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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.

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.

In this paper, we present the design and control of a novel aerial vehicle inspired by the biomechanics of a rabbit named “Flybbit”. Flybbit consists of two main components, namely a movable “Ears” part and a rigid “Body” part, forming a composite flying system with five controllable degrees of freedom (DOFs). The “Ears” part is equipped with two tiltable motors paired with optional-sized propellers, enabling additional thrust generation and flight stability maintenance, and the “Body” part incorporates four fixed motors, analogous to a rabbit’s limbs, to provide the primary propulsion. To fully exploit the actuation capability, we derive the system dynamics and introduce a dynamic control allocation method with an adaptive strategy to mitigate actuator saturation during complex combined maneuvers. Furthermore, we analyze the differential flatness property and develop a nonlinear inverse dynamics controller enhanced with hybrid external wrench estimation, enabling accurate trajectory tracking in five DOFs. Flybbit supports both manual operation via RC and autonomous flight via onboard computation. Comprehensive simulations and real-world experiments validate the proposed design and control framework.

This paper presents a three‐layer control architecture designed to enhance vehicle lateral stability under uncertain and varying road conditions. The system utilizes output‐feedback‐based finite‐time controllers to track the desired yaw rate and longitudinal slip in the upper and lower layers, respectively. The middle layer incorporates a finite‐time robust dynamic control allocation to distribute longitudinal slips among the tires. This approach effectively handles uncertainties and changing road conditions without the need for direct estimation of unmeasurable variables such as tire‐road friction and sideslip angle. The proposed output feedback control law consists of a stabilizer component to ensure finite‐time stability, a compensator to eliminate the unknown function in the upper and lower layers, and an auxiliary tracking term. Key advantages of the proposed framework include: no requirement for additional sensors, finite‐time convergence, reduced computational complexity compared to optimization‐based methods, and the ability to perform finite‐time stability analysis for the integrated closed‐loop system. The system performance is evaluated using a validated 10‐degree‐of‐freedom vehicle dynamics model and CarSim simulations during a double‐lane change maneuver. Simulation results demonstrate the superiority of the proposed control structure over sliding mode control, offering improved tracking accuracy and robust performance under varying road conditions.

This paper proposes a fault tolerant control strategy for drone interceptors with fixed wings and reaction jets subject to actuator faults. The drone interceptors have both continuous and discrete actuators, which pose a challenge to the control system design. The proposed fault tolerant control system consists of two parts, a nonlinear virtual control law and a dynamic control allocator. To deal with system uncertainty and quantization error, a virtual control law with a parameter update law is designed by command filtered backstepping. Then, a fault weighting dynamic control allocation algorithm is developed to distribute the virtual control signal to the actuators on the drone interceptor. When an actuator fault occurs, the proposed fault weighting dynamic control allocation scheme can redistribute the control signals to the remaining actuators. The effectiveness of the proposed algorithm is confirmed by numerical simulation.

A resource allocation problem, called the dynamic and stochastic knapsack problem (DSKP), is studied. A known quantity of resource is available, and demands for the resource arrive randomly over time. Each demand requires an amount of resource and has an associated reward. The resource requirements and rewards are unknown before arrival and become known at the time of the demand's arrival. Demands can be either accepted or rejected. If a demand is accepted, the associated reward is received; if a demand is rejected, a penalty is incurred. The problem can be stopped at any time, at which time a terminal value is received that depends on the quantity of resource remaining. A holding cost that depends on the amount of resource allocated is incurred until the process is stopped. The objective is to determine an optimal policy for accepting demands and for stopping that maximizes the expected value (rewards minus costs) accumulated. The DSKP is analyzed for both the infinite horizon and the finite horizon cases. It is shown that the DSKP has an optimal policy that consists of an easily computed threshold acceptance rule and an optimal stopping rule. A number of monotonicity and convexity properties are studied. This problem is motivated by the issues facing a manager of an LTL transportation operation regarding the acceptance of loads and the dispatching of a vehicle. It also has applications in many other areas, such as the scheduling of batch processors, the selling of assets, the selection of investment projects, and yield management.

Viability is the ability of a supply chain (SC) to maintain itself and survive in a changing environment through a redesign of structures and replanning of performance with long-term impacts. In this paper, we theorize a new notion—the viable supply chain (VSC). In our approach, viability is considered as an underlying SC property spanning three perspectives, i.e., agility, resilience, and sustainability. The principal ideas of the VSC model are adaptable structural SC designs for supply–demand allocations and, most importantly, establishment and control of adaptive mechanisms for transitions between the structural designs. Further, we demonstrate how the VSC components can be categorized across organizational, informational, process-functional, technological, and financial structures. Moreover, our study offers a VSC framework within an SC ecosystem. We discuss the relations between resilience and viability. Through the lens and guidance of dynamic systems theory, we illustrate the VSC model at the technical level. The VSC model can be of value for decision-makers to design SCs that can react adaptively to both positive changes (i.e., the agility angle) and be able to absorb negative disturbances, recover and survive during short-term disruptions and long-term, global shocks with societal and economical transformations (i.e., the resilience and sustainability angles). The VSC model can help firms in guiding their decisions on recovery and re-building of their SCs after global, long-term crises such as the COVID-19 pandemic. We emphasize that resilience is the central perspective in the VSC guaranteeing viability of the SCs of the future. Emerging directions in VSC research are discussed.

We propose a scalable, data-driven method for designing national policies for the allocation of deceased donor kidneys to patients on a waiting list in a fair and efficient way. We focus on policies that have the same form as the one currently used in the United States. In particular, we consider policies that are based on a point system that ranks patients according to some priority criteria, e.g., waiting time, medical urgency, etc., or a combination thereof. Rather than making specific assumptions about fairness principles or priority criteria, our method offers the designer the flexibility to select his desired criteria and fairness constraints from a broad class of allowable constraints. The method then designs a point system that is based on the selected priority criteria and approximately maximizes medical efficiency-i.e., life-year gains from transplant-while simultaneously enforcing selected fairness constraints. Among the several case studies we present employing our method, one case study designs a point system that has the same form, uses the same criteria, and satisfies the same fairness constraints as the point system that was recently proposed by U.S. policy makers. In addition, the point system we design delivers an 8% increase in extra life-year gains. We evaluate the performance of all policies under consideration using the same statistical and simulation tools and data as the U.S. policy makers use. Other case studies perform a sensitivity analysis (for instance, demonstrating that the increase in extra life-year gains by relaxing certain fairness constraints can be as high as 30%) and also pursue the design of policies targeted specifically at remedying criticisms leveled at the recent point system proposed by U.S. policy makers.

In this article, model predictive control is used to dynamically optimize an investment portfolio and control drawdowns. The control is based on multi-period forecasts of the mean and covariance of financial returns from a multivariate hidden Markov model with time-varying parameters. There are computational advantages to using model predictive control when estimates of future returns are updated every time new observations become available, because the optimal control actions are reconsidered anyway. Transaction and holding costs are discussed as a means to address estimation error and regularize the optimization problem. The proposed approach to multi-period portfolio selection is tested out of sample over two decades based on available market indices chosen to mimic the major liquid asset classes typically considered by institutional investors. By adjusting the risk aversion based on realized drawdown, it successfully controls drawdowns with little or no sacrifice of mean–variance efficiency. Using leverage it is possible to further increase the return without increasing the maximum drawdown.

Cognitive radio sensor networks are one of the kinds of application where cognitive techniques can be adopted and have many potential applications, challenges and future research trends. According to the research surveys, dynamic spectrum access is an important and necessary technology for future cognitive sensor networks. Traditional methods of dynamic spectrum access are based on spectrum holes and they have some drawbacks, such as low accessibility and high interruptibility, which negatively affect the transmission performance of the sensor networks. To address this problem, in this paper a new initialization mechanism is proposed to establish a communication link and set up a sensor network without adopting spectrum holes to convey control information. Specifically, firstly a transmission channel model for analyzing the maximum accessible capacity for three different polices in a fading environment is discussed. Secondly, a hybrid spectrum access algorithm based on a reinforcement learning model is proposed for the power allocation problem of both the transmission channel and the control channel. Finally, extensive simulations have been conducted and simulation results show that this new algorithm provides a significant improvement in terms of the tradeoff between the control channel reliability and the efficiency of the transmission channel.