suggested. The numerical example simulated on MATLAB@2019 and the mollified results affirm the validation of the designed algorithm.

Left atrial (LA) fibrosis plays a vital role as a mediator in the progression of atrial fibrillation. 3D late gadolinium-enhancement (LGE) MRI has been proven effective in identifying LA fibrosis. Image analysis of 3D LA LGE involves manual segmentation of the LA wall, which is both lengthy and challenging. Automated segmentation poses challenges owing to the diverse intensities in data from various vendors, the limited contrast between LA and surrounding tissues, and the intricate anatomical structures of the LA. Current approaches relying on 3D networks are computationally intensive since 3D LGE MRIs and the networks are large. Regarding this issue, most researchers came up with two-stage methods: initially identifying the LA center using a scaled-down version of the MRIs and subsequently cropping the full-resolution MRIs around the LA center for final segmentation. We propose a lightweight transformer-based 3D architecture, Usformer, designed to precisely segment LA volume in a single stage, eliminating error propagation associated with suboptimal two-stage training. The transposed attention facilitates capturing the global context in large 3D volumes without significant computation requirements. Usformer outperforms the state-of-the-art supervised learning methods in terms of accuracy and speed. First, with the smallest Hausdorff Distance (HD) and Average Symmetric Surface Distance (ASSD), it achieved a dice score of 93.1% and 92.0% in the 2018 Atrial Segmentation Challenge and our local institutional dataset, respectively. Second, the number of parameters and computation complexity are largely reduced by 2.8x and 3.8x, respectively. Moreover, Usformer does not require a large dataset. When only 16 labeled MRI scans are used for training, Usformer achieves a 92.1% dice score in the challenge dataset. The proposed Usformer delineates the boundaries of the LA wall relatively accurately, which may assist in the clinical translation of LA LGE for planning catheter ablation of atrial fibrillation.

The Hall sensor is the most commonly used position sensor of the permanent magnet brushless direct current (PMBLDC) motor. Its failure may lead to a decrease in system reliability. Hence, this article proposes a novel methodology for the Hall sensors fault diagnosis and fault-tolerant control in PMBLDC motor drives. Initially, the Hall sensor faults are analyzed and classified into three fault types. Taking the Hall signal as the system state and the conducted MOSFETs as the system event, the extended finite state machine (EFSM) of the motor in operation is established. Meanwhile, a motor speed observer based on the super twisting algorithm (STA) is designed to obtain the speed signal of the proposed strategy. On this basis, a real-time Hall sensor fault diagnosis strategy is established by combining the EFSM and the STA speed observer. Moreover, this article proposes a Hall signal reconstruction strategy, which can generate compensated Hall signal to realize fault-tolerant control under single or double Hall sensor faults. Finally, theoretical analysis and experimental results validate the superior effectiveness of the proposed real-time fault diagnosis and fault-tolerant control strategy.

The main idea of iterative learning control (ILC) was exposed when Arimoto's first paper was published. Industrial tasks mainly in repetition are controlled by an iterative teaching controller in both iteration and time-domain which makes ILC unique. ILC is becoming very popular among control engineers because of its very simple and effective control techniques. This paper describes the basic key knowledge about ILC and its types of applications. The core concern of this paper is to elaborate and explore the future scope and key application areas of iterative learning control in engineering as well as other all subjects of interest.

This paper proposes a backstepping fuzzy sliding mode control method for the antiskid braking system (ABS) of unmanned aerial vehicles (UAVs). First, the longitudinal dynamic model of the UAV braking system is established and combined with the model of the electromechanical actuator (EMA), based on reasonable simplification. Subsequently, to overcome the higher-order nonlinearity of the braking system and ensure the lateral stability of the UAV during the braking process, an ABS controller is designed using the barrier Lyapunov function to ensure that the slip ratio can track the reference value without exceeding the preset range. Then, a power fast terminal sliding mode control algorithm is adopted to realize high-performance braking pressure control, which is required in the ABS controller, and a fuzzy corrector is established to improve the dynamic adaptation of the EMA controller in different braking pressure ranges. The experimental results show that the proposed braking pressure control strategy can improve the servo performance of the EMA, and the hardware in loop (HIL) experimental results indicate that the proposed slip ratio control strategy demonstrates a satisfactory performance in terms of stability under various runway conditions.