[Spatial Network of Urban Heat Environment in Beijing-Tianjin-Hebei Urban Agglomeration Based on MSPA and Circuit Theory].

Rapid urbanization has increased the complexity of the urban heat environment system, which has negative impacts on the health of the urban ecological system and the human habitat. By combining theories and technologies such as geographic information systems, remote sensing, morphological spatial pattern analysis, and circuit theory with data from MODIS land surface temperature production, urban heat island patches in the Beijing-Tianjin-Hebei urban agglomeration were quantitatively identified in terms of their spatial and temporal distribution characteristics and their spatial and temporal transfer paths. This foundation revealed the geographical network structure of the urban heat environment as well as the spatial and temporal evolution process of critical corridors. According to the findings, urban heat island patches covered 16610 km2 in 2020, accounting for 7.68% of the study area. In the Beijing-Tianjin-Hebei urban agglomeration, both the area and the number of urban heat island patches considerably increased between 2005 and 2020, going from being dominated by isolated island types of urban heat island patches in 2005 to being dominated by core types in 2020. In particular, the non-urban heat island patches, core type, and edge type of urban heat island patches in 2005 were the major ancestors of the core type and edge type urban heat island patches in 2020. In the Beijing-Tianjin-Hebei urban agglomeration, there were more urban heat environment source sites, corridor length, densities, and present densities in 2020 than there were in 2005. The sensitive corridor was found to be the predominant type of urban heat island corridor in the Beijing-Tianjin-Hebei urban agglomeration in 2020. The number of sensitive corridors increased the highest in the period from 2005-2020. As the coefficient of urban heat environment corridors increased concurrently, it was apparent that the urban heat environment corridor had a propensity to grow continuously in the Beijing-Tianjin-Hebei urban agglomeration. The active adaptation and mitigation measures of the urban heat environment were proposed, and a spatial network model of the urban heat environment was finally provided. To adapt to, mitigate, and promote urban sustainable development risks, these research findings will serve as a paradigm for the identification of the urban heat environment spatial network actively and methodically.

Fixed-Time Adaptive Neural Network Control for Nonlinear Systems With Input Saturation.

This study concentrates on the tracking control problem for nonlinear systems subject to actuator saturation. To improve the performance of the controller, we propose a fixed-time tracking control scheme, in which the upper bound of the convergence time is independent of the initial conditions. In the control scheme, first, a smooth nonlinear function is employed to approximate the saturation function so that the controller can be designed under the framework of backstepping. Then, the effect of input saturation is compensated by introducing an auxiliary system. Furthermore, a fixed-time adaptive neural network control method is given with the help of fixed-time control theory, in which the dynamic order of controllers is reduced to a certain extent since there is only one updating law in the entire control design. Through rigorous theoretical analysis, it is concluded that the proposed control scheme can guarantee that: 1) the output tracking error can converge to a small neighborhood near the origin in a fixed time and 2) all signals in the closed-loop system are bounded. Finally, a numerical example and a practical example based on the single-link manipulator are provided to verify the effectiveness of the proposed method.

Global stabilization via output feedback for a class of uncertainty nonlinear systems with time-varying delay and zero dynamics.

This paper proposes a new control strategy to deal with three different types of uncertainties for a class of time-varying delay nonlinear systems. Quite different from related celebrated works, the considered systems allow the existence of parameter uncertainties in the output function and nonlinearities, dynamic uncertainties of the unmeasurable state, and continuous external disturbances, which invokes the motivation of the paper. The objective is to construct a continuous output feedback controller by utilizing a new restructured integral function and the double-domination method. The novelty control design is the capability of tackling zero-dynamic and unknown output function simultaneously, thereby guaranteeing the state convergence of the closed-loop system. Finally, the proposed scheme is applied to a practical example and a numerical one simultaneously to demonstrate the effectiveness and the superiority.

Adaptive Event-Triggered Fast Finite-Time Stabilization of High-Order Uncertain Nonlinear Systems and Its Application in Maglev Systems.

This article is concerned with the global fast finite-time adaptive stabilization for a class of high-order uncertain nonlinear systems in the presence of serious nonlinearities and constraint communications. By renovating the technique of continuous feedback domination to the construction of a serial of integral functions with nested sign functions, this article first proposes a new event-triggered strategy consisting of a sharp triggered rule and a time-varying threshold. The strategy guarantees the existence of the solutions of the closed-loop systems and the fast finite-time convergence of original system states while reaching a compromise between the magnitude of the control and the trigger interval. Quite different from traditional methods, a simple logic is presented to avoid searching all the possible lower bounds of trigger intervals. An example of the maglev system and a numerical example are provided to demonstrate the effectiveness and superiority of the proposed strategy.

Global Finite-Time Stabilization for Uncertain Systems With Unknown Measurement Sensitivity.

This article focuses on global finite-time output feedback stabilization for uncertain nonlinear systems with unknown measurement sensitivity. The existence of the continuous measurement error resulting from limited accuracy of sensors invalidates the existing design strategies depending on the use of the precise output in the construction of an observer, which highlights the contribution of this article. Essentially, different from related works, we propose a new finite-time convergent observer by avoiding the use of the information on nonlinearities. By combining the homogeneous domination with the addition of a power integrator method, an output feedback controller composed of multiple nested sign functions is successfully developed. Finally, the effectiveness of the presented scheme is exhibited by a numerical example.