Nonlinear rational systems/models, also known as total nonlinear dynamic systems/models, in an expression of a ratio of two polynomials, have roots in describing general engineering plants and chemical reaction processes. The major challenge issue in the control of such a system is the control input embedded in its denominator polynomials. With extensive searching, it could not find any systematic approach in designing this class of control systems directly from its model structure. This study expands the U‐model‐based approach to establish a platform for the first layer of feedback control and the second layer of adaptive control of the nonlinear rational systems, which, in principle, separates control system design (without involving a plant model) and controller output determination (with solving inversion of the plant U‐model). This procedure makes it possible to achieve closed‐loop control of nonlinear systems with linear performance (transient response and steady‐state accuracy). For the conditions using the approach, this study presents the associated stability and convergence analyses. Simulation studies are performed to show off the characteristics of the developed procedure in numerical tests and to give the general guidelines for applications.

In this paper, speed control curve of stepper motor was studied for optimizing the motion flexibility and improving the motion precision of the stepper motor under the open-loop control environment based on Microcontroller Unit (MCU). Firstly, the acceleration and deceleration control curve of trapezoidal and New S model was designed respectively. Secondly, the relationship curve between control pulse and time was discretized and the stepper motor control scheme was determined. The MCU provided drive pulse for stepper motor to realize the motion control. The contrast of running accuracy of stepper motor controlled by the two kinds of acceleration and deceleration curves was carried out. The results show that the stepper motor controlled by the new S-shape acceleration and deceleration curve has great performs of small noise, high positioning accuracy and good flexibility.

Hyperspectral imaging, a key technology in remote sensing, captures rich spectral information beyond the visible spectrum, rendering it indispensable for advanced classification tasks. However, with developments in hyperspectral imaging, spatial–spectral redundancy and spectral confusion have increasingly revealed the limitations of convolutional neural networks (CNNs) and vision transformers (ViT). Recent advancements in state space models (SSMs) have demonstrated their superiority in linear modeling compared to convolution and transformer-based approaches. Based on this foundation, this study proposes a model named AMamNet that integrates convolutional and attention mechanisms with SSMs. As a core component of AMamNet, Attention-Bidirectional Mamba Block, leverages the self-attention mechanism to capture inter-spectral dependencies, while SSMs enhance sequential feature extraction, effectively managing the continuous nature of hyperspectral image spectral bands. Technically, a multi-scale convolution stem block is designed to achieve shallow spatial–spectral feature fusion and reduce information redundancy. Extensive experiments conducted on three benchmark datasets, namely the Indian Pines dataset, Pavia University dataset, and WHU-Hi-LongKou dataset, demonstrate that AMamNet achieves robust, state-of-the-art performance, underscoring its effectiveness in mitigating redundancy and confusion within the spatial–spectral characteristics of hyperspectral images.

Remote sensing images are increasingly used in applications, such as environmental monitoring and urban planning. However, the complexity of these images poses challenges for accurate object segmentation, particularly when guided by referring expressions. In this letter, we propose RSTG-segment anything model (SAM), a novel two-stage framework for referring remote sensing image segmentation (RRSIS), which builds upon the SAM. In the first stage, a multimodal encoder and a transformer-based prompt generator produce a sparse prompt that provides coarse object localization based on fused vision-language features. In the second stage, SAM utilizes this prompt to conduct fine-grained segmentation. We also introduce a lightweight text-guided adapter module for the efficient fine-tuning of SAM and a dual-attention fusion (DAF) module to enhance multimodal alignment. Experiments on the RRSIS-D dataset demonstrate that RSTG-SAM achieves the state-of-the-art performance across multiple metrics, particularly in handling small targets.

Three-phase AC/DC converter is widely used in many industrial applications. To improve performance, this paper proposes an adaptive neural network based controller design for three-phase PWM AC/DC voltage source converters. The controller is designed based on a nonlinear multi-input multi-output model using Lyapunovpsilas direct method. Since neural networks can approximate unknown nonlinear dynamics, there is no need to know the parameters of the system. In this way, the controller is robust to parameter drifting and changes of operating points. Additionally, the proposed control can be applied directly online after initialization. Thus, the time-consuming offline training process is avoided. Furthermore, the proposed controller design also avoids the singularity problem, which may exist in regular feedback linearization based controls. Co-simulation using Matlab/Simulink and PSIM demonstrates the effectiveness of the proposed controller design.