Cost Function Research Articles

Assessment of an auto-routing method for topology generation of aircraft power supply systems

AbstractDuring early phases of aircraft development, overall systems design is performed to estimate relevant system design parameters such as mass, power consumption, and compliance with architectural requirements (redundancy). The GeneSys software framework has been developed for parametric modeling of aircraft on-board systems for overall systems design. To this end, model-based architecture definition, topology generation (components positioning and connections routing), and system sizing are conducted. The topology generation is currently performed using design templates containing knowledge-based heuristics derived from systems topologies of existing aircraft. However, using such design templates limits their generic application to novel aircraft models because exceptions for geometric differences between these models need to be implemented. This especially applies to the topology generation of power supply systems (electric, hydraulic) due to their high number of interfaces to consumer systems. To overcome these limitations, an automated routing method is presented in this paper as a generic approach for topology generation. With this method, the system topology is generated based on a defined routing network, which comprises areas of the aircraft for allowed components positioning and connections routing. Using Dijkstra’s algorithm, the shortest path connections are found within the established routing network. Furthermore, boundary conditions for routing, for instance adding a cost function for power segregation, can be defined. An assessment of the automated routing method applied on the electric power supply system is performed using qualitative parameters such as the capability to adapt the routing network. Quantitative parameters like cable length and system mass are assessed as well.

An improved model predictive control for linear induction machine drive-based split-source inverters

The Split Source Inverter (SSI) is a single-stage DC-AC converter topology widely recognized for its beneficial features compared to the Z-source inverter. These advantages include a reduced number of components, a constant input current, and minimized losses. This study aims to extensively investigate and analyze the operation of a 3 kW Linear Induction Motor (LIM) using SSI through comprehensive simulations. The suggested control approach, employing finite control-model predictive thrust control (FC-MPTC), exhibits remarkable dynamic behavior and achieves a fast transient response without necessitating modifications to the control loop. Moreover, the proposed method allows for the manipulation of multiple variables by utilizing a single cost function, eliminating the need for lookup tables. The efficacy of the proposed control method is validated through comprehensive simulations and experimental tests, which exhibit accurate reference tracking speed and superior dynamic performance in regulating the thrust and primary flux. The results also showed that the proposed method had lower undulations of 5.8% than the traditional method. The proposed approach outperforms the DTC-SVM method under varying load and speed conditions by delivering remarkable accuracy and improved control characteristics.

Acoustic 2-D Full-Waveform Inversion with Non-Balanced Finite differences and Adaptive Weight Decay Methods

Full waveform inversion (FWI) is a powerful seismic imaging technique that aims to achieve high-resolution subsurface models by iteratively comparing recorded seismic waveforms with modeled waveforms. The success of FWI relies on the precision of forward modeling algorithms and the effectiveness of optimization strategies. In this study, we propose a FWI methodology that leverages non-balanced staggered-grid finite differences schemes (NBFD) in the forward modeling process. By employing operators with spatial differencing of varying lengths, these schemes enhance the efficiency of forward modeling.For the optimization process, we employ adaptive gradient-based optimization with weight decay, a widely-used approach in machine learning and deep learning algorithms for model training. These methods demonstrate efficient optimization processing for large data volumes with high precision. The objective of adaptive gradient-based optimization is to dynamically adjust the update rate model parameters during the iterative process. This facilitates improved convergence speed, enhances stability, and overcomes challenges associated with traditional fixed-rate adjustment methods.To ensure accurate inversion results and avoid local minima in the cost function minimization, we implement a Multiscaling inversion strategy within the workflow. The obtained results are compared with those achieved through conventional FWI using standard staggered finite differences and various optimization methods. Our findings demonstrate that employing non-balanced staggered-grid finite differences scheme leads to significant improvements compared to the previously reported results in the literature.

Mechanism design for public projects via three machine learning based approaches

We study mechanism design for nonexcludable and excludable binary public project problems. Our aim is to maximize the expected number of consumers and the expected agents’ welfare. We first show that for the nonexcludable public project model, there is no need for machine learning based mechanism design. We identify a sufficient condition on the prior distribution for the existing conservative equal costs mechanism to be the optimal strategy-proof and individually rational mechanism. For general distributions, we propose a dynamic program that solves for the optimal mechanism. For the excludable public project model, we identify a similar sufficient condition for the existing serial cost sharing mechanism to be optimal for 2 and 3 agents. We derive a numerical upper bound and use it to show that for several common distributions, the serial cost sharing mechanism is close to optimality. The serial cost sharing mechanism is not optimal in general. We propose three machine learning based approaches for designing better performing mechanisms. We focus on the family of largest unanimous mechanisms, which characterizes all strategy-proof and individually rational mechanisms for the excludable public project model. A largest unanimous mechanism describes an iterative mechanism, which is defined by an exponential number of mechanism parameters. Our first approach describes the largest unanimous mechanism family using a neural network and training is carried out by minimizing a cost function that combines the mechanism design objective and the constraint violation penalty. We interpret the largest unanimous mechanisms as price-oriented rationing-free (PORF) mechanisms, which enables us to move the mechanisms’ iterative decision making off the neural network, to a separate simulation process, therefore avoiding the vanishing gradient problem. We also feed the prior distribution’s analytical form into the cost function to achieve high-quality gradients for efficient training. Our second approach treats the mechanism design task as a Markov Decision Process with an exponential number of states. During the Markov decision process, the non-consumers are gradually removed from the system. We train multiple neural networks, each for a different number of remaining agents, to learn the optimal value function on the states. Training is carried out by supervised learning toward a set of manually prepared base cases and the Bellman equation. Our third approach is based on reinforcement learning for a Partially Observable Markov Decision Process. Each RL episode randomly draws a type profile, which is hidden from the RL agent (mechanism designer). The RL agent only observes which cost share offers have been accepted under the largest unanimous mechanism under discussion. We use a continuous action space reinforcement learning approach to adjust the offer policy (i.e., adjust mechanism parameters). Lastly, our first two approaches use “supervision to manual mechanisms” as a systematic way for network initialization, which is potentially valuable for machine learning based mechanism design in general.

Reliability-based design optimization for a vertical-type breakwater with multiple limit-state equations under Korean marine environments varying from sea to sea

In this study, as part of basic research aimed at enhancing the accuracy of load and resistance coefficients to ensure their suitability for practical design and promoting the application of underutilized reliability-based design in Korea, the author conducts optimal design based on the reliability analysis of a vertical-type breakwater in the seas off of Haeundae, Yeosu, Mokpo, Gunsan, and Incheon—representative ports in Korea. In doing so, the author utilized the double-loop approach, which simultaneously addresses a reliability problem nested within an optimization process, employing the Polak–He optimization algorithm. To mitigate the substantial numerical effort required by the double-loop approach based on the Polak–He optimization algorithm, which necessitates the gradients of both cost and constraint functions, the subset simulation method was employed. In this process, the author deliberately refrained from using design waves of a specific return period and linear probabilistic models such as the Gaussian distribution, especially concerning wave and lift forces, often viewed as barriers to the widespread application of reliability-based design in Korea. Instead, the author focused on characterizing the uncertainties associated with the wave force, lift force, and overturning moment—variables that significantly impact the integrity of vertical-type breakwaters—by developing probabilistic models for these random variables directly from long-term in situ wave data. These models capture the varied characteristics of the Korean marine environment from sea to sea. In this way, the need for additional assumptions concerning the interrelationship between significant wave and maximum wave heights, along with the wave period, can be eliminated. Following Occam's razor principle, which suggests that explanations constructed with the smallest possible set of assumptions are superior, the reliability-based design optimization of a vertical-type breakwater presented in this study demonstrates promise in terms of simplicity and practicality. The limit state of the vertical-type breakwater was defined to encompass sliding, overturning, and collapse failures, and the strong interrelations between the wave force, lift force, and overturning moment were described using the Nataf joint distribution. As anticipated, simulation results show that solely considering sliding failure, as in the current reliability-based design platform in Korea, leads to an underestimated failure probability. Furthermore, ensuring a consistent failure probability for vertical-type breakwaters using design waves with a specific return period, as in past studies, is not feasible. In contrast, this study demonstrated that breakwaters optimally designed to meet the reliability index requirement of β = 3.5–4 consistently maintain a target failure probability in all sea areas.

Hemodynamics of ventricular-arterial coupling under enhanced external counterpulsation: An optimized dual-source lumped parameter model

Background and objectiveEnhanced external counterpulsation (EECP) is a mechanically assisted circulation technique widely used in the rehabilitation and management of ischemic cardiovascular diseases. It contributes to cardiovascular functions by regulating the afterload of ventricle to improve hemodynamic effects, including increased diastolic blood pressure at aortic root, increased cardiac output and enhanced blood perfusion to multiple organs including coronary circulation. However, the effects of EECP on the coupling of the ventricle and the arterial system, termed ventricular-arterial coupling (VAC), remain elusive. We aimed to investigate the acute effect of EECP on the dynamic interaction between the left ventricle and its afterload of the arterial system from the perspective of ventricular output work. MethodsA neural network assisted optimization algorithm was proposed to identify the ordinary differential equation (ODE) relation between aortic root blood pressure and flow rate. Based on the optimized order of ODE, a lumped parameter model (LPM) under EECP was developed taking into consideration of the simultaneous action of cardiac and EECP pressure sources. The ventricular output work, in terms of aortic pressure and flow rate cooperated with the LPM, was used to characterize the VAC of ventricle and its afterload. The VAC subjected to the principle of minimal ventricular output work was validated by solving the Euler-Poisson equation of cost function, ultimately determining the waveforms of aortic pressure and flow rate. ResultsA third-order ODE can precisely describe the hemodynamic relationship between aortic pressure and flow rate. An optimized dual-source LPM with three energy-storage elements has been constructed, showing the potential in probing VAC under EECP. The LPM simulation results demonstrated that the VAC in terms of aortic pressure and flow rate yielded to the minimal ventricular output work under different EECP pressures. ConclusionsThe ventricular-arterial coupling under EECP is subjected to the minimal ventricular output work, which can serve as a criterion for determining aortic pressure and flow rate. This study provides insight for the understanding of VAC and has the potential in characterizing the performance of the ventricular and arterial system under EECP.

Fast Newton-like extremum seeking with asymptotic convergence guarantees

Extremum seeking denotes control approaches to steer the input of a dynamical system towards the optimizer of an associated cost function. The main strength of those schemes is to achieve their objective without any prior knowledge of the mathematical expression of the cost, nor the value of its gradient. However, those algorithms typically suffer from a slow convergence speed. In this paper, we propose a novel class of Newton-like extremum seeking systems separating the gradient and Hessian estimation from the update of the cost input towards the optimizer. The stability properties of the proposed schemes, including the ability of some of them to enforce the asymptotic convergence of the cost inputs to the optimizer, are proved. Their performances are also examined via some numerical examples. The latter will demonstrate the ability of the presented schemes to significantly reduce the convergence time compared with existing Newton-like schemes.

Whole-Body Dynamics for Humanoid Robot Fall Protection Trajectory Generation with Wall Support.

When humanoid robots work in human environments, they are prone to falling. However, when there are objects around that can be utilized, humanoid robots can leverage them to achieve balance. To address this issue, this paper established the state equation of a robot using a variable height-inverted pendulum model and implemented online trajectory optimization using model predictive control. For the arms' optimal joint angles during movement, this paper took the distributed polygon method to calculate the arm postures. To ensure that the robot reached the target position smoothly and rapidly during its motion, this paper adopts a whole-body motion control approach, establishing a cost function for multi-objective constraints on the robot's movement. These constraints include whole-body dynamics, center of mass constraints, arm's end effector constraints, friction constraints, and center of pressure constraints. In the simulation, four sets of methods were compared, and the experimental results indicate that compared to free fall motion, adopting the method proposed in this paper reduces the maximum acceleration of the robot when it touches the wall to 69.1 m/s2, effectively reducing the impact force upon landing. Finally, in the actual experiment, we positioned the robot 0.85 m away from the wall and applied a forward pushing force. We observed that the robot could stably land on the wall, and the impact force was within the range acceptable to the robot, confirming the practical effectiveness of the proposed method.

Fixed-time cluster optimization of multi-agent systems on directed detail-balanced networks

This paper investigates the fixed-time cluster optimization problem of first-order multi-agent systems (MASs) under directed detail-balanced networks, in which the optimization objective is a linear combination of local objective functions. Two piecewise distributed control protocols are proposed to solve the optimization problem with time-invariant and time-varying objective functions, respectively. Under the two proposed piecewise control protocols, the fixed-time cluster optimization problem can be solved on the basis of maintaining the cluster consensus unchanged. Furthermore, the initial states of all the agents need not be restricted to the minimum of their local cost functions. Finally, we provide two examples to illustrate the effectiveness of the proposed control protocols.

Convolutional neural network for improved event-based Shack-Hartmann wavefront reconstruction

Shack-Hartmann wavefront sensing is a technique for measuring wavefront aberrations, whose use in adaptive optics relies on fast position tracking of an array of spots. These sensors conventionally use frame-based cameras operating at a fixed sampling rate to report pixel intensities, even though only a fraction of the pixels have signal. Prior in-lab experiments have shown feasibility of event-based cameras for Shack-Hartmann wavefront sensing (SHWFS), asynchronously reporting the spot locations as log intensity changes at a microsecond time scale. In our work, we propose a convolutional neural network (CNN) called event-based wavefront network (EBWFNet) that achieves highly accurate estimation of the spot centroid position in real time. We developed a custom Shack-Hartmann wavefront sensing hardware with a common aperture for the synchronized frame- and event-based cameras so that spot centroid locations computed from the frame-based camera may be used to train/test the event-CNN-based centroid position estimation method in an unsupervised manner. Field testing with this hardware allows us to conclude that the proposed EBWFNet achieves sub-pixel accuracy in real-world scenarios with substantial improvement over the state-of-the-art event-based SHWFS. An ablation study reveals the impact of data processing, CNN components, and training cost function; and an unoptimized MATLAB implementation is shown to run faster than 800 Hz on a single GPU.

Toffoli-depth reduction method preserving in-place quantum circuits and its application to SHA3-256

When implementing a quantum circuit for a desired reversible function, an attempt is made to design an accurate quantum circuit. Then, the quantum circuit is optimized based on a specific cost function, such as design cost, width (the number of qubits), depth, etc. In particular, if an in-place subcircuit itself can be optimized while maintaining its in-place property, it will be a very useful way to increase efficiency without changing the initial architecture of the entire quantum circuit. Furthermore, since its (clean) work qubits can easily be utilized in subsequent subroutines of the quantum circuit, it has an additional important advantage in terms of width. In this paper, for the first time to the best of our knowledge, we present a global Toffoli-depth reduction methodology for an in-place version reversible circuit in the case that the given input circuit is optimized with Toffoli-count. We mainly introduce a process to optimize the χ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\chi $$\\end{document} internal function block in SHA3-256 to explain our Toffoli-depth reduction approach preserving the in-place property, and hence, well-balanced five χ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\chi $$\\end{document} quantum circuits are induced in terms of its width and T-depth. And then, we apply these five χ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\chi $$\\end{document} circuits to design the entire SHA3-256 cryptosystem. One of the proposed SHA3-256 quantum circuits has a width of 1600 and a T-depth of 264, and this shows a result of 50% and 38.9% reduction compared to the previous circuit in terms of width and T-depth, respectively. Other versions of our SHA3-256 circuits just required 10–33 qubits per one T-depth compared to the previous results which require over 1800 qubits per T-depth, and so this means that our SHA3-256 circuits are well-balanced. Finally, we constructed Grover’s algorithm circuit using each version that realized SHA3-256. When output circuits for the presented method were used, quantum volume values of Grover’s algorithm circuits became 33 and 50%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} of the value when the input circuit was used.

Communication topology for AUV formation consensus based on optimal cost control under time-varying communication and input delays

This study investigates the optimal communication topology for consensus in autonomous underwater vehicle (AUV) formations, considering both leader-following and leaderless scenarios. Initially, a second-order dynamic model of the AUV is established using the feedback linearization method. To address time-varying delays, a universal predictor-based consensus protocol is introduced, capable of operation with or without a designated leader. Subsequently, two global quadratic cost functions are formulated based on distinct consensus errors. Utilizing linear quadratic regulator (LQR) theory and matrix theory, the optimal communication topologies and associated gain matrices are deduced. Specifically, for leader-following AUV formations, the optimal communication topology is identified as an unevenly weighted star-shaped directed spanning tree. Conversely, for leaderless formations, the optimal topology adopts a complete digraph, which indicates high interaction requirements. To accommodate practical constraints, a suboptimal topology is devised, featuring a directed spanning tree. These findings offer a theoretical framework for selecting communication topologies in AUV formations, with numerical examples provided to validate the proposed approaches.

MTP-YOLO: You Only Look Once Based Maritime Tiny Person Detector for Emergency Rescue

Tiny person detection based on computer vision technology is critical for maritime emergency rescue. However, humans appear very small on the vast sea surface, and this poses a huge challenge in identifying them. In this study, a single-stage tiny person detector, namely the “You only look once”-based Maritime Tiny Person detector (MTP-YOLO), is proposed for detecting maritime tiny persons. Specifically, we designed the cross-stage partial layer with two convolutions Efficient Layer Aggregation Networks (C2fELAN) by drawing on the Generalized Efficient Layer Aggregation Networks (GELAN) of the latest YOLOv9, which preserves the key features of a tiny person during the calculations. Meanwhile, in order to accurately detect tiny persons in complex backgrounds, we adopted a Multi-level Cascaded Enhanced Convolutional Block Attention Module (MCE-CBAM) to make the network attach importance to the area where the object is located. Finally, by analyzing the sensitivity of tiny objects to position and scale deviation, we proposed a new object position regression cost function called Weighted Efficient Intersection over Union (W-EIoU) Loss. We verified our proposed MTP-YOLO on the TinyPersonv2 dataset. All these results confirm that this method significantly improves model performance while maintaining a low number of parameters and can therefore be applied to maritime emergency rescue missions.

An improved constant current step-based grey wolf optimization algorithm for photovoltaic systems

In this paper, an improved constant current step based on the grey wolf optimization (CCS-GWO) algorithm for photovoltaic systems is investigated. The development of grey wolf optimization has been widely spread over photovoltaic applications. This method is one of the metaheuristic swarm optimization algorithm groups inspired by an optimum means of chasing prey by grey wolves. The proposed technique applies constant current steps to the pack of wolves (alpha, beta, and omega) by monitoring the average of the internal current step and external current step in order to target the leader alpha wolf position. Moreover, the proposed technique solves the convergence process issues, low convergence speed, and premature local optima problems of the traditional GWO algorithm. This CCS-GWO algorithm accurately tracks the maximum power point from the photovoltaic systems for load charging in different partial shading conditions (PSCs). A number of standard benchmark functions are presented with low average cost functions and their corresponding standard deviation values. The simulation results revealed that the proposed CCS-GWO approach outperforms the existing GWO and GA algorithms in terms of efficiency (98.55%) and tracking time (0.3 s).

Development of Modified ECC-Based Secured FoG-Assisted Healthcare Data Management System in IoT-Enabled WSN

The Internet of Things (IoT) consists of a massive count of connected devices with different sensing support, particularly in medical health constraints sensing. In these summaries, secure communication and data collection to centralized servers is rather difficult for preventing the occurrence of diverse attacks for illegal data access. To tackle these problems, this task plans to design and implement the FoG-based secure data management system in IoT-WSN using adaptive encryption and decryption processes. As the FoG server is away from the corresponding aggregator node, it is transmitted via the other nodes using the proposed modified Elliptic Curve Cryptography (ECC)-based encryption process. For secured data transmission the designed method undergoes two main stages as message receiving stage, and the message-extraction stage. The hybridized meta-heuristic algorithm termed Jaya-Galactic Swarm Optimization (J-GSO) is used for modifying the ECC with an optimized key. From the simulation findings, the performance of the suggested scheme at the data size of three bytes is 3.08%, 3.22%, 3.65%, and 3.33% better than the MFO-m-ECC, PSO-m-ECC, GSO-m-ECC, and JA-m-ECC at the ECC curve variation of “secp192r1”. Experimental results reveal the superiority of the designed method when tested with baseline schemes regarding time complexity, space complexity, and cost function.

Solving a class of multi-scale elliptic PDEs by Fourier-based mixed physics informed neural networks

Deep neural networks have garnered widespread attention due to their simplicity and flexibility in the fields of engineering and scientific calculation. In this study, we probe into solving a class of elliptic partial differential equations (PDEs) with multiple scales by utilizing Fourier-based mixed physics informed neural networks (dubbed FMPINN), its solver is configured as a multi-scale deep neural network. In contrast to the classical PINN method, a dual (flux) variable about the rough coefficient of PDEs is introduced to avoid the ill-condition of neural tangent kernel matrix caused by the oscillating coefficient of multi-scale PDEs. Therefore, apart from the physical conservation laws, the discrepancy between the auxiliary variables and the gradients of multi-scale coefficients is incorporated into the cost function, obtaining a satisfactory solution of PDEs by minimizing the defined loss through some optimization methods. Additionally, a trigonometric activation function is introduced for FMPINN, which is suited for representing the derivatives of complex target functions. Handling the input data by Fourier feature mapping will effectively improve the capacity of deep neural networks to solve high-frequency problems. Finally, to validate the efficiency and robustness of the proposed FMPINN algorithm, we present several numerical examples of multi-scale problems in various dimensional Euclidean spaces. These examples cover low-frequency and high-frequency oscillation cases, demonstrating the effectiveness of our approach. All code and data accompanying this manuscript will be publicly available at https://github.com/Blue-Giant/FMPINN.

The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2

Abstract. A new weakly coupled land data assimilation (WCLDA) system based on the four-dimensional ensemble variational (4DEnVar) method is developed and applied to the fully coupled Energy Exascale Earth System Model version 2 (E3SMv2). The dimension-reduced projection four-dimensional variational (DRP-4DVar) method is employed to implement 4DVar using the ensemble technique instead of the adjoint technique. With an interest in providing initial conditions for decadal climate predictions, monthly mean anomalies of soil moisture and temperature from the Global Land Data Assimilation System (GLDAS) reanalysis from 1980 to 2016 are assimilated into the land component of E3SMv2 within the coupled modeling framework with a 1-month assimilation window. The coupled assimilation experiment is evaluated using multiple metrics, including the cost function, assimilation efficiency index, correlation, root-mean-square error (RMSE), and bias, and compared with a control simulation without land data assimilation. The WCLDA system yields improved simulation of soil moisture and temperature compared with the control simulation, with improvements found throughout the soil layers and in many regions of the global land. In terms of both soil moisture and temperature, the assimilation experiment outperforms the control simulation with reduced RMSE and higher temporal correlation in many regions, especially in South America, central Africa, Australia, and large parts of Eurasia. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought, highlighting the crucial role of land surface in drought lifecycle. The WCLDA system is intended to be a foundational resource for research to investigate land-derived climate predictability.

A low computation-cost locomotion control for underwater snake robot based on Monte Carlo model predictive control and curvature derivative control

ABSTRACT This work studies the application of a sample-based model Monte Carlo Model Predictive Control (MCMPC) to address discontinuous events, such as collisions. The inclusion of contact dynamics in the prediction steps of MCMPC renders it advantageous for the operation of underwater snake robots in restricted environments during rescue missions. The Curvature Derivative Control (CDC) method is employed for the purpose of controlling the joints, with the exception of the head joint. The snake robot body demonstrates adaptability to various environments with the assistance of the CDC. Results obtained from the simulation demonstrate that the snake robot is capable of autonomously navigating from its beginning position outside the region to a pipe as the entrance in order to get inside. The robot successfully traverses through various internal structures within the pipe and ultimately reaches the intended destination, guided by a predetermined cost function of MCMPC. By using the capabilities of CUDA GPU parallel computing, the computational time is substantially lowered.

A novel consensus model considering individual and social behaviors under the social trust network

In practical decision-making scenarios, the challenges lie in obtaining the collective opinion that the majority of decision makers are willing to accept, thereby arriving at the final decision result. Among them, both the inherent uncertainty and reliability embedded in the information may lead to a deviation from the fact in the final decision result. Moreover, individual and social behaviors have the potential to influence the acquisition of effective decision result. Based on this, this paper constructs a novel social network-based consensus model under discrete Z-number environment, taking into account the individual and social behaviors of experts. First, novel trust propagation and trust aggregation operators with discrete Z-numbers are developed, which aim to form a complete social trust network. Then, unit consensus cost function and compromise boundary function based on social trust network are given to objectively determine the unit adjustment costs and compromise boundaries of each decision maker. In addition, a minimum cost consensus model considering the coexistence and interaction between individual and social behaviors is explored. Last, the feasibility and superiority of the model are proved through a case of the agricultural supply chain. Through simulation and comparative analysis, the results show the effectiveness of novel trust propagation and aggregation operators. And the necessity of considering individual and social behaviors for consensus reaching process is presented.

Distributed moving horizon fusion estimation for linear constrained uncertain systems

AbstractIn this paper, the distributed moving horizon fusion estimation of uncertain systems with constraints of system noise and state variables is studied. Firstly, relying on the basic idea of consensus algorithm, the cost function in the performance index is reconstructed by weighted fusion of the state prediction values. Secondly, considering the performance index with uncertain parameters, the min‐max optimization problem of the algorithm is transformed into the least squares optimization problem based on 2‐norm regularization method. Thirdly, the scalar‐weighted linear minimum variance fusion estimation strategy is used to realize the weighted fusion of local state estimation values. Then, on the premise of minimum network connectivity and collective observability, the stability of the proposed algorithm is studied, and the sufficient conditions for the expected convergence of the fused estimation error norm square are given. Finally, the effectiveness of the algorithm is verified by numerical simulation.