Uncertainty Constraints Research Articles

Adaptive SOSM Control for Nonlinear Systems With Parametric Uncertainties and Time-Varying Asymmetric Output Constraints

Adaptive SOSM Control for Nonlinear Systems With Parametric Uncertainties and Time-Varying Asymmetric Output Constraints

Contingency selling in the presence of capacity constraints and customer heterogeneity

This paper examines an emerging pricing model, contingency selling, for markets where the product is sold in advance with attribute uncertainty and capacity constraint. A typical example is tournament ticket sales for sports events where it is uncertain whether a particular team will appear in the game. The model enables the firm to sell tickets in a contingent fashion, i.e., some tickets will be valid only if a certain team gets into the game. When consumers (fans) have sufficiently different preferences for different products (games with different teams), selling these “contingent tickets” better matches the price with the willingness-to-pay of particular consumer segments (fans of specific teams). Exploiting state-based price discrimination and belief heterogeneity, contingent tickets generate a higher margin per seat than the traditional way of selling general tickets, especially when there are supply constraints. We further show that contingency selling continues to be advantageous in the presence of secondary markets where consumers purchase tickets in advance but can trade tickets once they know which team will be in the game. We extend our model to examine the impacts of consumer optimism bias and fan base asymmetry. Finally, we compare contingency selling with alternative selling mechanisms to demonstrate the robustness of our results and illustrate the situations where this new selling model is more (or less) advantageous.

Edge-Intelligence-Powered Joint Computation Offloading and Unmanned Aerial Vehicle Trajectory Optimization Strategy

UAV-based air-ground integrated networks offer a significant benefit in terms of providing ubiquitous communications and computing services for Internet of Things (IoT) devices. With the empowerment of edge intelligence (EI) technology, they can efficiently deploy various intelligent IoT applications. However, the trajectory of UAVs can significantly affect the quality of service (QoS) and resource optimization decisions. Joint computation offloading and UAV trajectory optimization bring many challenges, including coupled decision variables, information uncertainty, and long-term queue delay constraints. Therefore, this paper introduces an air-ground integrated architecture with EI and proposes a TD3-based joint computation offloading and UAV trajectory optimization (TCOTO) algorithm. Specifically, we use the principle of the TD3 algorithm to transform the original problem into a cumulative reward maximization problem in deep reinforcement learning (DRL) to obtain the UAV trajectory and offloading strategy. Additionally, the Lyapunov framework is used to convert the original long-term optimization problem into a deterministic short-term time-slot problem to ensure the long-term stability of the UAV queue. Based on the simulation results, it can be concluded that our novel TD3-based algorithm effectively solves the joint computation offloading and UAV trajectory optimization problems. The proposed algorithm improves the performance of the system energy efficiency by 3.77%, 22.90%, and 67.62%, respectively, compared to the other three benchmark schemes.

Composite adaptive neural control for automatic carrier landing system with input saturation and output constraints

This paper investigates the automatic carrier landing control problem in the presence of model uncertainty, airwake disturbances, input saturation, and output constraints. Considering the performance requirements of the carrier-based aircraft, a composite adaptive neural controller is proposed based on the time-varying barrier Lyapunov function and backstepping control techniques. The radial basis function neural network is used to approximate the model uncertainty, where the neural network weight update law incorporating prediction and tracking errors further improves the convergence rate of the neural network and mitigates high-frequency oscillations. Furthermore, an adaptive disturbance compensation model is established to mitigate the adverse effects of airwake disturbances and estimation errors in the neural network. Based on the Lyapunov stability theory, it is proven that the proposed controller maintains the aircraft trajectory within the prescribed constraints and also ensures that all signals in the closed-loop control system are semiglobally uniformly ultimately bounded. Finally, comparative simulations are performed to demonstrate the effectiveness and superiority of the proposed composite adaptive neural control method.

Observer‐based adaptive control of vehicle platoon with uncertainty and input constraints

AbstractThis study focuses on the highway platoon driving mode and proposes a distributed adaptive control algorithm based on an observer. Firstly, the adaptive observer is designed to compensate for the effect of unknown driving resistance and thus enhance the adaptation ability of the system to uncertainty. Secondly, an auxiliary system is introduced to specifically address actuator saturation constraints, ensuring the stability of the platoon driving in extreme conditions. Lastly, combining an event‐triggered mechanism, a control strategy is designed to achieve the stability of the entire platoon while maximizing the conservation of communication resources. The algorithm's viability and efficiency are confirmed through simulation outcomes.

Optimizing multi-energy systems with enhanced robust planning for cost-effective and reliable operation

This paper introduces a comprehensive and resilient multi-energy system (MES) designed for independent planning and real-time implementation. A robust daily coordinated planning model is proposed, incorporating adjustable optimization with fundamental operational and uncertainty constraints. The model integrates various energy sources and systems, including photovoltaics, wind turbines, combined heat and power (CHP) units, energy storage system (ESS), electric vehicle (EV), electric boilers, and power-to-gas (P2G) facilities, to manage electricity, natural gas, and heat demands. The objective is to minimize MES operational costs while meeting electricity and heat requirements, considering renewable energy uncertainties. It includes the development of a two-stage flexible robust optimization model that accounts for energy equilibrium, capacity constraints, and demand response mechanisms. The model incorporates price-based demand response with both switchable and interruptible loads, enhancing system controllability and flexibility. Additionally, a scenario generation and reduction technique based on the Kantorovich distance is employed to effectively manage forecast errors and uncertainties. A novel modified Slime Mold Algorithm (SMA) is utilized to solve the optimization problem, demonstrating superior convergence and computational efficiency compared to traditional meta-heuristics. The slime mold algorithm is further enhanced with chaos theory, using a sine map to introduce dynamic exploration capabilities. The findings indicate that the proposed multi-energy system model effectively balances electricity, natural gas, and heat loads while accommodating renewable energy fluctuations. The enhanced slime mold algorithm provides optimal solutions swiftly, ensuring reliable and cost-effective multi-energy system operation.

Financing and operational strategies for supply chains with yield uncertainty and capital constraints

Facing yield uncertainty is a common phenomenon that corporations encounter in their production and operations processes, especially small and medium-sized enterprises (SMEs). It is a challenge for SMEs that also have capital constraints, and thus financing from supply chain members or commercial banks is the normal way to improve the efficiency of supply chain operations. We consider a supply chain finance system consisting of an SME, a core enterprise (CE), and a bank, in which the SME is capital-constrained and faces productivity yield uncertainty. Therefore, the SME may use two financing sources to support his production: financing or advance payment from the CE. In the presence of bankruptcy risks for the SME, we model their strategic interaction as a Stackelberg game with the CE as the leader. We design the framework for the order penalty contract and derive the optimal strategies that include the planned production quantity decision of the SME and the pricing decisions of the CE under the two financing sources. We find that from the SME’s perspective, he will always prefer advance payments from the CE to bank financing. Furthermore, we propose a framework for partial credit guarantees. When it is difficult for banks to evaluate the credit history of SMEs, CEs provide guarantees to banks for financing SMEs. The analytical methods and numerical results provide decision-making methods and suggestions for production planning of the SME and wholesale prices formulated by CE.

Robust secure resource optimization for active STAR-RIS systems

In this paper, we investigate the robust secure resource optimization for the active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) system under the imperfect eavesdroppers channel state information. Considering the fairness, a max–min secure rate optimization problem is formulated based on several practical constraints. To deal with the original non-convex problem, an alternative iteration algorithm is proposed. First, the original problem is decomposed into two non-convex sub-problems. Next, the continuous convex approximation and S-procedure techniques are applied to deal with the non-convex and uncertainty constraints, respectively. Then, the first-order Taylor expansion formula is utilized to approximate the convex difference form of the objective function. Finally, two sub-problems are transformed into convex ones and alternately solved until convergence. Simulation results show that the secure rate of the proposed scheme is higher than the conventional schemes.

Exploring young adults' experiences with food allergy during their teenage years: A practice research study.

Symptoms of anxiety, eating disorders and social isolation are prevalent among teenagers with food allergy compared to peers without. Treatment of teenagers with food allergy focus on preventing anaphylactic reactions, with little attention to promoting social and emotional well-being. The aim of the study was to explore young adults' perspectives on everyday life with food allergy during their teenage years to improve future clinical practice. Critical psychological practice research. During a 2-day camp the perspectives of 10 young adults (18-23 years) were explored through participant observation and informal interviews. Three follow up interviews were conducted. A co-researcher group discussed preliminary results, clinical challenges and ways forward. Being together with peers with food allergy was crucial, fostering belonging and normalisation. The shift in responsibility of managing the risk feels overwhelming and stressful during teen age. Self-understanding was influenced when managing food allergy in social contexts, inducing feelings of burden and isolation. Acceptance and understanding from social relations became important for all participants, and they all underlined desire for being viewed as individuals rather than being defined by their allergy. Support from other peers with food allergy is crucial for the participants. Transition to independently managing risks introduces uncertainty and social constraints, affecting self-understanding and interactions. Clinicians should prioritise peer support and empower teenagers in managing the risk and psychosocial challenges.

Primary Frequency Control of Multiple ON/OFF Load Clusters Considering Capacity Uncertainty and Lock-Time Constraint

Primary Frequency Control of Multiple ON/OFF Load Clusters Considering Capacity Uncertainty and Lock-Time Constraint

Active suspension hierarchical control with parameter uncertainty and external disturbance of electro-hydraulic actuators

In order to improve the ride comfort and handling stability of the electro-hydraulic active suspension system, a hierarchical control strategy is proposed. For the active suspension system with body mass uncertainty and safety constraints, an enhanced constraint adaptive backstepping controller is designed to generate the target force of body vertical motion. When dealing with constraints, nonlinear filters are introduced, backstepping technology and quadratic Lyapunov function are used to combine the control target and domain constraints, and the vertical displacement of the body and the suspension deflection control index are synthesized into a single controlled variable, so that the control variable converging to zero meets the suspension dynamic displacement limit. The ride comfort and safety of the active suspension system are improved. Secondly, stability analysis is conducted on the zero dynamic error system to ensure that all safety performance indicators are bounded. The effects of external disturbances, parameter uncertainties and modelling errors on the force tracking accuracy of asymmetric cylinder electrohydraulic systems are addressed. A backstepping dynamic surface control method based on a nonlinear perturbation observer is proposed, which estimates the composite perturbation terms such as modelling error and external perturbation, and uses the estimated value of the nonlinear observer to design a feedforward compensating control term to offset the effect of the composite perturbation on the system performance. In addition, the dynamic surface control method is used to calculate the derivative of the target force input, which avoids the derivative explosion phenomenon and reduces the computational complexity of the system. Simulation test results show that this method reduces the computational complexity of the system and improves the control performance. And under random and bumpy road conditions, the root-mean-square value of sprung mass acceleration performance index is reduced by 48.354 % and 72.677 % compared with passive suspension, which improves the ride comfort of the vehicle.

Risk assessment and the use of personal protective equipment in an emergency department: Differing perspectives of emergency and infection control clinicians. A video-vignette survey

The use of personal protective equipment (PPE) in emergency departments (EDs) is an important defense during infectious disease emergencies. However, what counts as appropriate PPE in EDs is contentious and inconsistently implemented in practice. An online scenario-based video survey was distributed through purposive sampling, and completed by 270 ED and infection prevention and control clinicians in Australia. A descriptive content analysis was performed on the data, and differences between groups were tested using Fisher exact test. Participants agreed that most items were required in both scenarios. Eye protection, mask use, and hand hygiene frequency were more contentious. Physicians were more likely than nurses, and ED clinicians more likely than infection prevention and control clinicians, to regard items or actions as optional rather than essential. Many ED clinicians, particularly physicians, regarded sequences as too time-consuming to be practical in a busy ED. Our findings likely reflect differences in professional roles, competing priorities, and risks, and highlight important contextual characteristics of EDs, such as diagnostic uncertainty, equipment inaccessibility, and resource constraints. To be feasible, practicable, and thereby effective, PPE guidance in the ED must be designed collaboratively with frontline ED staff, and reflects the complexities of their practice.

Formation Control of Autonomous Underwater Vehicles Using an Improved Nonlinear Backstepping Method

The characteristics of autonomous underwater vehicles include nonlinearity, strong coupling, multiple inputs and multiple outputs, uncertainty, strong disturbance, underdrive, and multiple constraints. Autonomous underwater vehicle cluster systems are associated with large-scale complex dynamic systems through local perception or network communication, which have the structural characteristics of “complex dynamic + association topology + interaction rules”. To solve the problem of formation trajectory tracking of underactuated autonomous underwater vehicles, a controller was designed on the basis of an improved nonlinear backstepping algorithm, cascade system theory, and the Lyapunov direct method. In this design, the formation is determined from the actual trajectory of the leader autonomous underwater vehicle. The formation control rate is determined using the backstepping method and Lyapunov theory. Nonlinear disturbance observers were added to ensure that the trajectory error of the formation control could be quickly reduced in a real case with interference. The stability and effectiveness of this method were verified through simulation experiments. The robustness of the control algorithm was verified using two simulation cases, and the simulation results show that the proposed control method can maintain the expected formation.

A search algorithm for the compact uncertainty region of system elements with interval and affine constraint properties

This study proposes a search algorithm for the compact uncertainty region of system elements with affine constraints and interval information. While existing studies have considered only the interval information of uncertainty, the proposed algorithm reflects both the interval and affine constraints of uncertain elements to reduce conservatism due to uncertainties. First, the affine constraints of uncertainty are plotted in an N-dimensional space, where N is the number of elements that should be considered. Due to the properties of the affine constraints, the affine constraints are expressed as an (N-1) dimensional structure. The interval information is then expressed as an N-dimensional structure. The algorithm iteratively checks the edges of the N-dimensional structure between two geometrical structures to determine the intersection region. The algorithm then collects the vertices of the convex polytope that overlap the two structures. To show the effectiveness of proposed algorithm, this study proposes a consensus criterion for a multi-agent time-delayed system with uncertain switching topologies as an application of a system with uncertainties in system elements.

Vision-based adaptive LT sliding mode admittance control for collaborative robots with actuator saturation

Abstract In this paper, we propose a novel vision-based adaptive leakage-type (LT) sliding mode admittance control for actuator-constrained collaborative robots to realize the synchronous control of the precise path following and compliant interaction force. Firstly, we develop a vision-admittance-based model to couple the visual feedback and force sensing in the image feature space so that a reference image feature trajectory can be obtained concerning the contact force command and predefined trajectory. Secondly, considering the system uncertainty, external disturbance, and torque constraints of collaborative robots in reality, we propose an adaptive sliding mode controller in the image feature space to perform precise trajectory tracking. This controller employs a leakage-type (LT) adaptive control law to reduce the side effects of system uncertainties without knowing the upper bound of system uncertainties. Moreover, an auxiliary dynamic is considered in this controller to overcome the joint torque constraints. Finally, we prove the convergence of the tracking error with the Lyapunov stability analysis and operate various semi-physical simulations compared to the conventional adaptive sliding mode and parallel vision/force controller to demonstrate the efficacy of the proposed controller. The simulation results show that compared with the controller mentioned above, the path following accuracy and interaction force control precision of the proposed controller increased by 50% and achieved faster convergence.

СТРАТЕГИЧЕСКИЕ ПОДХОДЫ К ОБЕСПЕЧЕНИЮ ПРОДОВОЛЬСТВЕННОЙ БЕЗОПАСНОСТИ В УСЛОВИЯХ НЕОПРЕДЕЛЕННОСТИ

The current global situation is characterized by a high degree of uncertainty and risk. Epidemics, natural disasters, economic crises and other factors can dramatically affect food security. Therefore, developing strategies that can adapt to changing conditions becomes an important task. The paper examines the issue of ensuring food security in conditions of uncertainty. The concept of food security strategy and the importance of food market infrastructure in achieving this goal are considered. There are two key approaches to ensuring food security: traditional and flexible. The characteristics and features of each of these strategies are analyzed in detail. Traditional strategy focuses on stability and predictability, while flexible strategy aims to adapt to changing conditions. Particular attention is paid to the proposed algorithm for selecting a food security strategy, which takes into account a number of factors, including the level of risk, degree of uncertainty, product availability and budget constraints. This algorithm provides a scientifically proven method for selecting the optimal strategy, taking into account the context and specifics of the situation. Research is that both approaches, traditional and flexible, have their own unique advantages and can be applied in different contexts depending on the level of uncertainty and degree of risk. The choice between them depends on the specific needs and characteristics of the country or region, allowing for effective management of food security in a changing environment. The author proposes a comprehensive approach to ensuring food security, including the development of strategies, methods and selection algorithms, which makes it an important contribution to the field of food security and risk management.

Co-design of adaptive event-triggered mechanism and controller for networked control systems with uncertainty and time-varying delay

ABSTRACT This paper investigates the co-design problem of event-triggered mechanisms and state feedback controllers for networked control systems with parameter uncertainty and network-induced delay. Firstly, to overcome the network bandwidth limitation, an adaptive event-triggered mechanism is used to filter the required sampling signals to reduce the network resource occupancy. Second, establish a unified model subject to event triggering mechanisms, parameter uncertainty, and network delay constraints, and derive the stability conditions of the system for the model by combining Lyapunov stability theory and the linear matrix inequality method. Then, the state feedback controller is designed to satisfy the event-triggering condition and certain robust performance requirements, and an optimisation method is proposed to compromise network resource consumption and control performance. Finally, the effectiveness of the proposed method is verified by a numerical example.

Chance-constrained co-optimization of peer-to-peer energy trading and distribution network operations

Peer-to-Peer (P2P) energy markets enable participants to trade locally produced electrical energy at a mutually agreed-upon price. Distributed Energy Resources (DERs) and flexible loads are fundamental components of P2P energy markets, facilitating the market participants to supply or consume energy in accordance with trading agreements. Consequently, it is vital to account for the stochastic nature of DERs and load demand while designing the P2P energy trading model in order to attain optimal operation of P2P energy markets and secure operation of the distribution network. The scenario-based and robust optimization techniques commonly used to characterize uncertainty suffer from data requirement, computational burden, and solution conservatism standpoints. This paper, therefore, proposes a chance-constrained model for P2P energy trading considering renewable energy uncertainty and distribution network constraints. The uncertain behaviour of renewable generation resources is modelled using chance constraints and integrated with the decision-making process of the P2P energy market and distribution network operation. The chance-constrained P2P energy trading problem is reformulated as a deterministic equivalent problem that can be solved by readily available commercial solvers. The ex-ante and ex-post-performance of the proposed chance-constrained formulation is evaluated on a 15-bus radial distribution network, while the scalability of the proposed formulation is demonstrated on modified IEEE 33-bus and 69-bus test networks. The results showcase the effectiveness of the proposed chance-constrained model in handling the uncertainty and respecting the network constraints while deriving the P2P energy trading outcomes.

Uncertainty Constraint on Headphone Secondary Path Function for Designing Cascade Biquad Feedback Controller with Improved Noise Reduction Performance

The uncertainty in the secondary path of active noise control (ANC) headphones affects the waterbed effect and stability of the feedback system. This study focuses on the uncertainty of the secondary path when real users wear headphones and proposes a new uncertainty constraint based on the measured results of the secondary path transfer function under different wearing conditions of a dummy head and limited subjects. This constraint and a cascaded second-order infinite impulse response filter with fixed coefficients are used to formulate a control strategic function, which is optimized using the Improved Grey Wolf Optimizer (IGWO) algorithm to obtain the optimal controller with better noise reduction performance. The proposed method and simulation model are validated based on the experimental test results. The results demonstrate that the safety factor and waterbed suppressing factor contained in the proposed uncertainty constraint ensure more stable noise reduction and effective suppression of the waterbed effect for new subjects without a priori data.

Robust Short-Term Operation of AC Power Network With Injection Uncertainties

With uncertain injections from Renewable Energy Sources (RESs) and loads, deterministic AC Optimal Power Flow (OPF) often fails to provide optimal setpoints of conventional generators. A computationally time-efficient, economical, and robust solution is essential for ACOPF with short-term injection uncertainties. Usually, applying Robust Optimization (RO) for conventional non-linear ACOPF results in computationally intractable Robust Counterpart (RC), which is undesirable as ACOPF is an operational problem. Hence, this paper proposes a single-stage non-integer non-recursive RC of ACOPF, using a dual transformation, for short-term injection uncertainties. The proposed RC is convex, tractable, and provides base-point active power generations and terminal voltage magnitudes (setpoints) of conventional generators that satisfy all constraints for all realizations of defined injection uncertainties. The non-linear impact of uncertainties on other variables is inherently modeled without using any affine policy. The proposed approach also includes the budget of uncertainty constraints for low conservatism of the obtained setpoints. Monte-Carlo Simulation (MCS) based participation factored AC power flows validate the robustness of the obtained setpoints on NESTA and case9241pegase systems for different injection uncertainties. Comparison with previous approaches indicates the efficacy of the proposed approach in terms of low operational cost and computation time.