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In this paper, a novel two-stage epidemic model with a dynamic control strategy is proposed to describe the spread of Corona Virus Disease 2019 (COVID-19) in China. Combined with local epidemic control policies, an epidemic model with a traceability process is established. We aim to investigate the appropriate control strategies to minimize the control cost and ensure the normal operation of society under the premise of containing the epidemic. This work mainly includes: (i) propose the concept about the first and the second waves of COVID-19, as well as study the case data and regularity of four cities; (ii) derive the existence and stability of the equilibrium, the parameter sensitivity of the model, and the existence of the optimal control strategy; (iii) carry out the numerical simulation associated with the theoretical results and construct a dynamic control strategy and verify its feasibility.

This paper proposes a design technique for vehicle lateral stability control. In this structure, reference variables are first determined based on the driver’s input. Using a fixed-time control technique, an upper controller, also known as virtual control, is designed to ensure vehicle stability for the desired yaw moment. Subsequently, optimal braking forces, also known as lower controls, are designed by virtual control and are utilized to generate the control inputs for the actuators. The optimal braking forces, which are constrained, are designed using a fixed-time dynamic control allocation method. This ensures their convergence to optimal values in a fixed time. Unlike static optimization methods, the proposed control allocation method introduces a dynamic update law. This approach not only reduces computational complexity but also guarantees the fixed-time convergence of braking forces to the optimal solution. The overall closed-loop stability in constant time is also achievable via the Lyapunov stability method. Simulations were conducted on a 10-DOF nonlinear dynamic vehicle model for standard test maneuvers of the electronic stability control system. The results indicate that the proposed method outperforms numerical optimization-based methods such as weighted least squares, weighted pseudoinverse, and asymptotic dynamic control allocation in terms of efficiency.

We present a method to solve fractional optimal control problems, where the dynamic control system depends on integer order and Caputo fractional derivatives. Our approach consists in approximating the initial fractional order problem with a new one that involves integer order derivatives only. The latter problem is then discretized, by application of finite differences, and solved numerically. We illustrate the effectiveness of the procedure with an example.

In the extreme environmental scenario, due to global warming and pollution, and in the necessary condition of energy saving, this research proposes an interdisciplinary methodological approach as a tool for environmental, urban/architectural and energy design, applied at an urban scale, i.e. to the complex building-plant system and its surroundings (built, unbuilt and/or green). Starting from the fundamental concept that the shape of any city, and on a smaller scale than of a single building, is an energy form, in the present research the urban environment has a shape and a structure directly connected to its energetic performances and behaviours in relation with environmental and social consequences that affect energy policies and adaptation to climate change but above all with the reduction of anthropogenic impacts. The present article shows the preliminary results of the ESCAPOS-LIFE-2023-2027 project for Florence. It is based on the concept of Dynamic Control Volume (DCV), a virtual capillary volume structured as a network that acquires information and knowledge in itself, a systemic, intelligent, and fast tool. The DCV is the operational tool with which, through a capillary network of always available and updatable, open-source information, all existing databases, urban plans, green plans, local-metropolitan agendas for sustainability, action plans for sustainable energy and climate, programs, policies, strategies and planning for climate adaptation are integrated and interfaced with each other, with a view to protecting health, biodiversity, forestation and the green energy economy.

The oil-ringed condensate gas reservoir is one of the more unique and complex types of oil and gas reservoirs. The stable oil and gas interface is crucial for the efficient development of such reservoirs, and the fundamental means of addressing the issue lies in maintaining the dynamic balance between the gas cap and the oil ring. In this paper, we address issues such as the rapid decline in oil ring production, gas cap breakthrough, and increased condensate oil loss exposed during the development of the R carbonate reservoir in the Caspian Sea Basin of Kazakhstan. Using reservoir engineering and numerical simulation methods, we optimized synergistic development ideas and the boundaries of exploitation policies for gas-capped condensate gas reservoirs and oil rings. We formulated reasonable development parameters such as extraction rate, injection-production ratio, barrier water injection ratio, and proposed dynamic control standards for zoning and classification. After implementing the plan, the gas-oil ratio of the oil ring significantly decreased, with a reduction rate of 12 percentage points. The gas production per unit pressure drop increased by 40%. The annual decline rate of condensate oil decreased from 23.6% to 13.8%. All development indicators exhibited characteristics of benign development, providing a theoretical foundation and technical methods for guiding the efficient development of similar oil and gas reservoirs.

In this letter, a control strategy comprising velocity and yaw rate controllers is proposed for a real four-wheel vehicle in a leader-follower formation when the leader vehicle drives at high speed, i.e., 100 km/h. Since vehicle stability plays an increasingly important role as speed increases, vehicle dynamics must be considered in vehicle formation control. Therefore, to increase the accuracy of the formation geometric model, bicycle model-based leader-follower formation models are suggested, which are denoted as the follower (F) bicycle model and the leader-follower (LF) bicycle model. Then, the velocity and yaw rate control of the follower vehicle is designed. In addition, vehicle longitudinal and wheel dynamic models are considered in the velocity control to generate the wheel torque. Finally, the control gains are determined under conditions that satisfy the Routh-Hurwitz stability criterion, which guarantees the stability of the vehicle simplified as a first-order lag model. The performance of the proposed leader-follower bicycle model and controllers are strictly demonstrated by implementing vehicle dynamics simulations in cases when vehicles in a formation drive at high speeds. The simulation results confirm that the suggested formation control strategy can be applied to real four-wheel vehicles under high-speed conditions on various types of paths, in comparison with the unicycle model-based formation shape model.

Purpose In pursuit of higher maneuverability and aerodynamic characteristics, aerodynamic configuration for an attitude control system of aircraft adopts generally an over-actuated form. The purpose of this paper is to address allocation problems of multiple actuators to improve the system rapidity and performance and simultaneously solve parameter uncertainties of the aircraft attitude control system. Design/methodology/approach Taking into account the respective frequency characteristics of actuators as well as position and rate constraints, the proposed method extends regular quadratic-programming control allocation by using a dynamic weighting matrix. And a robust controller, issuing a virtual control command to be allocated, is designed based on H∞ mixed sensitivity design. Findings Given an attitude control system with parametric uncertainties, the proposed virtual robust controller and control allocation method is verified and the system performance is improved. Originality/value The proposed dynamic control allocation method is designed based on actuator dynamic characteristics and the changing rate of the virtual control command, considering some actuator constraints simultaneously. The efficiency of the attitude control system is improved and the complex multi-input and multi-output system model can be simplified.

To enhance the maneuverability and spatial adaptability of quadrotors, this study proposes a deformable quadrotor with active foldable arms and a modular actuation mechanism. The folding mechanism integrates an offset slider-crank mechanism, a rack-and-pinion mechanism, and a single servo motor, enabling synchronous folding-deployment of the arms with high structural compactness. First, a comprehensive dynamic model of the quadrotor is established, incorporating a multi-wind-field model (covering basic wind, gust wind, gradual wind, and random wind) to characterize real-world flight disturbances. Key dynamic variations induced by arm folding—including center-of-mass (CoM) displacement, moment of inertia changes, and transient forces/torques—are quantified and embedded into the model, forming a complete dynamic control system for the deformable platform. Subsequently, a position-attitude controller based on Active Disturbance Rejection Control (ADRC) is designed. To improve anti-wind-disturbance performance, the fal function (a nonlinear piecewise function) in the ADRC’s Extended State Observer (ESO) is optimized into a piecewise fast optimal function (Pfal), yielding a Superior ADRC (SADRC) scheme with enhanced disturbance estimation and suppression capabilities. Simulation and experimental results indicate that: (1) via simulation, the arm folding-deployment process is tested in hovering conditions, verifying that the proposed fast dynamic control method outperforms traditional strategies in folding speed significantly; (2) flight stability under mixed wind disturbances is evaluated, where the optimized SADRC controller delivers better attitude/position tracking accuracy than conventional PID and standard ADRC methods; (3) experimental results demonstrate that the Fast Control method reduces the folding-deployment time by approximately 22.9% compared with the General Control method, with the deviation of the time reduction rate between experimental and simulated results being about 1.6%, which validates the effectiveness of the proposed Fast Control method. These works lay a foundation for the subsequent experimental research on quadrotors based on the combination of dynamic control methods and fast folding-deployment schemes, and also provide a new solution for active foldable arm quadrotors performing tasks in narrow spaces.

Intensive care unit (ICU) beds are valuable resources that are typically in short supply and therefore their effective and efficient use is essential particularly during periods when patient demand is high. In “Allocation of Intensive Care Unit Beds in Periods of High Demand,” H. Ouyang, N.T. Argon, and S. Ziya provide insights into what kind of patient prioritization decisions are likely to improve patient health outcomes by analyzing stylized mathematical formulations that capture the fundamental trade-off involved in ICU bed management. They also propose simple policies that are likely to perform well in practice and test them with a simulation study. Findings suggest that even simple policies are likely to bring significant benefits, although more work is needed to investigate whether there could be benefits to using methods that aim to capture patient health condition in a manner that is more precise than assumed in the paper. The objective of this paper is to use mathematical modeling and analysis to develop insights into and policies for making bed allocation decisions in an intensive care unit (ICU) of a hospital during periods when patient demand is high. We first develop a stylized mathematical model in which patients’ health conditions change over time according to a Markov chain. In this model, each patient is in one of two possible health stages, one representing the critical and the other representing the highly critical health stage. The ICU has limited bed availability and therefore when a patient arrives and no beds are available, a decision needs to be made as to whether the patient should be admitted to the ICU and if so, which patient in the ICU should be transferred to the general ward. With the objective of minimizing the long-run average mortality rate, we provide analytical characterizations of the optimal policy under certain conditions. Then, based on these analytical results, we propose heuristic methods, which can be used under assumptions that are more general than what is assumed for the mathematical model. Finally, we demonstrate that the proposed heuristic methods work well by a simulation study, which relaxes some of the restrictive assumptions of the mathematical model by considering a more complex transition structure for patient health and allowing for patients to be possibly queued for admission to the ICU and readmitted from the general ward after they are discharged.

This paper presents a hierarchical control architecture for high-precision trajectory tracking of a car-like mobile robot (CLMB) operating under external disturbances arising from normal and tangential wheel forces. The proposed solution addresses the critical challenge of simultaneously rejecting disturbances and accurately following a predefined path at a determined cruise velocity. Since the vehicle is equipped with an electronic differential at the low level, a nonlinear dynamic control (NDC) scheme is implemented to regulate the speed in each wheel. This controller actively estimates and compensates for differential traction losses and other lumped disturbances in real time, ensuring robust wheel velocity tracking across varying terrain conditions. The compensated system is then governed by a high-level frugal model predictive controller (FMPC) that leverages a dynamic vehicle model to compute optimal steering and velocity commands, thereby minimizing future trajectory-tracking errors. To achieve a precise and reliable state estimation necessary for feedback control, an Extended Kalman Filter (EKF) is designed to fuse high-frequency data from wheel encoders with absolute pose measurements from a motion capture system, mitigating the drift inherent in odometry alone. Experimental results on a physical robotic platform demonstrate tracking accuracy and robust disturbance rejection under different operating conditions.