527 results about "Mathematics model" patented technology

The present invention relates to the technical field of machine tool structure optimized design. Disclosed is a method for the optimized design of a headstock structure of a vertical machining center. The method mainly comprises: parameterizing to establish a headstock three-dimensional model; establishing a finite element model of a headstock structure; according to limit working conditions in an actual machining process, performing static and dynamic characteristic analysis on the headstock structure; performing modal test testing on a headstock, carrying out comparative analysis with a simulation result, and correcting a finite element model of the headstock; using a sensitivity analysis method to select a key dimension of the headstock as an optimized design input parameter, while stress strain, low-order inherent frequency, and quality are target output parameters, and establishing a mathematical model for the optimized design of the headstock; and on the basis of a response surface model constructed by a second-order polynomial and a genetic algorithm, performing optimized design of the headstock until an optimal headstock structure which has higher performance and is lighter is obtained. According to the present invention, design efficiency is improved, prototype testing costs are reduced, and a better headstock structure is obtained.

The invention discloses an unmanned aerial vehicle routing inspection path planning method considering risk levels, which comprises the following steps: 1) simulating an experimental map, and establishing a related mathematical model of a path planning algorithm; 2) using an improved BI-RRT * adaptive step length path planning algorithm to carry out primary measurement recording on the path distance between the inspection points of the unmanned aerial vehicle; 3) designing a grey wolf algorithm evaluation function, enabling the evaluation target to be comprehensive optimum of the inspection task point risk and the unmanned aerial vehicle inspection path cost, and enhancing the local optimum separation capability of the algorithm; and 4) carrying out secondary planning on the path according to the sequence to ensure that the path is optimal. According to the method, the routing inspection cost of the unmanned aerial vehicle is considered in the routing inspection process, and routing inspection points with high risk degrees are preferentially inspected; an IRRT *-Connect path planning algorithm based on environmental complexity is designed, and it is ensured that the unmanned aerial vehicle plans an optimal path with high efficiency when facing a complex and narrow area.

The invention relates to the technical field of railway engineering construction, and discloses a complex region railway engineering construction road intelligent line selection and evaluation decision method, which comprises the following steps: S1, in combination with terrain environment conditions and construction technology standards of a construction road, constructing an improved HAO plane line shape mathematical model, designing plane line shape constraint conditions, and constructing a line shape model; and establishing a line comprehensive cost model as an optimization objective function, and searching an optimal plane linear position parameter in a target area by using a particle swarm optimization algorithm. Compared with other intelligent line selection methods, the method can quickly evaluate and decide the advantages and disadvantages of the route scheme while quickly generating the horizontal and vertical section line shape of the construction road, optimizes the design process, facilitates the shortening of the construction period of the construction road, can remarkably reduce the workload of site survey and manual design, and improves the construction efficiency. The working efficiency and benefits of line selection design are practically improved, the investment of line selection cost is reduced, use is convenient, and the degree of dependence on equipment performance is low.

The invention relates to the technical field of cloud computing and data center energy efficiency optimization, and discloses a carbon neutralization-oriented cloud data center energy efficiency optimization method, which comprises the following steps of: 1, acquiring server computing load, power consumption, cooling state and carbon emission data of a data center based on a distributed sensor network; step 2, constructing an energy efficiency mathematical model of the data center based on a deep learning model of AI training; 3, training a task scheduling and cooling system optimization strategy based on a reinforcement learning agent; 4, analyzing the real-time carbon emission condition of the data center by using an AI prediction algorithm; 5, based on the carbon emission trend predicted by AI, triggering an intelligent carbon compensation strategy in combination with the real-time energy consumption condition of the data center; and step 6, dynamically adjusting task scheduling, a cooling control strategy and a carbon compensation scheme by adopting an AI adaptive learning method based on data feedback. And dynamic scheduling of calculation tasks of the data center is realized through a reinforcement learning agent trained by AI.

The invention discloses an unmanned aerial vehicle cluster ground attack task allocation method and system based on an improved particle swarm algorithm, belongs to the technical field of intelligent optimization algorithms, and aims at solving the problem of unmanned aerial vehicle cluster ground attack task allocation. And analyzing two types of constraints including the target type constraint and the unmanned aerial vehicle type constraint, and establishing a mathematical model for maximizing the attack cost-effectiveness ratio. The method comprises the following specific steps: step 1, establishing a target function for distributing and establishing a target attack revenue and a target attack cost to the ground attack task, wherein the target function is used for maximizing the target attack revenue and minimizing the target attack cost; 2, analyzing constraint conditions of ground attack task allocation; 3, integrating the target function and the constraint condition of the ground attack task allocation, and establishing a mathematical model of the ground attack task allocation; and 4, solving the mathematical model established in the step 3 by using an improved particle swarm algorithm to obtain a final distribution result.

The invention discloses a code generation method, electronic equipment, a readable storage medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the steps that code generation demand information and code generation cue words are input into a language model with a code generation function, and a code generation initial result is obtained; determining a semantic space model for defining a semantic space to which the code generation result belongs according to the code generation demand information, and converting the semantic space model into a mathematical model corresponding to the code generation scene to obtain a semantic verification model; inputting the code generation initial result into a semantic verification model, reasoning the code generation initial result, and determining a semantic verification result according to whether an end node can be reached or not; and determining a code generation result according to the semantic verification result and the code generation initial result. The problem that high-quality codes cannot be generated due to large model illusion in the prior art can be solved, and the code generation accuracy and reliability are effectively improved.

The invention discloses a ship formation autonomous obstacle avoidance control method based on offset intervention guidance. The method comprises the steps of obtaining a nonlinear mathematical model of a three-degree-of-freedom ASV; acquiring a smooth reference path for USV ship formation according to the virtual ship L1VS, and acquiring a ship obstacle avoidance virtual reference path according to the obstacle avoidance virtual ship VS in combination with a DWA algorithm; designing a kinematics virtual control law of the USV ship formation based on the constructed offset intervention guidance mechanism; on the basis of the constructed dynamic event triggering mechanism, a robust neural damping technology is combined, and a robust kinematics error is obtained according to the kinematics error of the USV ship formation; and designing an adaptive robust command filtering controller according to the robust kinematics error so as to realize autonomous obstacle avoidance control of the ship formation. The problems that according to a traditional ship formation control method, when a collision avoidance task in an unknown water area is executed in a complex marine environment, information of moving and fixed obstacles cannot be obtained in time, and communication load balancing and obstacle avoidance control precision cannot be effectively achieved are solved.

According to the new energy automobile flexible assembly line multi-target dynamic intelligent scheduling optimization method coping with the uncertain disturbance influence, the flexibility of the assembly manufacturing unit and the flexibility of the assembly manufacturing process sequence are comprehensively considered, and a dynamic intelligent scheduling optimization scheme is generated for multiple optimization targets. The method comprises the following steps: firstly, constructing a multi-target dynamic intelligent scheduling optimization problem mathematical model of an automobile flexible final assembly line; furthermore, the optimization problem is converted into a Markov decision process, and states, actions, multi-target reward components and reward component aggregation thereof are defined. And then, carrying out a multi-target deep reinforcement learning training process oriented to a new energy automobile flexible final assembly line, and constructing and forming a scheduling agent. And finally, realizing self-adaptive decision making of the automobile flexible assembly line under an uncertain disturbance condition based on the scheduling intelligent agent. According to the method, the robustness and the execution efficiency of the new energy automobile flexible assembly line in a dynamic environment can be remarkably improved, and the method has good practical value and popularization prospect.

The invention relates to a dependent task-oriented resource allocation method, and belongs to the technical field of wireless communication. The method comprises the following steps: S1, in an industrial internet scene, constructing an industrial internet system model comprising a cloud server, an edge server and industrial internet equipment, and modeling a dependency relationship among tasks into a directed acyclic graph; s2, constructing a system total time delay energy consumption weighted sum mathematical model of the industrial internet network; and S3, modeling the problem model as a Markov decision process, and using a deep reinforcement learning (DRL)-based SAC algorithm framework to obtain a task-dependent approximate optimal unloading strategy in an industrial internet IIOT equipment scene with a plurality of edge servers.

The invention discloses a wireless ad hoc network routing and resource cross-layer joint optimization method based on a genetic algorithm. The method comprises the following steps: step 1, aiming at a plurality of end-to-end communication services in a wireless ad hoc network, carrying out mathematical modeling on a normalized difference sum of each service flow path hop count and a theoretical minimum value thereof and a constraint condition; performing joint optimization on link layer resource allocation and network layer routing decision; and step 2, solving the mathematical model based on a genetic algorithm to realize the targets of reasonable distribution of network resources and optimization of service quality. According to the method, link layer resource allocation and network layer routing decision are jointly optimized in a global mode under the condition that network communication resources are limited, it is ensured that a service flow is close to shortest path transmission as far as possible, end-to-end delay and network loads are reduced, and effective allocation of resources and flexible construction of routing are achieved.

The invention relates to an elevation control and precise regulation and control method for frozen sand mold additive manufacturing in a time sequence space. According to the method, a mathematical model that the thickness of floating sand changes along with time due to moisture heat exchange of the frozen sand mold in air is established, and the thickness of the floating sand of frozen sand molds with different curved surfaces under the parameter conditions of different environment temperatures, time scales, sand mold materials, moisture contents and the like is predicted and fed back; and in the freezing printing process, real-time adhesive and height compensation is carried out on sand molds at different wall thicknesses, and the dimensional precision and geometric precision of the frozen sand molds are protected to a certain extent. According to the method, the problem of dimensional accuracy error caused by floating sand generated by heat exchange in air in the manufacturing and subsequent placing processes of the frozen sand mold is effectively solved, and the dimensional accuracy of the sand mold during casting is ensured.

The invention relates to the technical field of computer-aided engineering, in particular to a pure cotton dark-color fabric dye-resistant area dynamic prediction method based on a dye diffusion model. The method comprises the following steps: acquiring a parameter data set representing a fabric microstructure, dye properties and a printing process; on the basis, a multi-scale coupling mathematical model is constructed, the dye diffusion range is predicted, the diffusion process is decomposed into seepage diffusion between macroscopic yarns and diffusion in microcosmic fibers through the model, and the diffusion rate difference of the dye in the fabric direction is represented through anisotropic diffusion tensors; through finite element analysis of structure perception, running a model to calculate dye space-time concentration distribution, and generating geometric data for predicting a bleeding boundary according to a preset concentration threshold value; according to the data, printing process parameters and model parameters are adjusted in a self-adaptive mode, and dynamic prediction of the dye-resistant area is achieved; according to the method, computer simulation and finite element analysis technologies are utilized, and the accuracy of dynamic prediction of the dyeing-resistant area of the pure cotton dark-color fabric is improved.

The invention discloses a super-multi-target vehicle path planning method based on deep reinforcement learning assisted evolution, and aims to dynamically guide a search direction through deep reinforcement learning for multi-target optimization and path planning problems. The core of the method comprises the following steps: 1) constructing a super-multi-target vehicle path problem mathematical model; 2) designing a deep Q network as a decision model, and adaptively selecting a high-potential search direction in iteration; 3) designing a global search strategy, and balancing global search and local optimization; 4) generating a filial generation and evaluating the fitness, and executing environment selection to update the population; and 5) calculating a reward value according to a population evolution effect, and optimizing DQN network parameters. According to the method, through a collaborative mechanism of reinforcement learning and an evolutionary algorithm, a search strategy is dynamically adjusted, the convergence speed and stability of the algorithm are effectively improved, the adaptability to a complex path planning problem is enhanced, and the method has remarkable advantages in solving efficiency, solution set quality and dynamic environment response.

The invention relates to the technical field of cooperative control, in particular to an adaptive event triggering fixed time consistency control method for a multi-agent system, which comprises the following steps of: S1, constructing a nonlinear high-order multi-agent system mathematical model with uncertain dynamics; s2, constructing a Lyapunov function containing an uncertain parameter lower bound based on coordinate transformation; s3, designing an adaptive event trigger controller with a positive bounded function by using a backstepping control method, wherein the adaptive event trigger controller comprises a virtual controller, an adaptive law and an event trigger mechanism; and S4, through stability analysis, it is proved that the high-order nonlinear multi-agent with the uncertain dynamic state can achieve actual fixed time output consistency, Zeno behaviors are avoided, and the self-adaptive event triggering fixed time consistency control method of the multi-agent system has the advantages of being high in practicability and easy to popularize. The problem of actual fixed time output consistency of a high-order nonlinear multi-agent system is solved, and an existing actual fixed time output consistency result is popularized.

The invention discloses an urban vertical take-off and landing airport flight program design and evaluation method, which comprises the following steps of 1, determining an urban vertical take-off and landing airport flight program basic structure and an operation function, and designing a multi-rotor unmanned aerial vehicle in-situ hovering waiting strategy and a flight program operation rule of a dynamic road right matrix planning algorithm; 2, establishing a flight program mathematical model, determining necessary parameters of a flight program, and constructing an optimal flight program structure; 3, selecting a flight program evaluation index, determining a flight program evaluation system, and analyzing the safety and operation efficiency of the flight program; and 4, analyzing the influence of three parameters, namely the first annular layer take-off and landing point, the total number of annular layers and the radius increase amount, on the operation effect of the flight program. According to the flight program design and evaluation method for the vertical take-off and landing airport in the city, operation standardization of the unmanned aerial vehicles in the vertical take-off and landing airport in the city is standardized, operation safety is guaranteed, and operation efficiency is improved.

The present invention relates to a calculation method for the general scour pattern of a cross-sea bridge considering the turbulence effect. The research area including the cross-sea bridge project is determined and meshed, the elevations of each grid node are obtained, typical computational hydrological boundary conditions are selected, a non-hydrostatic three-dimensional mathematical model including the entire cross-sea bridge project is established, the hydrodynamic fields before and after the bridge construction are calculated, the tidal level and flow velocity are verified, the pulsating velocity at each grid node, the sediment settlement velocity in still water and the settlement velocity under the influence of turbulent kinetic energy are calculated, the sediment carrying capacity is calculated according to the estuarine fine sediment model, and finally the general scour amplitude of the seabed at each grid node position is calculated, thereby obtaining the characteristics of the general scour amplitude in the upstream and downstream areas of the bridge. Combining the plane three-dimensional numerical model and the sediment carrying capacity calculation method considering the turbulence effect, through the dynamic equilibrium theory, the accurate prediction of the general scour pattern of the cross-sea bridge including the depth and range is realized, and the calculation accuracy of the general scour pattern of the cross-sea bridge is improved.

The invention discloses an optimal scheduling method considering participation of a multi-element load polymer in demand response, and aims to improve the safety and economy of a power system. The method comprises the following steps: firstly, constructing a mathematical model of a load, representing an adjustable domain of the load, and clustering the load by using a K-means algorithm; and then, carrying out internal approximation on adjustable domains of various loads through scaling and translation factors, optimizing parameters by adopting a linear programming method, and finishing aggregation processing of the loads. And finally, in combination with power supply parameters, a load aggregation domain, market demand prediction and the like, an MILP optimization model with the minimum operation cost is established, power dispatching is performed, a dynamic excitation electricity price mechanism is introduced, the price is adjusted according to the adjustment capability of the load, and the load is excited to actively participate in demand response. According to the scheme, by accurately describing the load adjustable domain and flexibly guiding the load to participate in the demand response, the scheduling of the power system is effectively optimized, the consumption level of renewable energy sources is improved, the operation cost of the system is reduced, and the method has a relatively strong engineering application prospect.

The invention provides a collaborative scheduling and income distribution method for energy storage power stations in an alliance chain, and belongs to the technical field of energy storage power stations. The method comprises the steps of constructing an energy storage capacity sharing alliance block chain taking the energy storage power stations as nodes; an intelligent contract is constructed, the intelligent contract comprises a mathematical model for solving the shared scheduling plan, and the benefit of the shared capacity of each energy storage power station under the optimal shared scheduling plan is obtained based on the intelligent contract; each energy storage power station solves the shared scheduling plan through real-time information, the smart contract compares the earnings under the shared scheduling plans to obtain the shared scheduling plan with the maximum total earnings as the optimal shared scheduling plan, and each energy storage power station executes the optimal shared scheduling plan. And the maximization of the total capacity is realized.

The invention discloses a multi-objective reinforcement learning man-machine cooperative assembly task allocation method based on neighborhood parameter migration, and the method comprises the steps: building a mathematical model which aims at minimizing the physiological fatigue accumulated value of a human operator and minimizing the maximum completion time for a multi-objective optimization problem of task allocation in a man-machine cooperative assembly system; a multi-target problem is decomposed into N standard sub-problems by adopting a weighting and decomposition strategy, and training is accelerated through a neighborhood parameter migration strategy; each sub-problem is solved based on a near-end strategy optimization algorithm of an Actor-Critic framework, Gaussian noise is added to an Actor network to simulate environment uncertainty, an action mask mechanism is introduced to process priority constraints of assembly tasks, and it is ensured that a generated task allocation scheme is always feasible. According to the method, the convergence speed and diversity of the Pareto solution set can be remarkably improved, and the assembly efficiency and operator fatigue are effectively balanced.

The invention discloses a design method of a barrier function rehabilitation robot safety tracking control system, and relates to the technical field of medical robots. The method comprises the steps that on the basis of an existing double-joint single-arm robot mathematical model, a mathematical model with uncertain model parameters, uncertain external disturbance and uncertain external force borne by the tail end is established, and the established model is closer to a real model of a rehabilitation robot; the uncertainty in the model is classified into one class, and a disturbance observer is used for estimating and compensating the uncertainty; a logarithmic obstacle Lyapunov function is adopted to constrain the joint angle; and designing an adaptive trajectory tracking controller based on the disturbance observer and the obstacle Lyapunov function method, and tracking a set trajectory. The high-precision tracking of the target trajectory is ensured, the actual motion trajectory always moves within a certain range on the two sides of the target trajectory, and the secondary injury of the patient in the rehabilitation process is avoided.

The invention provides a self-adaptive fixed time tracking control method based on event triggering, which is used for solving the technical problem that a virtual control law is easy to generate singularity in the traditional backstepping method design. The method comprises the following steps: establishing a mathematical model of a non-strict feedback nonlinear system with input time delay, and introducing an auxiliary system to counteract delay influence; an error dynamic system is obtained through coordinate transformation; according to system fixed time convergence, designing a piecewise function, introducing a backstepping method to design a controller and an adaptive law, using an RBFNN to approach an unknown function therein, designing a virtual control signal corresponding to each error signal, and obtaining an actual controller and an adaptive law; according to the actual self-adaptive controller, event triggering conditions are designed, when the event triggering moment is reached, the event triggering conditions containing input are triggered, and information transmission and response are carried out. An event triggering mechanism is designed, the number of times of control instruction transmission is reduced, communication resources are saved, and rapid convergence and safe operation of the system state are achieved.

The embodiment of the invention discloses a task processing method and device. The task processing method and device are used for improving the task processing efficiency of computing equipment. The method comprises the steps that a calculation device obtains a mathematical model expression and instance data of a to-be-solved problem, the mathematical model expression comprises a target function or a constraint condition, and the instance data is used for indicating the value of a coefficient of the mathematical model expression; and decomposing the to-be-solved problem into N sub-problems based on the mathematical model expression and the instance data of the to-be-solved problem. The N sub-problems are distributed to M threads to be executed in parallel according to the calculated amount of each sub-problem in the N sub-problems, N is larger than M, each thread in the M threads processes one or more sub-problems, and the difference value between the calculated amounts needed by each thread in the M threads to process one or more sub-problems is smaller than or equal to a preset threshold value.

The invention provides a permanent magnet synchronous motor current control method and system based on model predictive control, and aims to improve the dynamic response speed and control precision of a motor through an advanced control strategy. According to the method, a model prediction control technology is utilized, a mathematical model of the motor is constructed, and an optimization algorithm is designed, so that accurate prediction and control of the current of the motor are realized. An intelligent algorithm, such as a neural network or fuzzy logic, is particularly introduced to enhance the adaptability and robustness of a control strategy. Compared with a traditional control method, the method has the advantages that the calculation burden is reduced, the real-time performance of control is ensured, and the performance of the system facing parameter change and external disturbance is remarkably improved. The innovative control method is suitable for industrial application occasions with extremely high requirements on dynamic performance and control precision, and has important practical application value and wide market prospect.

The invention relates to a hydrogen production and refueling station hydrogen leakage range prediction method based on meshless particle method simulation, and belongs to the technical field of hydrogen energy safety and numerical simulation. Aiming at the problems that a traditional CFD grid method is poor in complex geometric boundary adaptability, low in calculation efficiency and insufficient in dynamic early warning capacity, a three-dimensional scene of a hydrogen production and refueling station is constructed by combining a geographic information system (GIS) and a building information model (BIM), a mathematical model is discretized by adopting a smoothed particle hydrodynamics (SPH) method, and the three-dimensional scene of the hydrogen production and refueling station is obtained. Real-time sensor data are integrated, and simulation is accelerated in parallel through a GPU. And carrying out numerical simulation of hydrogen leakage of the hydrogen production / hydrogen refueling station under multiple factors in combination with large eddy simulation, and generating high-precision simulation of evolution and distribution of a hydrogen leakage concentration field of a hydrogen production / hydrogen refueling station scene. The method has the advantages of being high in modeling speed, high in simulation precision, wide in applicability and the like, the problem of precise simulation of hydrogen leakage and diffusion under the complex geometric structure of the hydrogen production and refueling station is effectively solved, and reliable technical support is provided for safe operation of the hydrogen production and refueling station.

The invention discloses a process optimization method for laser-arc hybrid welding seam forming, which comprises the following steps: S1, acquiring a plurality of parameters of a hybrid welding experiment, taking fusion depth and fusion width in the parameters as experiment output parameters, and taking other parameters as experiment input parameters; s2, parameters are analyzed through Spearman correlation coefficients, and then key parameters influencing welding seam forming are obtained; s3, obtaining a range level of a single parameter variable value in the key parameters through a single variable method; s4, a response curved surface mathematical model is established, and more parameter matrix schemes meeting the welding seam forming conditions are screened out through the response curved surface mathematical model; and S5, taking the two input parameter variables, namely the fusion depth and the fusion width, as optimization targets, and performing multi-target optimization on the parameter matrix obtained in the step 4 by using a second-generation non-dominated sorting genetic algorithm to obtain a final optimization solution of the optimization targets. Compared with an existing method for improving welding seam forming based on process parameters, the problems of insufficient efficiency, high experiment cost and the like are solved.

The invention provides a collaborative collision and obstacle avoidance strategy of an underactuated unmanned ship considering input and state quantification. The collaborative collision and obstacle avoidance strategy comprises the following steps: establishing a mathematical model of the underactuated unmanned ship; a distributed guidance rule is constructed in the USV kinematics subsystem by using an auxiliary variable method, and a repulsive force function of an improved artificial potential field method is introduced to reconstruct the guidance rule, so that collaborative collision avoidance and obstacle avoidance at the kinematics level and expected trajectory tracking of the underactuated USV are realized; designing an extended state observer to solve the influence of unknown items in the system and quantitative state variables in the communication process on the formation control system; a linear time-varying model is adopted to describe a quantizer, so that a bottom-layer quantization controller does not need to predict specific information of quantization parameters; and proving the stability of the constructed USV formation tracking control system according to an input-state stability theory. According to the invention, starting from two aspects of input quantization and state quantization, the problem of USV formation tracking control in the marine environment with limited communication bandwidth and complex quantization environment is solved.

The invention relates to the field of intelligent temperature control, and provides a refrigeration house PID control parameter optimization method and device, equipment and a storage medium. The method comprises the steps of collecting temperature data and energy consumption data of the refrigeration house at a constant-temperature stage; based on the temperature data and the energy consumption data, a mathematical model representing the mapping relation between the temperature and the energy consumption of the refrigeration house is established; constructing a deep reinforcement learning model based on a preset reward function and a mathematical model giving consideration to the temperature stability and energy consumption optimization of the refrigeration house; and through interactive learning of the intelligent agent of the deep reinforcement learning model and the refrigeration house environment, determining optimization parameters used for adjusting refrigeration house PID control. According to the refrigeration house PID control parameter optimization method, device and equipment and the storage medium provided by the invention, the mathematical model of the temperature and energy consumption mapping relation of the refrigeration house can be effectively established, the deep reinforcement learning model is constructed through the reward function and the mathematical model, and the optimization parameters of refrigeration house PID control are adjusted through deep reinforcement learning.

The invention discloses a robot optimal path planning method based on an improved Hemma optimization algorithm. A plurality of improvement measures are provided for problems possibly existing in path planning of a Hemma optimization algorithm. Firstly, a Circle chaotic mapping strategy is adopted to replace random initialization, so that the quality and distribution uniformity of an initial population are improved, and the global exploration capability is enhanced. Secondly, mathematical models of three core behavior stages of a standard HO algorithm are deeply optimized, and adaptive dynamic weight, a dynamic weighting strategy based on a current optimal solution and individual fitness and guided Levy flight direction adjustment are introduced; and finally, introducing a dynamic stage conversion and adaptive exploration intensity mechanism DPTAEI, and dynamically adjusting the participation proportion of the individual in different behavior stages and the intensity of the exploration behavior according to the real-time search progress of the algorithm. Through the improvements, the optimization precision, the convergence speed and the global search capability of the IHO algorithm in the aspect of robot path planning are remarkably improved.

The present invention provides a fault-tolerant control method for a DC converter, and relates to the technical field of fault-tolerant control of DC converters. The method comprises the following steps: pre-building a DC converter system, and establishing a corresponding mathematical model according to the switching characteristics of the DC converter system; step 2: designing an adaptive observer to estimate the state of the DC converter system and the voltage source fault; and step 3: designing a state feedback fault-tolerant controller to achieve fault-tolerant control performance. The present invention takes into account that in the case of a voltage source fault and external disturbance in the DC converter, the fault estimation can be performed by designing an adaptive observer, and then the size and shape of the fault can be obtained; and the fault-tolerant control performance is achieved by designing a state feedback fault-tolerant controller, so that in the case of a voltage source fault and external disturbance, the DC converter can still maintain stable and safe operation within the required range. Based on this, the problem that the fault-tolerant control effect cannot be achieved for the DC converter in the prior art is solved.

The invention discloses a multi-agent system safety convergence control method based on dual-channel adaptive event triggering, and the method comprises the steps: firstly building a multi-agent system mathematical model, and designing a controller of each agent in a multi-agent system based on the multi-agent system mathematical model; secondly, establishing a sequence scaling attack model, and combining the sequence scaling attack model to a controller of an intelligent agent; and then designing an event trigger controller based on the controller of the intelligent agent containing the sequence scaling attack. And then, based on the event triggering controller, safety convergence control is carried out, and multi-agent control in the multi-agent system is completed. According to the method, malicious network attacks can be effectively resisted, the problem of limited bandwidth resources can be solved, and the applicability and the application range are improved and expanded.