This paper mainly studies the modeling, stability analysis and proportional-integral (PI) controller design for a class of discrete-time cascade control systems (CCSs). In the existing CCSs for solving gain of the controller, the controller mostly adopts the proportional (P) controller, but the most commonly used controller in industrial production is PI controller. Based on the actual demand in industry and in order to obtain a better control effect, the design problem of primary and secondary controllers in a class of discrete cascade control is considered, in which the primary controller adopts a PI controller and the secondary controller adopts a P controller. On this basis, the model of cascade control system (CCS) is established. Then, we can create a new Lyapunov functional, the sufficient conditions for the stability of the system is given. Then, the collaborative design method of primary PI controller and secondary P controller is given by using linear matrix inequality (LMI) technique. Finally, a simulation example of a main steam temperature with CCS structure is given to demonstrate the effectiveness of the method. The PI controller is better than the existing P controller, this method has not only the rapidity of P control, but also the ability of integral control to eliminate steady-state errors. Note to Practitioners — The inspiration of this paper comes from the adjustment of plant controller parameters. The PI controller is the most widely used controller in the existing industrial production, but the adjustment of proportional and integral parameters of the controller is a very troublesome thing. In order to obtain a better controller parameters, a professional engineer may spend a whole day to adjust the parameters. This paper can design the solution of the primary and secondary controller parameters to stabilize the CCS at the same time by LMI technique, which can greatly reduce the work of adjusting the controller parameters. Through simulation, we can know that this method can make the system stable, which shows this method is feasible.

The canal waterways of China still play an important role in the logistics and transportation industry. Unmanned technology helps to reduce costs and improve the safety of navigation. Real-time environmental awareness is vital to making unmanned surface vehicles (USVs) come true. This paper proposes a new lightweight environmental awareness method based on deep convolutional neural networks ( DCNN ) and a mixed attention mechanism for USVs in canals, which can simultaneously perform ship detection, segmentation, and surface and background segmentation tasks. The features of the ships, surface, and background are extracted by a shared feature extraction backbone network and hybrid attention mechanism, which improves the efficiency of visual environmental awareness. In addition, a dataset named USV-Canal is constructed to enrich the features of canal waterways for environmental awareness, which contains typical canal scenes and 3443 ship objects. To improve the generalization, multiple public datasets are mixed with the USV-Canal dataset to build an integrated dataset to train our model, which boasts diversity in scene types and ship classes. The comparative and field experiments’ results show that 40.9% of mAP , 95.8% of mIoU, and 5 frames per second ( FPS ) inference speed can be achieved, and have good generalization, which can meet the requirements of environmental awareness of low-speed ships in canal waterways Note to Practitioners —The trained and validated model can ultimately be deployed on unmanned surface vehicles, and the required hardware platform is NVIDIA’s Jetson Nano, which is used for real-time perception of surrounding ships and navigable surfaces during navigation. The information can be integrated into the guidance, navigation, and control (GNC) system of USVs, achieving obstacle avoidance and ensuring safe navigation. It is vital to make autonomous navigation come true.

Networked cascade control systems (NCCS) are susceptible to performance degradation from network-induced delays, communication constraints, and component failures. To address these interconnected challenges, this paper develops an integrated fault-tolerant H-infinity control framework for discrete-time nonlinear NCCS. A key contribution is a novel, disturbance-aware adaptive event-triggered mechanism (AETM) that uniquely incorporates measurable external disturbances into its triggering logic. This design dynamically reduces communication frequency while preserving performance. Furthermore, a unified co-design of the controller parameters and the AETM’s weighting matrix is established, while stability is rigorously substantiated through theoretical analysis. Simulation results for a boiler control system validate the proposed method’s effectiveness, demonstrating significant improvements in both system robustness and communication efficiency.

This paper is concerned with the problems of modeling, stabilization, and H∞ control for continuous-time singular networked cascade control systems (SNCCSs) with state delay and event-triggered control. An event-triggered scheme is firstly introduced to this system for utilizing the network bandwidth resources efficiently. Considering the effects of both time-varying network-induced delay and event-triggered control, a singular networked cascade control system (SNCCS) model is established. By constructing a suitable Lyapunov-Krasovskii functional, sufficient condition of admissibility for this system is proposed, and the co-design method of event-triggered parameter, primary state feedback controller and secondary state feedback controller are also derived. Furthermore, H∞ control is concerned for SNCCS via linear matrix inequality (LMI) technique. Finally, a simulation example considering a heating furnace with the structure of SNCCS and event-triggered control is given to illustrate the effectiveness of the proposed method, where it can be seen this method is superior to the existing one with periodic control.

This paper studies the problem of distributed containment control for multi-agent systems with high-order dynamics and input delays. Two event-triggered control algorithms are proposed for multi-agent systems without and with input delay, respectively. The communication instants between two linked followers are determined by the event-triggering condition, and every follower can detect the event based on its own control input. For the followers, edge-based estimators are adopted to predict state differences to neighbors. Control inputs of the followers are calculated based on the predicted values of the state differences. To deal with the input delay, a delay comprehension approach is developed. It is proved that for arbitrarily large but bounded input delays, the followers can move into the convex hull spanned by the leaders asymptotically. Simulation results show the effectiveness of the proposed algorithms.