1230 results about "Autonomous control" patented technology

Disclosed in the present invention are a multi-modal shared teleoperation system and method for a three-arm space robot. The system at least comprises a master-side teleoperation system, a communication module and a slave-side robot system, wherein the master-side teleoperation system at least comprises two force feedback hand controllers, a microphone array and upper computer software, and the slave-side robot system comprises two operating arms each equipped with a gripper at the end, an observation arm having a binocular camera mounted at the end, a vision unit, a force sensor and lower computer software. The method comprises: an operator controlling two operating arms of an extravehicular robot to execute a task, and controlling an observation arm to acquire a better local field of view. In the method, a multi-modal teleoperation method comprising pose control, voice control and force control is fused with autonomous control of a robot by means of a shared control algorithm, and thus, human-robot collaborative control over the position, orientation and contact force of a robotic arm can be realized on the basis of the requirements of the operator, and the robot autonomously executes other relatively simple tasks, thereby reducing the operation burden of operators, and improving the control efficiency.

A multimodal shared telerobotic system and method for a three-arm space robot, the system at least includes a local-site system, a communication module, and a remote-site system, where the local-site system includes two force-feedback haptic devices for left and right hands, a microphone array, and upper computer software; the remote-site system includes two robotic arms provided with end-effectors, an observation arm with a stereo camera installed at an end thereof, two force sensors, a vision unit and lower computer software; an operator can control the two robotic arms of the robot outside a cabin for performing operations, and control the observation arm to obtain a better local view; and a multimodal telerobotic control method of pose control, voice control, and force control is integrated with the robot's autonomous control through a shared control algorithm.

The invention discloses a micro-grid dynamic coordination control method and system for distributed energy, and relates to the technical field of micro-grid energy management. The method comprises the steps of 1, collecting multi-source micro-grid data in real time, performing edge calculation and data preprocessing operation, and performing early judgment of an island mode; 2, converging the structure and state of each micro-grid, dynamically constructing and updating a micro-grid topological graph, analyzing the structure change and health condition of the micro-grid topological graph, and evaluating the comprehensive risk of micro-grid nodes; 3, calculating the actual distributable power of each type of loads, and carrying out autonomous control and elastic mode switching; and step 4, on the basis of the actual distributable power of each type of loads, evaluating the collaborative energy scheduling capability of the micro-grid in real time, performing role identification and implementing an optimization strategy. The problem of island emergency scheduling response lag caused by sudden failure of a main network due to some extreme events along with continuous expansion of the access scale of distributed energy and a micro-grid is solved.

The invention belongs to the technical field of aircraft navigation, guidance and control, and provides a satellite exploration control system and method based on deep reinforcement learning, and the system comprises a sensing layer, a decision layer, and an execution layer. The sensing layer is used for integrating equipment data and performing primary processing; the decision-making layer is used for deploying a deep reinforcement learning model and regularly carrying out data training and model updating; the execution layer is used for controlling satellite attitude and orbit adjustment and performing autonomous fault diagnosis and recovery operation; according to the method, effective control under different environments and emergencies is realized through continuous learning and optimization, the adaptive capacity and the autonomous control capacity are relatively high, and an efficient and accurate control strategy can be realized in a complex and changeable space environment through gradual optimization and iteration, so that the method is suitable for large-scale popularization and application. And the autonomous operation capability and the task execution efficiency of the satellite are greatly improved.

The invention discloses an intelligent navigation system and method for a ship in the technical field of ship navigation, and the system comprises a sensing module which is used for collecting the surrounding environment information and the state information of the ship in real time; the data processing and fusing module is used for preprocessing, calibrating and fusing the surrounding environment information and the self state information of the sensing module; the route optimization module is used for formulating a navigation plan and a collision avoidance strategy; the navigation control module is used for autonomously controlling the ship according to the output of the ship route optimization module; and the communication monitoring module monitors the navigation state of the ship and the change of the surrounding environment through real-time communication between the ship and a shore-based control center, other ships and a maritime affair management department. According to the method, data of different sensing units are associated, and the associated data are fused, so that mutual verification and complementation can be realized, and the accuracy and reliability of target identification are improved.

The invention relates to the technical field of unmanned aerial vehicle autonomous control, and discloses an unmanned aerial vehicle obstacle avoidance control method and system based on computer vision, and the method comprises the steps: collecting the multi-view image data of a flight environment in real time through a multi-view camera, and carrying out the preprocessing; using an improved YOLOv7 network to identify obstacles in the preprocessed image, and extracting position, size and motion information of the obstacles; generating a three-dimensional environment map and a plurality of candidate obstacle avoidance paths in combination with the flight parameters of the unmanned aerial vehicle and the extracted obstacle information; an optimal path is screened based on a dynamic safety evaluation model, and the attitude and power output of the unmanned aerial vehicle are adjusted in real time to complete obstacle avoidance; an obstacle avoidance effect is verified by using an optical flow method and depth information, and path planning is dynamically corrected. According to the method, the dynamic safety evaluation model is introduced, multi-dimensional factors are comprehensively considered, the safety of each path is dynamically determined, and it is ensured that the flight path of the unmanned aerial vehicle can be timely and accurately modified and optimized in a dynamic complex environment.

The invention discloses a double-arm robot autonomous control system and method based on teleoperation and visual features. The system comprises a teleoperation acquisition module, a multi-view visual perception module, a data synchronization and demonstration acquisition module, a strategy model training module, an autonomous strategy execution module, a track deviation detection and takeover module and a hybrid control interface module. Human demonstration is completed through teleoperation, multi-modal information of images, tracks, clamping jaws and muscle activation is collected, perception features are fused by adopting multi-view space-time alignment and a cross-view attention mechanism, strategy learning is completed in combination with an end-to-end large model, and in the autonomous operation stage, the multi-view space-time alignment and the cross-view attention mechanism are combined with the end-to-end large model. The risk of current operation is predicted and evaluated through track deviation detection and collision probability, the autonomous control proportion is dynamically adjusted, manual intervention is allowed when necessary, the safety and stability of the whole system are improved, the robot control mode of seamless switching between man-machine cooperation and autonomous and teleoperation is achieved, and the method is suitable for object control tasks in complex and highly variable environments.

The invention relates to the field of power distribution networks, in particular to a power distribution network voltage collaborative autonomous method and system based on dynamic partition. The method comprises the following steps: acquiring electrical measurement data and network topology parameters of distributed nodes of a power distribution network, and generating a characteristic state set representing the operation state of a system; performing dynamic subarea division based on node voltage coupling strength and power balance constraint to obtain a dynamic subarea set with an autonomous boundary; each partition control main body independently solves a voltage regulation objective function of the partition according to an autonomous boundary, and generates a partition autonomous control strategy; and boundary interactive iterative coordination is carried out between adjacent partitions, and a global optimal voltage cooperative control instruction is generated and executed. According to the method, the problems that partition division is not matched with the operation state, and partition collaboration is insufficient are solved, unification of partition autonomy and global optimization is achieved, and the real-time performance and accuracy of voltage regulation and control of the power distribution network are remarkably improved.

The invention relates to the technical field of intelligent equipment autonomous control, and discloses an AIGA-driven intelligent equipment autonomous control and feedback adjustment method, which comprises the following steps of S1, collecting equipment body state parameters and external environment data in real time through a multi-mode sensor array; s2, combining the collected data with a preset user instruction and a real-time environment semantic analysis result; s3, disassembling the task target into a continuous action instruction set; s4, executing the action instruction set through the layered real-time control architecture; s5, monitoring an action execution effect based on the multi-modal sensor array; and S6, analyzing the relevance between the evaluation result and the action instruction through a causal inference engine. According to the method, an equipment action execution result is fed back to an intention generation layer in real time, a deviation source is positioned through a causal inference engine, a control strategy is dynamically adjusted, closed-loop iteration from one-way instruction execution to perception-decision-execution-verification is achieved, and the effect of self-adaptability in a dynamic scene is achieved.

The invention provides an anti-condensation temperature and humidity regulation and control system and method for remote deep learning prediction aided decision making, and the method is characterized in that a field controller is connected with a temperature and humidity collection device, obtains the environment temperature and humidity, calculates the dew point temperature, obtains the surface temperature of a protected part, and starts and stops a heating and / or dehumidification device by comparing the difference between the two values with a preset threshold value; the temperature and humidity acquisition device acquires field environment temperature and humidity data for the field controller to calculate the dew point temperature, and the temperature and humidity acquisition device is also used for acquiring the surface temperature of the protected part; the heating device is used for raising environment temperature; the dehumidification device is used for reducing the environment humidity; the communication module is used for data transmission between the field controller and the remote prediction terminal, sending field data and receiving prediction information; the remote prediction terminal obtains field data through the communication module; an anti-condensation system structure combining local autonomous control and remote prediction assistance is established, and redundancy fault-tolerant capability is achieved; even if the remote prediction function fails, the field control can still operate independently and reliably.

The invention discloses an underwater propeller control method and system based on water body environment analysis. The method comprises the following steps: acquiring image data and laser point cloud data of a target water body, and constructing a three-dimensional environment model; initial position information and a target position of the underwater propeller are obtained, and a working path is generated based on environment model analysis; doppler sonar data on a path are collected, water flow information is analyzed, and a deviation correction control instruction is generated; and the instruction transmission quality is analyzed, and an optimal transmission scheme is determined. According to the method, dynamic modeling and analysis of a complex water body environment are realized by fusing multi-source environment sensing data, the path planning precision and autonomous control capability of the underwater propeller are improved, and the method is suitable for precise navigation and dynamic obstacle avoidance scenes in complex underwater operation tasks.

The invention discloses a satellite formation autonomous control method based on relative motion fitting, belongs to the technical field of spacecraft control, and aims to solve the problem that a traditional formation control method cannot meet formation control requirements in an environment with navigation signal deficiency. The method comprises the following steps: step 1, when a main satellite passes through an ascending node, performing relative motion fitting by utilizing inter-satellite spacing and direction of a plurality of discretely distributed sub-satellites relative to the main satellite observed in a previous orbital period so as to obtain coefficients of relative motion functions of the sub-satellites; step 2, obtaining orbit parameter deviation of the child satellites relative to the primary satellite according to the coefficient of the relative motion function of the child satellites; step 3, acquiring a control vector and a control duration required by the formation adjustment of the child satellites according to the orbit parameter deviation; and step 4, each sub-satellite performs autonomous control according to the control vector and the control duration obtained in the step 3. The method is used for satellite formation autonomous control.

The invention provides a high-precision topographic mapping and three-dimensional modeling system based on an unmanned aerial vehicle, and the system comprises an unmanned aerial vehicle cooperation module which is used for deploying a multi-source sensor and a sensor electronic interface on the unmanned aerial vehicle, and building a distributed network of the unmanned aerial vehicle; the real-time dynamic positioning module is used for acquiring unmanned aerial vehicle positioning data and an initial target terrain map; the autonomous control module is used for generating a flight path, performing dynamic adjustment, constructing an autonomous flight control model and outputting a flight control instruction; the data quality enhancement module is used for performing parameter adjustment on the real-time data and performing space and signal level combined processing on the real-time data so as to enhance the data quality; and the terrain model optimization module is used for processing the laser radar point cloud data so as to construct a three-dimensional terrain model post-processing module, and carrying out data visual display and flight suggestion generation. According to the method, the positioning and modeling precision is greatly improved, efficient surveying and mapping are achieved, the single-point fault risk is reduced, and it is guaranteed that tasks can be completed in a high-safety, high-precision and intelligent mode.

The invention provides an unattended video remote monitoring system. The unattended video remote monitoring system comprises a multi-mode monitoring module, a scene cognition module, a prediction and early warning module, an immune defense module, an autonomous control module and a knowledge evolution module. According to the system, intelligent perception of a scene is realized through collaborative acquisition of multi-source heterogeneous data and deep feature fusion, active early warning of risks is realized by using dynamic causal reasoning and a multi-agent game framework, and stable operation of the system is guaranteed based on an adaptive immune network and a three-level protection mechanism. And adopting a differential neural knowledge graph to support continuous optimization of the system. The technical bottlenecks of fixed threshold, passive response, scene understanding shallow layer and the like of a traditional monitoring system are broken through, autonomous perception, autonomous decision, autonomous operation and maintenance and autonomous optimization of the system are realized, and the system has important application value in the aspects of improving the monitoring quality, reducing the labor cost, enhancing the system reliability and the like.

The invention discloses an intelligent drive-by-wire chassis comprehensive control platform, and belongs to the technical field of automobile chassis control. An intelligent drive-by-wire chassis comprehensive control platform is divided into an information interaction processing layer, a state information estimation layer, a control mode management layer, a comprehensive motion control layer and a drive-by-wire execution layer, so that the drive-by-wire chassis can generalize more intelligent capabilities; comprising environment information cognitive ability, chassis fault pre-judgment ability, real-time information interaction ability, vehicle stability autonomous control ability, minimum risk state automatic control ability and failure state autonomous adjustment ability. The chassis health state assessment is used for acquiring the execution capability and the comprehensive health degree of chassis components in real time, carrying out fault definition and division based on the information, and ensuring the implementation or degradation control of an expected target through failure reconstruction control; the minimum risk control module serves as a minimum intelligent driving backup, and it is guaranteed that after an intelligent driving system fails or exceeds a design operation domain, the vehicle can achieve basic decision making, planning and control for guaranteeing safety of the vehicle.

The invention relates to the technical field of data communication, and discloses a rail transit station-level agent implementation method and system. The method comprises the following steps: constructing a professional body base through an ARM processor, designing a LiteIP security communication bus based on an FPGA, constructing a multi-core CPU cluster to construct an intelligent body center, establishing a real-time and entity database to form a data basis, fusing federated learning and a neural network to generate a decision, and executing edge calculation to realize station-level autonomous control. According to the invention, millisecond-level data exchange and intelligent cooperative control in the station-level system are realized, and the response speed and the processing efficiency of the system in an emergency are remarkably improved.

A method autonomously controls at least one actuator of an automotive device. The actuator has an influence on the trajectory of the automotive device. The method includes acquiring parameters relating to the trajectory of the automotive device and to the speed of the automotive device, and having a computer calculate a control setpoint for each actuator, as a function of the parameters, using a controller. The controller used varies according to the speed of the vehicle.

The invention relates to the technical field of tunnel engineering safety monitoring, in particular to an autonomous controllable three-dimensional tunnel gushing analysis method and system. The method comprises the following steps: collecting multi-source heterogeneous geological, hydrological and construction data, fusing to obtain multi-source heterogeneous data, and constructing a dynamically updated three-dimensional geological-construction coupling digital twinborn model after alignment coding; carrying out multi-scale physical-AI hybrid modeling, correcting real-time data, carrying out water gushing evolution rolling prediction, and outputting prediction data; a three-dimensional visual platform is constructed, and multi-level linkage early warning is started based on a risk threshold value; the system comprises a data fusion module, a water gushing evolution rolling prediction module and a multi-stage linkage early warning module. By fusing geological data, hydrological monitoring data and construction parameters and combining three-dimensional visual rendering and deep learning fluid simulation technologies, the requirements for rapid early warning and timely decision making of water burst disasters in the tunnel construction process are met, and intelligent support is provided for engineering disaster prevention decision making.

The invention discloses a bridge ship collision early warning system based on artificial intelligence, and relates to the field of intelligent traffic infrastructure safety protection. The method is used for solving the problems of difficulty in multi-source data fusion and low risk identification accuracy in a complex water area. The method comprises the following steps: firstly, uniformly mapping a radar point cloud, an AIS signal and a visual trajectory to a coordinate system taking a pier as a center through a Lie group transformation algorithm, and constructing a ship motion state tensor; then, ship behavior characteristics are extracted through a space-time diagram convolutional network, and intention probability distribution and behavior deviation degree are output; thirdly, an orthogonal constraint variational auto-encoder is adopted to decouple the autonomous control behavior and the environment disturbance factor, and a collision risk quantized value is generated according to the norm ratio of the autonomous control behavior and the environment disturbance factor; and finally, constructing a dynamic alarm threshold value according to the behavior deviation degree and the disturbance characteristics, realizing graded early warning, and driving a behavior pattern library to update through a feedback mechanism to form a self-adaptive closed-loop early warning system.

Systems and techniques are described for implementing autonomous control of powered earth-moving vehicles, including to automatically control movement of a vehicle's component parts on a job site to perform tasks. The techniques may include using an MPC-based Control System to perform a cycle of training and deployment of a predictive model specific to a particular earth-moving vehicle to control that vehicle's autonomous operations, and to further using resulting data in additional manners—such a cycle may include using a data gathering module on the vehicle to gather actual operational data of the vehicle during manual control of the vehicle on job site(s) by human operator(s) during performance of task(s), generating a 3D site map modeling the vehicle's surroundings, training a Model Predictive Control (MPC) model based on the actual operational data and 3D site map, and deploying the trained model to the vehicle for use in autonomous operations.

In some embodiments, a non-transitory computer-readable medium having logic stored thereon is provided. The logic, in response to execution by one or more processors of an unmanned aerial vehicle (UAV), causes the UAV to perform actions comprising receiving at least one ADS-B message from an intruder aircraft; generating a intruder location prediction based on the at least one ADS-B message; comparing the intruder location prediction to an ownship location prediction to detect conflicts; and in response to detecting a conflict between the intruder location prediction and the ownship location prediction, determining a safe landing location along a planned route for the UAV and descending to land at the safe landing location.

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

Systems and techniques are described for implementing autonomous control of powered earth-moving vehicles, including to automatically control use of a ripper tool attachment to loosen ground materials for subsequent blade tool operations, such as to determine placements of the ripper tool for multiple ripping passes so that its teeth perform ground-loosening operations that in the aggregate span the width of a blade tool to be used for subsequent pushing / cutting operations. For example, the techniques may include obtaining information about a width of a ripper tool attachment and placement of one or more teeth on the ripper tool, obtaining information about a width of a blade tool attachment, and determining multiple placements of the ripper tool attachment during ripping operations that in the aggregate cover the width of the blade tool attachment to be used for subsequent pushing / cutting operations.

The invention discloses an ocean wind power underwater noise monitoring method and system, and belongs to the technical field of ocean environment monitoring, artificial intelligence and autonomous systems.The ocean wind power underwater noise monitoring method comprises the steps that a digital twinborn model fusing multi-mode perception is constructed; deducing future states of the system and the environment based on a digital twinborn model to generate a predictive state sequence; solving and generating a cooperative control strategy according to a joint optimization target of data quality and energy consumption; and analyzing the cooperative control strategy to synchronously regulate and control the physical system. According to the method, a digital twinborn driven cognitive decision and autonomous control architecture is adopted, a high-fidelity digital twinborn body is constructed to carry out prospective and uncertainty deduction on a physical system and a marine environment, and a closed-loop decision is carried out by taking risk-income optimization as a principle; the monitoring system can have long-term, autonomous and online self-improvement and risk avoidance capabilities, and the reliability of monitoring data in a complex marine environment and the full-life-cycle autonomous operation level of the system are remarkably improved.

The invention relates to a variant four-rotor unmanned aerial vehicle system based on body intelligence and a control method, and belongs to the field of unmanned aerial vehicles. A multi-sensor fusion technology is utilized to perform noise reduction and filtering processing on laser radar point cloud data, extract key feature points and generate a panoramic image; based on environment information sensed in real time, the unmanned aerial vehicle dynamically adjusts the form of the unmanned aerial vehicle through an intelligent control algorithm. In a narrow environment, the unmanned aerial vehicle autonomously shrinks the vehicle arms to reduce the size and ensure safe passing. Under the satellite denial condition, part of the sensors fail, and the unmanned aerial vehicle depends on the multi-mode sensors and the local decision-making ability to ensure that tasks are completed autonomously. Therefore, the system deeply fuses environment perception, form decision and task execution, significantly improves the flight efficiency, cruising ability and task generalization in a complex environment, and especially has strong autonomous control ability in the case of satellite rejection.

A method includes obtaining a first track associated with an object. A first set of parameters is generated based on the first track. Measurement data are obtained from one or more sensors. A first set of features are extracted from the measurement data. Based on the first set of parameters and the first set of features, a second set of parameters are generated by a machine learning model. The second set of parameters represent an adjustment to the first set of parameters. Based on the second set of parameters, the first track is adjusted to generate a second track associated with the object. The second track is provided to an autonomous vehicle control system for autonomous control of a vehicle.

The invention relates to the technical field of unmanned aerial vehicle autonomous control, and discloses a power transmission line unmanned aerial vehicle autonomous inspection path planning method and a quality inspection system, and the method comprises the following steps: obtaining or constructing and dynamically evolving a cognitive digital twin containing geometric, semantic and uncertainty information; based on the first task intention, aiming at reducing the uncertainty of the cognitive digital twinning, generating a first flight path by optimizing information gain; controlling the unmanned aerial vehicle to fly along the first flight path, and performing in-service quality inspection on the acquired data to obtain a quantized target component defect suspected degree; and when the defect suspected degree meets a preset risk triggering condition, dynamically generating a second task intention with a higher priority, and generating a second flight path for fine detailed investigation based on the intention so as to replace or modify the first flight path. According to the invention, the unmanned aerial vehicle is converted into an active diagnosis agent from a passive path executor, and the autonomy, reliability and efficiency of routing inspection are significantly improved.

Systems and techniques are described for implementing autonomous control of earth-moving construction and / or mining vehicles, including to automatically determine and control autonomous movement of part or all of one or more such vehicles (e.g., a vehicle's arm(s) and / or attachment(s), such as a digging bucket, claw, hammer, blade, etc.) to move materials or perform other actions in a manner that is based at least in part on data from simulated operation of the vehicle(s). For example, the systems / techniques may include using data from simulated operation of the earth-moving vehicle(s) in various manners, such as for use in training one or more machine learning models that are used in implementing the autonomous operations, determining optimal or otherwise preferred hardware component configurations to use, determining optimal or otherwise preferred implementation plans to use for one or more tasks and / or multi-task jobs, enabling user what-if experimentation activities, etc.

The invention discloses a robot image-free visual target docking method and system based on multi-modal perception, and the method comprises the steps: carrying out the recognition calculation of a preset target in the image data of the environment in front of a robot through a target detection model YOLOv4-tiny, and constructing a robot control feedback quantity; radial distance information of an object in the environment is obtained, a scanning range is divided through a regional strategy, straight line fitting calculation is conducted, and a deviation evaluation value of the robot relative to the yaw angle is constructed; and the robot accumulative mileage is obtained, finite-state machine hierarchical decision making and mistaken stop prevention judgment are conducted in combination with the robot control feedback quantity and the robot relative yaw angle deviation evaluation value, and robot graph-free visual target docking is achieved. According to the invention, full-process autonomous control of the robot from target discovery, navigation on the way, obstacle avoidance to end fine alignment can be realized. The robot graph-free visual target docking method and system based on multi-modal perception can be widely applied to the technical field of graph-free navigation.

The invention discloses a VSG-VDG-based autonomous control method and device for an AC-DC hybrid micro-grid interconnection converter. The equipment comprises a direct-current micro-grid, an alternating-current micro-grid and an interconnection converter connected with the direct-current micro-grid and the alternating-current micro-grid. The direct current microgrid is provided with a direct current side VDG control module which is used for controlling a direct current converter to adjust direct current power supply output by collecting internal voltage and current signals; the AC micro-grid is provided with an AC side VSG control module which is used for controlling on-off of a switching tube of a three-phase full-bridge inverter by collecting internal voltage and current signals so as to adjust AC three-phase voltage output. The interconnection converter is provided with an interconnection converter control module which is used for connecting the direct-current bus and the alternating-current bus through the bidirectional interface converter and adjusting energy balance in the subnet through bidirectional energy flow. According to the method, the robustness of the AC / DC micro-grid system is enhanced, and the fault self-healing capability of the system under extreme conditions and the transient and steady-state stability of a controlled system are improved.