Sequential quadratic programming-based non-cooperative target distributed hybrid processing optimization method

The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.

Non-Cooperative Behavior Management in Large-Scale Group Decision-Making Considering the Altruistic Behaviors of Experts and Its Application in Emergency Alternative Selection

Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns,leading to non-cooperative behaviors during the consensus-reaching process.Many studies on noncooperative behavior management assumed that the maximumdegree of cooperation of experts is to totally accept the revisions suggested by the moderator,which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process.In addition,when grouping a large group into subgroups by clustering methods,existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously.In this study,we introduce a clustering method considering the similarity of evaluation values and the trust relations of experts and then develop a consensusmodel taking into account the altruistic behaviors of experts.First,we cluster experts into subgroups by a constrained Kmeans clustering algorithm according to the opinion similarity and trust relationship of experts.Then,we calculate the weights of experts and clusters based on the centrality degrees of experts.Next,to enhance the quality of consensus reaching,we identify three kinds of non-cooperative behaviors and propose corresponding feedback mechanisms relying on the altruistic behaviors of experts.A numerical example is given to show the effectiveness and practicality of the proposed method in emergency decision-making.The study finds that integrating altruistic behavior analysis in group decision-making can safeguard the interests of experts and ensure the integrity of decision-making information.

Non-Cooperative Game of Coordinated Scheduling of Parallel Machine Production and Transportation in Shared Manufacturing

Given the challenges of manufacturing resource sharing and competition in the modern manufacturing industry,the coordinated scheduling problem of parallel machine production and transportation is investigated.The problem takes into account the coordination of production and transportation before production as well as the disparities in machine spatial position and performance.A non-cooperative game model is established,considering the competition and self-interest behavior of jobs from different customers for machine resources.The job from different customers is mapped to the players in the game model,the corresponding optional processing machine and location are mapped to the strategy set,and the makespan of the job is mapped to the payoff.Then the solution of the scheduling model is transformed into the Nash equilibrium of the non-cooperative game model.A Nash equilibrium solution algorithm based on the genetic algorithm(NEGA)is designed,and the effective solution of approximate Nash equilibrium for the game model is realized.The fitness function,single-point crossover operator,and mutation operator are derived from the non-cooperative game model’s characteristics and the definition of Nash equilibrium.Rules are also designed to avoid the generation of invalid offspring chromosomes.The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes.Compared with other algorithms such as heuristic algorithms(FCFS,SPT,and LPT),the simulated annealing algorithm(SA),and the particle swarm optimization algorithm(PSO),experimental results show that the proposed NE-GA algorithm has obvious performance advantages.

Peer-to-Peer Energy Trading Method of Multi-Virtual Power Plants Based on Non-Cooperative Game

The current electricity market fails to consider the energy consumption characteristics of transaction subjects such as virtual power plants.Besides,the game relationship between transaction subjects needs to be further explored.This paper proposes a Peer-to-Peer energy trading method for multi-virtual power plants based on a non-cooperative game.Firstly,a coordinated control model of public buildings is incorporated into the scheduling framework of the virtual power plant,considering the energy consumption characteristics of users.Secondly,the utility functions of multiple virtual power plants are analyzed,and a non-cooperative game model is established to explore the game relationship between electricity sellers in the Peer-to-Peer transaction process.Finally,the influence of user energy consumption characteristics on the virtual power plant operation and the Peer-to-Peer transaction process is analyzed by case studies.Furthermore,the effect of different parameters on the Nash equilibrium point is explored,and the influence factors of Peer-to-Peer transactions between virtual power plants are summarized.According to the obtained results,compared with the central air conditioning set as constant temperature control strategy,the flexible control strategy proposed in this paper improves the market power of each VPP and the overall revenue of the VPPs.In addition,the upper limit of the service quotation of the market operator have a great impact on the transaction mode of VPPs.When the service quotation decreases gradually,the P2P transaction between VPPs is more likely to occur.

Coordinated Scheduling of Two-Agent Production and Transportation Based on Non-Cooperative Game

A two-agent production and transportation coordinated scheduling problem in a single-machine environment is suggested to compete for one machine from different downstream production links or various consumers.The jobs of two agents compete for the processing position on a machine,and after the pro-cessed,they compete for the transport position on a transport vehicle to be trans-ported to two agents.The two agents have different objective functions.The objective function of the first agent is the sum of the makespan and the total trans-portation time,whereas the objective function of the second agent is the sum of the total completion time and the total transportation time.Given the competition between two agents for machine resources and transportation resources,a non-cooperative game model with agents as game players is established.The job pro-cessing position and transportation position corresponding to the two agents are mapped as strategies,and the corresponding objective function is the utility func-tion.To solve the game model,an approximate Nash equilibrium solution algo-rithm based on an improved genetic algorithm(NE-IGA)is proposed.The genetic operation based on processing sequence and transportation sequence,as well as the fitness function based on Nash equilibrium definition,are designed based on the features of the two-agent production and transportation coordination scheduling problem.The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes.When compared to heuristic rules such as the Longest Processing Time first(LPT)and the Shortest Processing Time first(SPT),the objective function values of the two agents are reduced by 4.3%and 2.6% on average.

Frequency-Hopping Frequency Reconnaissance and Prediction for Non-cooperative Communication Network

The continuous change of communica-tion frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication net-works.Since the frequency-hopping(FH)sequence is usually generated by a certain model with certain regularity,the FH frequency is thus predictable.In this paper,we investigate the FH frequency reconnais-sance and prediction of a non-cooperative communi-cation network by effective FH signal detection,time-frequency(TF)analysis,wavelet detection and fre-quency estimation.With the intercepted massive FH signal data,long short-term memory(LSTM)neural network model is constructed for FH frequency pre-diction.Simulation results show that our parameter es-timation methods could estimate frequency accurately in the presence of certain noise.Moreover,the LSTM-based scheme can effectively predict FH frequency and frequency interval.

Impedance control of multi-arm space robot for the capture of non-cooperative targets

Robotic systems are expected to play an increasingly important role in future space activities. The robotic on-orbital service, whose key is the capturing technology, becomes a research hot spot in recent years. This paper studies the dynamics modeling and impedance control of a multi-arm free-flying space robotic system capturing a non-cooperative target. Firstly, a control-oriented dynamics model is essential in control algorithm design and code realization. Unlike a numerical algorithm, an analytical approach is suggested. Using a general and a quasi-coordinate Lagrangian formulation, the kinematics and dynamics equations are derived.Then, an impedance control algorithm is developed which allows coordinated control of the multiple manipulators to capture a target.Through enforcing a reference impedance, end-effectors behave like a mass-damper-spring system fixed in inertial space in reaction to any contact force between the capture hands and the target. Meanwhile, the position and the attitude of the base are maintained stably by using gas jet thrusters to work against the manipulators' reaction. Finally, a simulation by using a space robot with two manipulators and a free-floating non-cooperative target is illustrated to verify the effectiveness of the proposed method.

Design of a Non-cooperative Target Capture Mechanism

A passive compliant non-cooperative target capture mechanism is designed to maintain the non-cooperative target on-orbit. When the relative position between capture mechanism and satellite is confirmed,a pair of four-bar linkages lock the docking ring,which is used for connecting the satellite and the rocket. The mathematical model of capture mechanism and capture space is built by the Denavit-Hartenberg(D-H)method,and the torque of each joint is analyzed by the Lagrange dynamic equation. Besides,the capture condition and the torque of every joint under different capture conditions are analyzed by simulation in MSC. Adams. The results indicate that the mechanism can capture the non-cooperative target satellite in a wide range. During the process of capture,the passive compliant mechanism at the bottom can increase capture space,thereby reducing the difficulty and enhance stability of the capture.

Resource Allocation for Network Security Risk Assessment：A Non-Cooperative Differential Game Based Approach

In this paper, we propose a non-cooperative differential game theory based resource allocation approach for the network security risk assessment. For the risk assessment, the resource will be used for risk assess, including response cost and response negative cost. The whole assessment process is considered as a differential game for optimal resource control. The proposed scheme can be obtained through the Nash Equilibrium. It is proved that the game theory based algorithm is applicable and the optimal resource level can be achieved based on the proposed algorithm.

Design of a Flexible Capture Mechanism Inspired by Sea Anemone for Non-cooperative Targets

Robotic grippers have been used in industry as end-effectors but are usually limited to operations in pre-defined workspace.However,few devices can capture irregularly shaped dynamic targets in space,underwater and other unstructured environments.In this paper,a novel continuum arm group mechanism inspired by the morphology and motions of sea anemones is proposed.It is able to dissipate and absorb the kinetic energy of a fast moving target in omni-direction and utilize multiple arms to wrap and lock the target without accurate positioning control.Wire-driven actuation systems are implemented in the individual continuum arms,achieving both bending motion and stiffness regulation.Through finite element method,the influence of different configurations of the continuum arm group on the capture performance is analyzed.A robotic prototype is constructed and tested,showing the presented arm group mechanism has high adaptability to capture targets with different sizes,shapes,and incident angles.

QoS awared power and admission controls based on non-cooperative game theory in wireless networks

In order to better accommodate heterogeneous quality of service (QoS) in wireless networks, an algorithm called QoS-aware power and admission controls (QAPAC) is proposed. The system is modeled as a non-cooperative game where the users adjust their transmit powers to maximize the utility, thus restraining the interferences. By using adaptive utility functions and tunable pricing parameters according to QoS levels, this algorithm can well meet different QoS requirements and improve system capacity compared with those that ignore the QoS differences.

Non-cooperative target pose estimation based on improved iterative closest point algorithm

For localisation of unknown non-cooperative targets in space,the existence of interference points causes inaccuracy of pose estimation while utilizing point cloud registration.To address this issue,this paper proposes a new iterative closest point(ICP)algorithm combined with distributed weights to intensify the dependability and robustness of the non-cooperative target localisation.As interference points in space have not yet been extensively studied,we classify them into two broad categories,far interference points and near interference points.For the former,the statistical outlier elimination algorithm is employed.For the latter,the Gaussian distributed weights,simultaneously valuing with the variation of the Euclidean distance from each point to the centroid,are commingled to the traditional ICP algorithm.In each iteration,the weight matrix W in connection with the overall localisation is obtained,and the singular value decomposition is adopted to accomplish high-precision estimation of the target pose.Finally,the experiments are implemented by shooting the satellite model and setting the position of interference points.The outcomes suggest that the proposed algorithm can effectively suppress interference points and enhance the accuracy of non-cooperative target pose estimation.When the interference point number reaches about 700,the average error of angle is superior to 0.88°.

A DNN based trajectory optimization method for intercepting non-cooperative maneuvering spacecraft

Current successes in artificial intelligence domain have revitalized interest in neural networks and demonstrated their potential in solving spacecraft trajectory optimization problems. This paper presents a data-free deep neural network(DNN) based trajectory optimization method for intercepting noncooperative maneuvering spacecraft, in a continuous low-thrust scenario. Firstly, the problem is formulated as a standard constrained optimization problem through differential game theory and minimax principle. Secondly, a new DNN is designed to integrate interception dynamic model into the network and involve it in the process of gradient descent, which makes the network endowed with the knowledge of physical constraints and reduces the learning burden of the network. Thus, a DNN based method is proposed, which completely eliminates the demand of training datasets and improves the generalization capacity. Finally, numerical results demonstrate the feasibility and efficiency of our proposed method.

Multi-Feature Fusion Based Relative Pose Adaptive Estimation for On-Orbit Servicing of Non-Cooperative Spacecraft

On-orbit servicing, such as spacecraft maintenance, on-orbit assembly, refueling, and de-orbiting, can reduce the cost of space missions, improve the performance of spacecraft, and extend its life span. The relative state between the servicing and target spacecraft is vital for on-orbit servicing missions, especially the final approaching stage. The major challenge of this stage is that the observed features of the target are incomplete or are constantly changing due to the short distance and limited Field of View (FOV) of camera. Different from cooperative spacecraft, non-cooperative target does not have artificial feature markers. Therefore, contour features, including triangle supports of solar array, docking ring, and corner points of the spacecraft body, are used as the measuring features. To overcome the drawback of FOV limitation and imaging ambiguity of the camera, a "selfie stick" structure and a self-calibration strategy were implemented, ensuring that part of the contour features could be observed precisely when the two spacecraft approached each other. The observed features were constantly changing as the relative distance shortened. It was difficult to build a unified measurement model for different types of features, including points, line segments, and circle. Therefore, dual quaternion was implemented to model the relative dynamics and measuring features. With the consideration of state uncertainty of the target, a fuzzy adaptive strong tracking filter( FASTF) combining fuzzy logic adaptive controller (FLAC) with strong tracking filter(STF) was designed to robustly estimate the relative states between the servicing spacecraft and the target. Finally, the effectiveness of the strategy was verified by mathematical simulation. The achievement of this research provides a theoretical and technical foundation for future on-orbit servicing missions.

An Optimized Resource Scheduling Strategy for Hadoop Speculative Execution Based on Non-cooperative Game Schemes

Hadoop is a well-known parallel computing system for distributed computing and large-scale data processes.“Straggling”tasks,however,have a serious impact on task allocation and scheduling in a Hadoop system.Speculative Execution(SE)is an efficient method of processing“Straggling”Tasks by monitoring real-time running status of tasks and then selectively backing up“Stragglers”in another node to increase the chance to complete the entire mission early.Present speculative execution strategies meet challenges on misjudgement of“Straggling”tasks and improper selection of backup nodes,which leads to inefficient implementation of speculative executive processes.This paper has proposed an Optimized Resource Scheduling strategy for Speculative Execution(ORSE)by introducing non-cooperative game schemes.The ORSE transforms the resource scheduling of backup tasks into a multi-party non-cooperative game problem,where the tasks are regarded as game participants,whilst total task execution time of the entire cluster as the utility function.In that case,the most benefit strategy can be implemented in each computing node when the game reaches a Nash equilibrium point,i.e.,the final resource scheduling scheme to be obtained.The strategy has been implemented in Hadoop-2.x.Experimental results depict that the ORSE can maintain the efficiency of speculative executive processes and improve fault-tolerant and computation performance under the circumstances of Normal Load,Busy Load and Busy Load with Skewed Data.

Accurate 3D geometry measurement for non-cooperative spacecraft with an unfocused light-field camera

This work explores an alternative 3D geometry measurement method for non-cooperative spacecraft guiding navigation and proximity operations.From one snapshot of an unfocused light-field camera, the 3D point cloud of a non-cooperative spacecraft can be calculated from sub-aperture images with the epipolar plane image(EPI) based light-field rendering algorithm.A Chang'e-3 model(7.2 cm×5.6 cm×7.0 cm) is tested to validate the proposed technique.Three measurement distances(1.0 m, 1.2 m, 1.5 m) are considered to simulate different approaching stages.Measuring errors are quantified by comparing the light-field camera data with a high precision commercial laser scanner.The mean error distance for the three cases are 0.837 mm, 0.743 mm, and 0.973 mm respectively, indicating that the method can well reconstruct 3D geometry of a non-cooperative spacecraft with a densely distributed 3D point cloud and is thus promising in space-related missions.

Excess Benefit Allocation in Shared Savings Based on Non-cooperative Game Theory

Energy saving income distribution mode is of great significance to the energy industry.With the continuous application of new technologies,the problem of excess energy saving income distribution has become one of the obstacles to the appreciation of energy performance.At present,the distribution of risk and income is mainly based on the contribution of risk and income,which has some limitations.The benefit distribution of energy saving negotiation between energy saving service companies and clients can be regarded as a bargaining process where an effective range satisfying both parties can be obtained.This provides a new perspective in solving the problem of excess energy saving income distribution in energy management contract projects.

Analytic optimal pose tracking control in close-range proximity operations with a non-cooperative target

This paper investigates an analytical optimal pose tracking control problem for chaser spacecraft during the close-range proximity operations with a non-cooperative space target subject to attitude tumbling and unknown orbital maneuvering.Firstly,the relative translational motion between the orbital target and the chaser spacecraft is described in the Line-of-Sight(LOS)coordinate frame along with attitude quaternion dynamics.Then,based on the coupled 6-Degree of Freedom(DOF)pose dynamic model,an analytical optimal control action consisting of constrained optimal control value,application time and its duration are proposed via exploring the iterative sequential action control algorithm.Meanwhile,the global closed-loop asymptotic stability of the proposed predictive control action is presented and discussed.Compared with traditional proximity control schemes,the highlighting advantages are that the application time and duration of the devised controller is applied discretely in light of the influence of the instantaneous pose configuration on the pose tracking performance with less energy consumptions rather than at each sample time.Finally,three groups of illustrative examples are organized to validate the effectiveness of the proposed analytical optimal pose tracking control scheme.

A grasp planning algorithm under uneven contact point distribution scenario for space non-cooperative target capture

The contact point configuration should be carefully chosen to ensure a stable capture,especially for the non-cooperative target capture mission using multi-armed spacecraft.In this work scenario,the contact points on the base and on the arms are distributed on the opposite side of the target.Otherwise,large forces will be needed.To cope with this problem,an uneven-oriented distribution union criterion is proposed.The union criterion contains a virtual symmetrical criterion and a geometry criterion.The virtual symmetrical contact point criterion is derived from the proof of the force closure principle using computational geometry to ensure a stable grasp,and the geometry criterion is calculated by the volume of the minimum polyhedron formed by the contact points to get a wide-range distribution.To further accelerate the optimization rate and enhance the global search ability,a line array modeling method and a continuous-discrete global search algorithm are proposed.The line array modeling method reduces the workload of calculating the descent direction and the gradient available,while the continuous-discrete global search algorithm reducing the optimization dimension.Then a highly efficient grasping is achieved and the corresponding contact point is calculated.Finally,an exhaustive verification is conducted to numerically analyze the disturbance resistance ability,and simulation results demonstrate the effectiveness of the proposed algorithms.

Relative dynamics estimation of non-cooperative spacecraft with unknown orbit elements and inertial tensor

The state estimation for relative motion with respect to non-cooperative spacecraft in ren- dezvous and docking （RVD） is a challenging problem. In this paper, a completely non-cooperative case is considered, which means that both orbit elements and inertial tensor of target spacecraft are unknown. By formulating the equations of relative translational dynamics in the orbital plane of chaser spacecraft, the issue of unknown orbit elements is solved. And for the problem for unknown inertial tensor, we propose a novel robust estimator named interaction cubature Kalman filter （InCKF） to handle it. The novel filter consists of multiple concurrent CKFs interlacing with a max- imum a posteriori （MAP） estimator. The initial estimations provided by the multiple CKFs are used in a Bayesian framework to form description of posteriori probability about inertial tensor and the MAP estimator is applied to giving the optimal estimation. By exploiting special property of spherical-radial （SR） rule, a novel method with respect to approximating the likelihood probability of inertial tensor is presented. In addition, the issue about vision sensor＇s location inconformity with center mass of chaser spacecraft is also considered. The performance of this filter is demonstrated by the estimation problem of RVD at the final phase. And the simulation results show that the perfor- mance of InCKF is better than that of extended Kalman filter （EKF） and the estimation accuracy of oose and attitude is relatively high even in the comoletely non-coooerative case.