Complex Constraints Research Articles

Research on optimal power flow of AC/DC hybrid system for civil more electric aircraft

Modern civil more electric aircraft often use AC/DC systems instead of AC systems to reduce the weight of the power system. Compared with the AC/DC system of the land power grid, the aircraft power system needs to pursue economic operation mode under more complex security constraints. This paper establishes the optimal power flow model of the AC/DC hybrid system of more electric aircraft, which takes minimizing the active power loss of the line as the optimization objective, including the steady-state operation constraints and security constraints of the AC/DC system. The interior point method and prediction correction interior point method are used to solve the model. Then, based on the simplified steady-state model of the B787 power system, the model and algorithm are verified by experiments. The experimental results show that by optimizing the power flow distribution of the power system, the power utilization efficiency of the system has been significantly improved, and its stability and reliability can also be better guaranteed.

Constraint tightening-based control for small-satellite formations using differential aerodynamic forces

Aerodynamic forces hold significant promise for controlling small-satellite formations in low Earth orbit. However, significant challenges still exist regarding complex input constraints and their application to multi-satellite formations. This paper proposes a constraint tightening-based control scheme for small-satellite formations, utilizing differential drag and lift as actuating forces. Two attitude adjustment strategies, namely the reorientation strategy and the single-radial rotation strategy, are compared in terms of their control effectiveness and applicability. Relative aerodynamic coefficients are employed as control inputs in view of their solid reachable region, followed by the development of a decoupling algorithm for determining pointing angles. Through a method that tightens the reachable region using liner models, the input constraints are linearized and can be explicitly expressed. Subsequently, an observer-based Model Predictive Control (MPC) algorithm is formulated to meet these linearized input constraints and provide feedforward compensation for system disturbances. This discrete algorithm enables practical onboard implementations owing to its low computational complexity. Additionally, a concept of overall computation for all satellites’ attitudes is proposed to adapt the control scheme for multi-satellite formations. Hardware-in-the-loop simulations are conducted to evaluate the control scheme's performance in along-track and circular formations, accounting for uncertainties in aerodynamic forces and attitude dynamics.

Accelerated Distributed Nesterov Optimization Subject to Complex Constraints and Its Applications

Accelerated Distributed Nesterov Optimization Subject to Complex Constraints and Its Applications

A new measurement method of volumetric frost heave deformation of freezing soil under complex pressure and constraint conditions

Preventing and controlling frost heave deformation is critical to artificial freezing construction. Compared to road engineering, the soil experienced complex pressure and constraint conditions during the freezing process. Therefore, this study introduces a new method for measuring the volumetric frost heave coefficient (VFHC) of freezing soil under complex pressure and constraint conditions. Series experiments were performed to validate the method and explore the pressure effects. Experimental results demonstrate that this method consistently produces stable and well-reasoned outcomes, providing valuable reference for the development of specialized three-dimensional frost heave testing apparatus in subsequent research. Furthermore, this research reveals that pressure and constraint conditions have a significant influence on the volumetric frost heave deformation of soil. An empirical equation that describes the variation of the VFHC with moisture content, considering the pressure and constraint effects, was proposed. These findings have significant implications for underground engineering employing the artificial ground freezing method.

Multi-body dynamical modeling and prediction of flexible origami/kirigami structures by affine transformation

A generalized multi-body dynamical modeling method of Origami/Kirigami structures considering the elastic–flexible coupling of surfaces and nonlinear creases is proposed. For different structures with complex spatial configuration and constraints, an Affine Transformation (AT) technique based on Absolute Nodal Coordinate Formulation (ANCF) is proposed to make the modeling more convenient and efficient. The surfaces are discretized by triangle reduced elements, and the constitutive relations of creases are described by nonlinear state-dependent torque constraints. The proof of the topological invariance of configurations is also given. The simulation of the folding capacity and dynamic behaviors on two Origami structures, Origami solar array mechanism, and enclosed Yoshimura Origami, are given, which verifies the effectiveness of the modeling method. Additionally, the quantitative dynamic behaviors contributes to the design principle of a Kirigami structure for multi-stability. Comparisons between theoretical results from modeling and experiments demonstrate the effectiveness of the modeling method and crease design. The AT-ANCF lays a theoretical foundation for dynamic prediction and design for large deformation Origami/ Kirigami structures, which has significant potential applications in dynamic properties design in the fields of aerospace, robotic etc.

Enhanced multi-strategy bottlenose dolphin optimizer for UAVs path planning

The path planning of unmanned aerial vehicle is a complex practical optimization problem, which is an important part of unmanned aerial vehicle technology. For constrained path planning problem, the traditional path planning methods can not deal with the complex constraint conditions well, and the classical nature-inspired algorithms will find the local optimal solution due to the lack of optimization ability. In this paper, an enhanced multi-strategy bottlenose dolphin optimizer is proposed to solve the unmanned aerial vehicle path planning problem under threat environments. Firstly, the introduction of fish aggregating device strategy that simulates the living habits of sharks enriches the behavioral diversity of the population. Secondly, random mixed mutation strategy and chaotic opposition-based learning strategy expand the exploration range of the algorithm in the solution space by disturbing the positions of some individuals and generating the opposite population respectively. Finally, after balancing the exploration and exploitation ability of the algorithm more reasonably through the mutation factor and energy factor, this paper proposes a new swarm intelligence algorithm. After verifying the adaptability and efficiency of the proposed algorithm through different types of test functions, this paper further highlights the advantages of the proposed algorithm in finding the optimal feasible path in the unmanned aerial vehicle path planning model based on four constraints.

HP3O algorithm-based all electric ship energy management strategy integrating demand-side adjustment

To tackle the energy management challenge that integrates power generation scheduling and demand-side adjustment for all-electric ship in uncertain marine environment, a hybrid penalized proximal policy optimization algorithm (HP3O)-based energy management strategy is proposed. First, demand-side adjustment, which involves adjusting the power of the ship's electric propulsion motors and flexible service loads, is integrated into the energy management problem. Second, HP3O algorithm is employed to obtain both continuous and discrete variables simultaneously. It utilizes a continuous actor network to obtain continuous variables, such as the generator's power and ship cruising speed, while employing a discrete actor network to determine discrete variables, i.e., the on/off status of the generators. Third, to handle complex constraints reasonably, the energy management problem is formulated as a constrained Markov decision process (CMDP), and an action mask mechanism is also integrated into the energy management framework to make agent's actions more reliable. The simulation results of an all-electric cruise ship validate the effectiveness and superiority of the proposed strategy in achieving near-optimal scheduling while satisfying operation constraints. Furthermore, a case study on a hybrid diesel-electric ferry confirms its generalization performance.

Multiphase Autonomous Docking via Model-Based and Hierarchical Reinforcement Learning

With the rise of traffic around Earth’s orbit, spacecraft mission designs have placed an unprecedented demand on the capabilities of autonomous systems. In the early 2000s, the state-of-the-art autonomous spacecraft controllers were designed for static and uncluttered environments. A little over a decade later, the challenges facing spacecraft autonomy now include cluttered, dynamic environments with time-varying constraints, logical modes, fault tolerances, uncertain dynamics, and complex maneuvers. With this rise in complexity, many areas of research have been investigating more experimental control strategies, such as reinforcement learning (RL), as a potential solution to this problem. The research presented herein aims to expand on efforts to quantify the use of RL in autonomous rendezvous, proximity operations, and docking (ARPOD) environments, with consideration to the inherent drawbacks of the more common algorithms present in the field. We present hierarchical model-based RL as a solution to an autonomous docking problem. This algorithm can learn satellite parameters, extrapolate trajectory information, and learn uncertain dynamics via data collection. By using gradient-free model predictive control logic, the algorithm can handle nondifferentiable objectives and complex constraints. Lastly, the hierarchical structure demonstrates an ability to generate feasible trajectories in the presence of integrated third-party subcontrollers commonly found in spacecraft. This study highlights the ability of the hierarchical algorithm to combine and manipulate third-party subpolicies to achieve trajectories not previously trained on.

A closed-form solution for evaluating the Findley critical plane factor

The fatigue assessment of structural components is a significant topic investigated both in the academia and industry. Despite the significant progress in comprehension over the past few decades, fatigue damage remains a significant challenge, often leading to unexpected component failures. One commonly used approach for fatigue assessment is the critical plane analysis, which aids in identifying the critical location and early crack propagation direction in a component. However, the conventional method for calculating critical plane factors is computationally demanding and is typically utilized only when the critical regions of the component are already known. In situations where the critical areas are difficult to be identified due to complex geometry, loads, or constraints, a more efficient method is required for evaluating critical plane factors. This research paper introduces an analytical algorithm to efficiently evaluates the widely used Findley critical plane factor. The algorithm operates within the framework of linear-elastic material behavior and proportional loading conditions, relying on tensor invariants and coordinate transformation laws. The algorithm has been tested on different component geometries, including a box-welded joint and a tubular specimen, subjected to proportional loading conditions such as tension, torsion, and a combination of them. The analytical method allowed a significant reduction in computation time while providing the exact solution of critical plane factor and critical plane orientations.

Structural and empirical knowledge driven multi-objective evolutionary algorithm for urban drainage system design

Urban drainage system plays an important role in urban rainwater management. The design of urban drainage system is challenging for its complex constraints and expensive individual evaluation. A kind of multi-objective evolutionary algorithm driven by both structural and empirical knowledge, called structural and empirical knowledge driven multi-objective evolutionary algorithm, is proposed in this paper. A novel tree-template-based encoding schema is proposed to extract the structural knowledge of urban drainage system. The population initialization method and genetic operators (i.e., crossover and mutation) are customized to handle the constraints among decision variables caused by the structure restriction of the urban drainage system. The radial basis function (RBF) model is employed to learn the empirical knowledge from the previous evolution process, and the RBF-based approximate evaluation is used to partially replace the expensive individual evaluation to improve the efficiency of the algorithm. Besides, by combining the decomposition idea and the constrained dominance principle, the local constrained dominance principle is suggested to handle the constraints in decision space, which can effectively improve the defect that the constrained dominance principle is easy to fall into local optimum. Comprehensive experiments on two urban drainage system cases show the superiority of the proposed algorithm for the optimal urban drainage system design.

It’s Showtime: Live-Streaming E-commerce and Optimal Promotion Insertion Policy

Live-streaming e-commerce has gained tremendous success as a new form of business model over the past few years. Nevertheless, there has been scarce research examining the effect of exogenous stimulus-driven factors (i.e., social cues such as promotion coupons) on the profit of the live-streaming platform. We model the continuous evolvement of the aggregate viewer involvement level with geometric Brownian motion and investigate the optimal timing and depth of promotion insertion policy. Our findings reveal that the optimal promotion insertion policy for the live-streaming room is a threshold policy that consists of a start-to-promote threshold and an involvement target when the trend of the aggregate involvement level is not large and the promotion cost is not high. Under some scenarios, the promotion planning process is constrained by various business rules, such as the promotion depth being limited by a discrete set (e.g., integral multiples of [Formula: see text]). Although this problem involves more complex constraints, we analytically show that a variation of the abovementioned threshold policy is the optimal strategy among all feasible policies, i.e., when the aggregate viewer involvement drops below a certain level ([Formula: see text]), the live-streaming host promotes with the depth of the least integral multiple of [Formula: see text] to raise the viewer involvement level above [Formula: see text]. Finally, to examine the effectiveness of our proposed policy, we compare it with prevalent industry practices. The result shows that the proposed promotion insertion policy outperforms prevailing ones significantly in generating profit for the live-streaming room.

DSAC-Configured Differential Evolution for Cloud–Edge–Device Collaborative Task Scheduling

Industrial Internet-of-things enables various manufacturing processes executed in distributed production lines and flexible workshops. With different cloud-edge-device collaboration ways, interconnected manufacturing tasks and computational tasks are cooperatively completed in manufacturing cells, cloud resources, and edge resources. Large-scale decision variables and complex precedence constraints make the scheduling problem intractable. To this end, this paper proposed a discretized soft actor-critic configured differential evolution algorithm to find a stable solution for the cloud-edge-device collaborative task scheduling problem. A mathematical model is established to describe the relationship between different tasks, the variables, the main constraints in collaboration, and the scheduling targets. A decentralized partially observable Markov decision process is modeled with five neural networks and three discretized loss functions to formulate the discretized soft actor-critic policy efficiently and enable it to find the best differential evolution configurations for different scheduling cases. Experimental analysis of four cloud-edge-device scheduling instances indicates that the proposed method trained in one case is adaptable to the other three cases. In the four cases, the proposed method reduces the total objective by 30.82% and 44.35% at most compared to five deep reinforcement learning-based differential evolution algorithms and seven typical evolutionary algorithms, respectively.

A Multipopulation Evolutionary Algorithm Using New Cooperative Mechanism for Solving Multiobjective Problems With Multiconstraint

In science and engineering, multi-objective optimization problems usually contain multiple complex constraints, which poses a significant challenge in obtaining the optimal solution. This paper aims to solve the challenges brought by multiple complex constraints. First, this paper analyzes the relationship between single constrained Pareto Front (SCPF) and their common Pareto Front sub-constrained Pareto Front (SubCPF). Next, we discussed the SCPF, SubCPF, and Unconstrainti Pareto Front (UPF)’s help to solve constraining Pareto Front (CPF). Then further discusses what kind of cooperation should be used between multiple populations constrained multi-objective optimization algorithm (CMOEA) to better deal with multi-constrained multi-objective optimization problems (mCMOPs). At the same time, based on the discussion in this paper, we propose a new multi-population CMOEA called MCCMO, which uses a new cooperation mechanism. MCCMO uses C+2 (C is the number of constraints) populations to find the UPF, SCPF, and SubCPF at an appropriate time. Furthermore, MCCMO uses the newly proposed Activation Dormancy Detection (ADD) to accelerate the optimization process and uses the proposed Combine Occasion Detection (COD) to find the appropriate time to find the SubCPF. The performance on 32 mCMOPs and real-world mCMOPs shows that our algorithm can obtain competitive solutions on MOPs with multiple constraints.

Multi-satellite cooperative scheduling method for large-scale tasks based on hybrid graph neural network and metaheuristic algorithm

Traditional heuristic optimization algorithms are no longer applicable to the multiple satellites scheduling with large-scale tasks, as they are unable to provide satisfactory performance in terms of convergence and speed when addressing complex constraint conflicts. Therefore, we propose a multi-satellite cooperative scheduling method for large-scale tasks based on a hybrid graph neural network (GNN) and metaheuristic algorithm. By using the representation and extraction capability of GNN for relations in graph, the features of large-scale tasks and their constraint relations were expressed, and the generalized knowledge was extracted. And combined with metaheuristic algorithm, the optimization framework for large-scale satellite task scheduling based knowledge and data was implemented. The method consists of a GNN pre-solver module for static constraint conflicts and a metaheuristic optimization module for dynamic constraint conflicts of satellite missions. In the GNN pre-solver module, the graph sample and aggregate network was used to learn and extract the common knowledge in the static task-conflict graph, and provided effective prior information for the metaheuristic optimization module. The quadratic unconstrained binary optimization loss function was designed with multiple influencing factors, to convert the discrete optimization function into a continuous function; a greedy threshold was also added to improve the task completion rate. Finally, numerical experimental results showed that the proposed method can achieve an efficient solution to the multi-satellite scheduling problem with tens of thousands tasks. Compared with the commonly used multi-satellite scheduling algorithms (the genetic algorithm (GA) and greedy-GA), the proposed method can obtain higher-quality solutions under the same conditions and greatly improve the computational efficiency of large-scale mission planning.

Hierarchical Control for Head-to-Head Autonomous Racing

We develop a hierarchical controller for head-to-head autonomous racing. We first introduce a formulation of a racing game with realistic safety and fairness rules. A high-level planner approximates the original formulation as a discrete game with simplified state, control, and dynamics to easily encode the complex safety and fairness rules and calculates a series of target waypoints. The low-level controller takes the resulting waypoints as a reference trajectory and computes high-resolution control inputs by solving an alternative approximation formulation with simplified objectives and constraints. We consider two approaches for the low-level planner, constructing two hierarchical controllers. One approach uses multi-agent reinforcement learning (MARL), and the other solves a linear-quadratic Nash game (LQNG) to produce control inputs. The controllers are compared against three baselines: an end-to-end MARL controller, a MARL controller tracking a fixed racing line, and an LQNG controller tracking a fixed racing line. Quantitative results show that the proposed hierarchical methods outperform their respective baseline methods in terms of head-tohead race wins and abiding by the rules. The hierarchical controller using MARL for low-level control consistently outperformed all other methods by winning over 90% of head-to-head races and more consistently adhered to the complex racing rules. Qualitatively, we observe the proposed controllers mimicking actions performed by expert human drivers such as shielding/blocking, overtaking, and long-term planning for delayed advantages. We show that hierarchical planning for game-theoretic reasoning produces competitive behavior even when challenged with complex rules and constraints.

Iterative Model Predictive Control for Automatic Carrier Landing of Carrier-Based Aircrafts Under Complex Surroundings and Constraints

Iterative Model Predictive Control for Automatic Carrier Landing of Carrier-Based Aircrafts Under Complex Surroundings and Constraints

Tree Search Reinforcement Learning for Two-Dimensional Cutting Stock Problem With Complex Constraints

Tree Search Reinforcement Learning for Two-Dimensional Cutting Stock Problem With Complex Constraints

Fast and Scalable ACL Policy Solving Under Complex Constraints With Graph Neural Networks

Fast and Scalable ACL Policy Solving Under Complex Constraints With Graph Neural Networks

Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity.

Zeroth-order (a.k.a, derivative-free) methods are a class of effective optimization methods for solving complex machine learning problems, where gradients of the objective functions are not available or computationally prohibitive. Recently, although many zeroth-order methods have been developed, these approaches still have two main drawbacks: 1) high function query complexity; 2) not being well suitable for solving the problems with complex penalties and constraints. To address these challenging drawbacks, in this paper, we propose a class of faster zeroth-order stochastic alternating direction method of multipliers (ADMM) methods (ZO-SPIDER-ADMM) to solve the nonconvex finite-sum problems with multiple nonsmooth penalties. Moreover, we prove that the ZO-SPIDER-ADMM methods can achieve a lower function query complexity of [Formula: see text] for finding an ϵ-stationary point, which improves the existing best nonconvex zeroth-order ADMM methods by a factor of [Formula: see text], where n and d denote the sample size and data dimension, respectively. At the same time, we propose a class of faster zeroth-order online ADMM methods (ZOO-ADMM+) to solve the nonconvex online problems with multiple nonsmooth penalties. We also prove that the proposed ZOO-ADMM+ methods achieve a lower function query complexity of [Formula: see text], which improves the existing best result by a factor of [Formula: see text]. Extensive experimental results on the structure adversarial attack on black-box deep neural networks demonstrate the efficiency of our new algorithms.

Effect of Parameters of Ditch Geometry on the Uniformity of Water Filling in Ship Lock Chambers

The design of ditch structures in ship locks has an important influence on reducing water flow energy, improving the uniformity of water filling in lock chambers, and reducing the force acting on mooring ships. Existing studies on the mechanisms of flow fields and mooring conditions under the influence of geometric structural changes in ditches are insufficient, and research is limited by the complexity of the problem. Based on OpenFOAM v8, a numerical simulation of the flow structure in a lock chamber was carried out. Taking into account the ditch width, ditch depth, and sill height of the side wall as the research variables, the influence of structural changes in four single ditches on the uniformity of water filling in the lock chamber is systematically discussed. The results show that under a complex boundary constraint, the flow is diffuse in the ditch and the lock chamber, and the filling of the lock chamber is not uniform. The uniformity of water filling is closely related to the arrangement of the ditch geometry. Through the comprehensive analysis of multiple factors, some parameter thresholds were obtained to provide theoretical guidance for ditch design. It is hoped that through improved design, allowing an increase in the uniformity of water filling, the lock chamber will reach a relatively optimal state.