Hierarchical Optimization Research Articles

Non-Weighted Two-Stage Model Predictive Control Strategy Based on Three-Level NPC Inverter

This paper investigates the asynchronous motors driven by a Three-Level Neutral-Point-Clamped Voltage Source Inverter (3L-NPC-VSI) and aims to achieve control without weight factors and reduce torque ripple. It puts forward a non-weighted two-stage Finite-Control-Set Model Predictive Control (FCS-MPC) strategy. First, a hierarchical optimization method is adopted to address the difficulty of setting weight factors in traditional FCS-MPC applications. The method offers stratified designs of three performance indices, voltage jump, common-mode voltage, and current tracking, obviating the need for weight factor setting and reducing the calculation load of predictions. Secondly, to further mitigate torque ripple, an optimal vector or vector combination is implemented at the current control layer by adhering to the principle of minimal current tracking error. During the selection of the optimal vector combination, the first vector of the combination is chosen to be the vector at the end of the present cycle. This ensures that there is at most one switch within each control period, reducing the switching losses of the two-stage FCS-MPC. Lastly, detailed simulation and experimental analyses are conducted to verify the feasibility and effectiveness of the proposed strategy.

Hierarchical Framework for Space Exploration Campaign Schedule Optimization

Space exploration plans are becoming increasingly complex as public agencies and private companies target deep-space locations, such as cislunar space and beyond, which require long-duration missions and many supporting systems and payloads. Optimizing multimission exploration campaigns is challenging due to the large number of required launches as well as their sequencing and compatibility requirements, making conventional space logistics formulations unscalable. To tackle this challenge, this paper proposes an alternative approach that leverages a two-level hierarchical optimization algorithm: an evolutionary algorithm is used to explore the campaign scheduling solution space, and each of the solutions is then evaluated using a time-expanded multicommodity flow mixed-integer linear program. A number of case studies, focusing on the Artemis lunar exploration program, demonstrate how the method can be used to analyze potential campaign architectures. The method enables a potential mission planner to study the sensitivity of a campaign to program-level parameters such as logistics vehicle availability and performance, payload launch windows, and in situ resource utilization infrastructure efficiency.

Multi‐objective hierarchical optimisation design and experimental verification of an alterable‐magnetic‐circuit variable‐flux memory machine

AbstractRare‐earth permanent magnet synchronous machines face challenges in manipulating their magnetic fields, which hinders the ability to extend the operation speed range. Moreover, this inflexibility leads to reduced efficiency in high‐speed scenarios when the machine is under flux‐weakening control and increases the risk of the magnets becoming demagnetised. The authors propose an alterable‐magnetic‐circuit variable‐flux memory machine (AMC‐VFMM) and a multi‐objective hierarchical optimisation method is conducted to optimise the machine. Firstly, the topology and alterable‐magnetic‐circuit principle of the proposed AMC‐VFMM are introduced. Then, optimisation objectives including torque production capability, flux regulation capability, and resisting unintentional demagnetisation capability are defined, and the hierarchical optimisation approach is established by stratifying the optimisation objectives and variables through the sensitivity analysis. Finite element analysis indicates that electromagnetic performances of the optimised design scheme are significantly enhanced. The bench test of the prototype demonstrates the superiority of the proposed AMC‐VFMM and validates the effectiveness of the optimisation design method.

Collaborative hierarchical scheduling model of interconnected multi-microgrid and ADN considering DR with different strategies

The demand response (DR) resources of multiple entities have different operation strategies, which makes it difficult to realize the co-optimization. This paper proposes a cooperative hierarchical scheduling optimization model of interconnected multi-microgrid and an active distribution network (ADN) considering DR with different operation strategies. First, the collaborative hierarchical scheduling framework is proposed and the interaction characteristics and cooperative scheduling mode of multi-interconnected microgrid and ADN are analyzed. Second, the two-layer cooperative hierarchical scheduling model is established, considering DR with different strategies. The upper layer model takes the minimum operating cost of the ADN as the objective to optimize the trading tariff between the ADN and microgrids, and its own DR, while the lower layer model takes the operating cost of multi-microgrid as the objective to optimize the purchasing and selling of electrical energy for the ADN, the DR, and the interaction power. Finally, the case studies with three microgrids and an ADN are used to demonstrate the validity and effectiveness of the proposed model.

Two-player nonlinear Stackelberg differential game via off-policy integral reinforcement learning

For the nonzero-sum games, players have taken different strategies to achieve the Nash equilibrium. However, regarding hierarchical optimization and asymmetric information, Nash equilibrium is ineffective. The Stackelberg game provides us with a leader–follower strategy to cope with this problem. This paper solved two-player continuous-time nonlinear Stackelberg differential games with unknown system dynamics using an off-policy integral reinforcement learning (IRL) technique. A two-level hierarchy optimal control problem is the Stackelberg differential game, and locating the open-loop Stackelberg equilibrium is equivalent to solving the coupled Hamiltonian-Jacobi (HJ) equations. First, the optimal strategies for the leader and the follower are derived. Then, the closed-loop system’s asymptotic stability of optimal control solution is proved. Based on the policy iteration (PI) algorithm, an off-policy IRL algorithm is developed to solve the Stackelberg game for completely unknown system dynamics. The control strategies and cost functions of the leader and follower are approximated using neural networks (NNs). It is demonstrated that NNs weight errors of off-policy algorithm are uniformly ultimately bounded (UUB). Lastly, two simulation examples demonstrate that the proposed learning algorithm is capable of obtaining two-player Stackelberg equilibrium strategies.

The role of regional renewable energy integration in electricity decarbonization—A case study of Japan

Decarbonization strategies depend critically on the integration of region-specific renewable energy and social acceptance, yet their roles still need to be clarified. This study designs a regional-integrated electricity system and assesses the impact of regional decisions associated with renewable energy on cost-effective national decarbonization, using Japan as a case study owing to its active engagement in regional decarbonization. A high spatial resolution renewable electricity system model that explicitly considers all 1741 Japanese municipalities and employs a hierarchical optimization approach is developed. Based on the model, the opportunity cost of limiting renewable energy development is quantified for 30 representative municipalities. The results highlight that restrictions on municipal renewable energy can increase annual system costs by hundreds of millions of euros and national electricity costs by up to 0.4%, although it varies by region and energy source. This suggests that widespread opposition to renewables could be a significant barrier to cost-effective decarbonization. However, the impact of solar photovoltaic (PV) tends to be slight. In conclusion, investments in social acceptance of region-specific renewables can minimize opportunity losses and reduce system costs. Nonetheless, relocating the project to another region is possibly more cost-effective for solar PV.

Optimal Sustainable Manufacturing for Product Family Architecture in Intelligent Manufacturing: A Hierarchical Joint Optimization Approach

As consumers and governments prioritize cost-effectiveness and ecological sustainability, the limitations of traditional manufacturing paradigms become apparent in the context of constrained resources. The adverse effects of these paradigms on the environment and society hinder the achievement of a sustainable product life cycle. Intelligent manufacturing processes offer a solution by efficiently gathering meaningful data, such as usage and product recycling information, from previous product generations to enhance product design and subsequent sustainable manufacturing processes (SMPs). Modular product family architecture (PFA) design holds promise in promoting product sustainability and addressing diverse consumer needs. PFA design and SMPs are inherently interconnected within intelligent manufacturing frameworks. This paper aims to integrate the decision-making processes underlying PFA with SMPs. We model integrated PFA and SMP decisions as a Stackelberg game, which involves hierarchical joint optimization (HJO) for assessing product modularity and sustainable manufacturing fulfillment. We develop a bilevel 0–1 integer nonlinear programming model to represent the HJO decision-making process and propose a nested genetic algorithm (NGA) to solve the HJO problem. A case study with a laptop is conducted to validate the feasibility and potential of the proposed HJO model for joint optimization problems in PFA design and SMPs.

Stress-induced failure of embodied cognition: A general model

We derive the classic, ubiquitous, but enigmatic Yerkes–Dodson effect of applied stress on real-world performance in a highly natural manner from fundamental assumptions on cognition and its dynamics, as constrained by the asymptotic limit theorems of information and control theories. We greatly extend the basic approach by showing how differences in an underlying probability model can affect the dynamics of decision across a broad range of cognitive enterprise. Most particularly, however, this development may help inform our understanding of the different expressions of human psychopathology. A ‘thin tailed’ underlying distribution appears to characterize expression of ‘ordinary’ situational depression/anxiety symptoms of conditions like burnout induced by toxic stress. A ‘fat tailed’ underlying distribution appears to be associated with brain structure and function abnormalities leading to serious mental illness and poor decision making where symptoms are not only emerging in the setting of severe stress but may also appear in a highly punctuated manner at relatively lower levels of stress. A simple hierarchical optimization shows how environmental ‘shadow price’ constraints can buffer or aggravate the effects of stress and arousal. Extension of the underlying theory to other patterns of pathology, like immune disorders and premature aging, seems apt. Applications to the punctuated dynamics of institutional cognition under stress also appear possible. Ultimately, the probability models studied here can be converted to new statistical tools for the analysis of observational and experimental data.

Cascade Antidisturbance Control of Hydraulically Driven Bipedal Robots for High Dynamic Locomotion by Using Model Prediction and Task Hierarchical Optimization

Cascade Antidisturbance Control of Hydraulically Driven Bipedal Robots for High Dynamic Locomotion by Using Model Prediction and Task Hierarchical Optimization

Life-cycle integrity design method considering load robustness of offshore oil and gas equipment: SCSSV as a case study

Components that are vulnerable to the external environment and harsh working conditions easily fail during their life cycle under variable loads of offshore oil and gas equipment. The reliability of the system in the life cycle is reduced, various risks are increased, and substantial maintenance costs are generated. However, the current reliability-based design optimization method has difficulty meeting the situation that the life cycle is taken as the time dimension of design considering reliability, risk, and maintenance. To solve the problem, a life cycle integrity design optimization method considering robustness is proposed. A simplified integrity model is established through random load sampling and global sensitivity analysis. A multiobjective particle swam hierarchical optimization algorithm is proposed, in which hierarchical optimization order is determined by the priority of objectives, and the optimization design with structural integrity as the optimization objective is completed. The method is applied in the design of pressure pipeline of all-electric surface-controlled subsurface safety valve system.

Facial augmented reality based on hierarchical optimization of similarity aspect graph

BackgroundThe existing face matching method requires a point cloud to be drawn on the real face for registration, which results in low registration accuracy due to the irregular deformation of the patient's skin that makes the point cloud have many outlier points. MethodsThis work proposes a non-contact pose estimation method based on similarity aspect graph hierarchical optimization. The proposed method constructs a distance-weighted and triangular-constrained similarity measure to describe the similarity between views by automatically identifying the 2D and 3D feature points of the face. A mutual similarity clustering method is proposed to construct a hierarchical aspect graph with 3D pose as nodes. A Monte Carlo tree search strategy is used to search the hierarchical aspect graph for determining the optimal pose of the facial 3D model, so as to realize the accurate registration of the facial 3D model and the real face. ResultsThe proposed method was used to conduct accuracy verification experiments on the phantoms and volunteers, which were compared with four advanced pose calibration methods. The proposed method obtained average fusion errors of 1.13 ± 0.20 mm and 0.92 ± 0.08 mm in head phantom and volunteer experiments, respectively, which exhibits the best fusion performance among all comparison methods. ConclusionsOur experiments proved the effectiveness of the proposed pose estimation method in facial augmented reality.

Hierarchical Velocity Optimization for Connected Automated Vehicles With Cellular Vehicle-to-Everything Communication at Continuous Signalized Intersections

The rapid development of intelligent connected technologies and cellular vehicle-to-everything communication (C-V2X) provide new opportunities to solve the connected automated vehicle (CAV) traffic problem for eco-driving at continuous signalized intersections. With C-V2X, a hierarchical velocity optimization design based on hybrid model predictive control technique (HVO-HMPC) is presented to reduce the fuel consumption and pollution emission. First, a distance–domain velocity optimization problem, with distance as the independent variable, was constructed. Second, a hybrid MPC scheme was developed by combining the multiple shooting method and MPC technique to calculate the optimal velocity profile of a high-level controller, which acts as the reference velocity in a low-level controller. Then, a car-following model was built, the low-level controller tracked the reference velocity with the predictive control as the backbone, and the optimal velocity was calculated while ensuring that the safety velocity constraint is satisfied. Next, the proposed HVO-HMPC was tested in Prescan, and the effect comparisons with different control methods in terms of fuel consumption, pollution emission, braking time, and number of braking applications were studied under different driving scenarios. Results show that once the maximal speed is limited to 40 km/h under short-period signals and 20 km/h under long-period signals, the HVO-HMPC effectively reduces fuel consumption by 27.21%, 25.89%, and pollution emissions by 25.3%, 25.97%, respectively, while achieving best performance. Finally, an experimental prototype is built to confirm the validity of the HVO-HMPC.

A Scan-Based Hierarchical Heuristic Optimization Algorithm for PCB Assembly Process

A Scan-Based Hierarchical Heuristic Optimization Algorithm for PCB Assembly Process

Hierarchical Optimization Scheduling Algorithm for Logistics Transport Vehicles Based on Multi-Agent Reinforcement Learning

Hierarchical Optimization Scheduling Algorithm for Logistics Transport Vehicles Based on Multi-Agent Reinforcement Learning

Toward coordinated planning and hierarchical optimization control for highly redundant mobile manipulator

This paper represents a constraint planning and optimization control scheme for a highly redundant mobile manipulator considering a complex indoor environment. Compared with the traditional optimization solution of a redundant manipulator, infinity norm and slack variable are additionally introduced and leveraged by the optimization algorithm. The former takes into account the joint limits effectively by considering individual joint velocities and the latter relaxes the equality constraint by decreasing the infeasible solution area. By using derived kinematic equations, the tracking control problem is expressed as an optimization problem and converted into a new quadratic programming (QP) problem. To address the optimization problem, the two-timescale recurrent neural networks optimization scheme is proposed and tested with a 9 DOFs nonholonomic mobile-based manipulator. Additionally, the BI2RRT∗ path-planning algorithm incorporates path planning in the complex environment where different obstacles are positioned. To test and evaluate the proposed optimization scheme, both predefined and generated paths are tested in the Neurorobotics Platform (NRP) 22https://neurorobotics.net.which is open access and open source integrative simulation framework powered by Gazebo and developed by our team.

Mine water cooperative optimal scheduling based on improved genetic algorithm

This article addresses the issues of unreasonable water scheduling and high costs in coal mine shafts, proposing a hierarchical optimization scheduling strategy. Taking the water quality and quantity of a certain mining area in Inner Mongolia as the research object, it designs the objective function with the highest reuse efficiency and the lowest reuse cost of mine water resources, and establishes the constraint conditions of water quality and quantity for each water-using unit. In response to the problem that traditional genetic algorithms are prone to local optima, an adaptive autobiographical operator is proposed and improved based on Metropolis principle of simulated annealing algorithm. The improved algorithm is applied to the calculation of the scheduling model, and the results show that the recovery cost in the heating season is reduced by 66779.36 CNY/month, a decrease of 10.34%; the recovery cost in the non-heating season is reduced by 61469.28 CNY/month, a decrease of 9.91%. At the same time, the heating season and the non-heating season have reduced by 136.99 h/month and 154.52 h/month respectively, significantly reducing the recovery cost and time.

Bounded scheduling two-component jobs simultaneously or hierarchically

This paper studies the multi-objective problem of scheduling n two-component jobs on a fabrication machine with bounded batch capacity in terms of the number of jobs. Each job consists of two components, one is common among all jobs (called standard component) and the other is unique to itself (called specific component). The machine processes all components so that it might become a capacitated bottleneck machine. Standard components have equal processing times. They are fabricated in batches sequentially on the machine, and a setup time is required to fabricate a batch. Each batch contains the standard components of at most b(b < n) jobs. Specific components are manufactured individually, therefore their associated setup times could be absorbed into their processing times. Standard components in a batch are not available until all these components are completed (meaning that the processing time of a batch is equal to the total processing time of the standard components in it), whereas a specific component is available on completion of its processing. A job is completed when both its two components are completed and available. For simultaneous optimization of makespan and maximum lateness, an O(n3)-time algorithm is obtained. Through a careful use of the heap data structure, the time bound can be improved to O(n2 log n). For hierarchical optimization of two maximum lateness objectives and makespan, an O(n5)-time algorithm is obtained, which can be improved to run in time O(n4).

A hierarchical optimization approach for industrial task offloading and resource allocation in edge computing systems

A hierarchical optimization approach for industrial task offloading and resource allocation in edge computing systems

Control parameter optimization of underwater gliders with uncertainty using hierarchical optimization method and interval analysis theory

This article attempts to solve the engineering optimization problem for control parameter configuration of underwater gliders with parameter uncertainty. The optimization objectives are maximizing the glider energy utilization rate and average voyage velocity; design variables include the net buoyancy adjustment amount and movable mass block translation amount; and uncertain parameters include the glider control parameter errors, manufacturing errors and sensor measurement errors. First, a dynamic simulation-based performance evaluation model of the glider is established, and interval number order theory is used to quantify the uncertainty. Then, surrogate models of the performance evaluation model are established, and the Sobol’ method-based sensitivity analysis is executed to identify the key uncertain parameters. Thus, a hierarchical optimization framework is introduced, and its system level and sub-level are used for determining optimal control parameter values and performance evaluation parameter intervals, respectively. Finally, optimization calculation is executed under different parameter configurations, and some rules are summarized.

Octree-based hierarchical sampling optimization for the volumetric super-resolution of scientific data

When introducing physics-constrained deep learning solutions to the volumetric super-resolution of scientific data, the training is challenging to converge and always time-consuming. We propose a new hierarchical sampling method based on octree to solve these difficulties. In our approach, scientific data is preprocessed before training, and a hierarchical octree-based data structure is built to guide sampling on the latent context grid. Each leaf node in the octree corresponds to an indivisible subblock of the volumetric data. The dimensions of the subblocks are different, making the number of sample points in each randomly cropped training data block to be adaptive. We reconstruct the octree at intervals according to loss distribution to perform the multi-stage training. With the Rayleigh-Bénard convection problem, we deploy our method to state-of-the-art models. We constructed adequate experiments to evaluate the training performance and model accuracy of our method. Experiments indicate that our sampling optimization improves the convergence performance of physics-constrained deep learning super-resolution solutions. Furthermore, the sample points and training time are significantly reduced with no drop in model accuracy. We also test our method in training tasks of other deep neural networks, and the results show our sampling optimization has extensive effectiveness and applicability. The code is publicly available at https://github.com/xinjiewang/octree-based_sampling.