5009 results about "Robot control" patented technology

The invention discloses a mobile storage and charging robot remote scheduling and path planning system based on the Internet of Things, and relates to the technical field of robot control. Comprising a path dependency modeling module, a path conflict analysis module, a resource dependency graph construction module, a decoupling rearrangement scheduling module, a time sequence offset evaluation module and a path weight regulation and control module, and obtaining a path node sequence and an access time period of a current to-be-executed task of each mobile storage and charging robot, and generating a task path pre-occupation graph. By constructing the path dependence model, the conflict prediction mechanism and the task decoupling rearrangement strategy, accurate identification and effective intervention of path conflicts and resource deadlocks in the multi-robot scheduling process are realized, the stability of the scheduling system and the task execution continuity are improved, efficient completion of energy supply is ensured, and the scheduling efficiency is improved. And the operation efficiency and safety of the system are obviously optimized.

The invention discloses an unhooking robot control method and system based on depth visual recognition, and relates to the field of visual recognition, and the method comprises the steps: obtaining an original optical signal and ambient light intensity, carrying out the dynamic focusing of the original optical signal, generating a target pose sequence according to a frequency domain fusion algorithm and a gradient maximization positioning algorithm, and carrying out the dynamic focusing of the original optical signal; meanwhile, the environment light intensity is substituted into adjacent frame pose difference, and a real-time speed vector is obtained; performing modeling on the target pose sequence and the real-time velocity vector by adopting neuromorphic space-time motion to obtain a predicted trajectory of a dynamic coupling target body and a space-time constraint domain of an end effector of the robot; a bionics principle is adopted to simulate a biological motion coordination mechanism, a target motion track and mechanical arm dynamic constraint are jointly described, and the limitation that track prediction and mechanical arm motion ability are disjointed in traditional isolated modeling is broken through.

The invention belongs to the technical field of robot control, particularly provides a force feedback teleoperation control system and method for the tail end of a mechanical arm of a quadruped robot, and aims at the key challenges that control errors are caused by communication time delay and soft obstacles are difficult to recognize in a dynamic environment. Modeling and judgment are conducted on the contact state of the tail end of the mechanical arm in advance, and feedforward control and buffer adjustment oriented to communication time delay are achieved. And meanwhile, a dynamic semantic map is constructed in combination with multi-source sensing information, and soft obstacle reasoning and path optimization are performed by fusing a tail end force sense change trend, so that the recognition and avoidance capabilities of the system in a complex and invisible obstacle environment are remarkably improved. The system has good perspectiveness, self-adaptability and high redundancy safety characteristics, is suitable for multi-task inspection operation of industrial sites such as a thermal power plant, and is especially suitable for a remote man-machine cooperative operation scene in a narrow space.

The invention discloses a sparse reward environment-oriented collaborative knowledge fusion reinforcement learning method, and relates to the field of collaborative knowledge fusion reinforcement learning methods. By constructing a lightweight collaborative knowledge fusion model and a dynamic reward remodeling mechanism, the problems of low intelligent agent exploration efficiency and difficulty in strategy convergence in a sparse reward environment are solved. The method comprises the following steps: constructing a reinforcement learning framework comprising a policy network and a value network; designing an action space mutation supervision mechanism and a lightweight collaborative knowledge fusion model, and generating a smooth substitution action when a strategy is detected to be unstable; and a reward function is designed in combination with the task target and the dynamic constraint, and reward remodeling is realized by activating rewards through sub-target potential energy difference and knowledge fusion. According to the method, effective intermediate feedback can be provided for the agents in the sparse reward environment, the exploration efficiency is improved, the convergence time is shortened, the stability and cross-scene migration ability of the strategy are enhanced, and an effective solution is provided for sparse reward scenes such as robot control and multi-agent game.

The invention discloses a multi-modal collaborative decision-making method and device for a humanoid robot in an industrial scene, and relates to the technical field of robot control, and the method comprises the steps: obtaining a multi-frame monitoring image, force touch sensor data and equipment operation sensor data of a visual camera of the humanoid robot in the industrial scene; extracting feature vectors of the multi-frame monitoring image, the force touch sensor data and the equipment operation sensor data as multi-modal feature vectors; taking the multi-modal feature vector as a training sample to train a cross-modal model, so that the cross-modal model learns an association relationship among vision, haptic and equipment data; obtaining dynamic industrial scene data, extracting feature vectors, and inputting the feature vectors into the trained cross-modal model to generate scene description information; and outputting an operation decision instruction according to the scene description information so as to control the humanoid robot. Through cross-modal feature fusion and dynamic knowledge reasoning, the task execution precision and environmental adaptability of the humanoid robot in a complex industrial environment can be improved.

The invention relates to the technical field of robot control, and discloses a robot control method, system, equipment and medium based on a multi-modal large model, and the method comprises the steps: collecting the multi-source modal data of a scene where an operation task is located, and carrying out the processing through a machine learning model, obtaining a multi-modal feature, and carrying out the position coding and Transform fusion processing, multi-modal fusion features are obtained, the multi-modal fusion features and the constructed job task knowledge base are input into a large language model to decompose a target job task, a human-in-the-loop mechanism is introduced to optimize a decomposition result, and a sub-task sequence is obtained; according to a subtask type in the subtask sequence, processing the subtask sequence through a visual language action model or a reinforcement learning model, and generating a motion instruction to enable the robot to start an execution process of the target operation task; live-line work tasks are processed through the multi-modal large models LLM, VLA and the like, and the work efficiency of the autonomous distribution network live-line work robot is improved.

The invention discloses a multi-welding-robot collaborative operation system based on digital twinning, and belongs to the technical field of intelligent manufacturing and robot control, the system comprises a physical space module, a virtual space module, a twinning data layer and a control module, and communication connection is established among the physical space module, the virtual space module, the twinning data layer and the control module; the physical space module comprises a plurality of welding robots, a sensor group and a data transmission network; the virtual space module comprises a multi-welding robot twinning body and a workpiece twinning model; the twin data layer is used for connecting the physical space module and the virtual space module; and the control module generates a welding path optimization scheme, a multi-welding-robot collaborative collision avoidance strategy and a welding quality feedback control instruction based on the twin data layer. According to the method, a closed-loop control framework of'physical-virtual 'bidirectional mapping is constructed, so that dynamic collaborative optimization of multi-robot motion trails, welding seam forming and thermal deformation is realized.

The invention belongs to the technical field of robot control, and discloses a high-gear-ratio humanoid robot robust walking control method and system based on potential dynamics self-adaption, and the method comprises the steps: designing a control strategy training frame based on deep reinforcement learning, and carrying out the optimization through an Actor-Critic structure and a PPO algorithm; constructing a potential dynamic adaptive network LDAN, and extracting environment and ontology dynamic parameters through a variational auto-encoder; designing a multi-dimensional reward function; constructing a periodic gait library by using von Mises distribution and motion capture data; gradually introducing terrain disturbance and dynamic change through curriculum type simulation training; the trained strategy and the LDAN module are deployed on the high-gear-ratio driven humanoid robot, the problems that the high-gear-ratio humanoid robot is poor in motion stability, poor in adaptability and insufficient in action expression in a variable environment are solved, and the high-gear-ratio driven humanoid robot has the advantages of being high in robustness, high in natural expressivity and high in migration ability.

The invention relates to the technical field of robot control, and discloses a heavy-load robot motion trail method and system based on machine learning. The method comprises the steps that historical movement track data of the heavy-load robot in a working scene are collected, and the data comprise a joint position sequence, an end effector pose sequence and environment obstacle distribution information; the data is preprocessed, track features are extracted, a space-time correlation matrix is constructed, and the matrix is used for representing the dynamic coupling relation between joint movement and the tail end pose; training a trajectory prediction model containing a long and short-term memory network and an attention mechanism based on the matrix, and generating a collaborative mapping relation between a joint position and a tail end pose; obtaining a current task target pose sequence and an environment constraint condition in real time, and outputting a candidate track set meeting dynamic constraint through a model; and adopting a multi-objective optimization algorithm to screen candidate tracks, generating an optimal track instruction and issuing the optimal track instruction to an execution mechanism. The method adapts to the complex characteristics and variable working conditions of the heavy-load robot, and the track adaptability is improved.

The invention provides a logistics robot control method and system based on artificial intelligence. After the surface material type, the three-dimensional geometric contour and the surface stress distribution data of a to-be-sorted package are obtained through the logistics robot, the hardness grade and the friction coefficient interval of the package are determined, the geometric model of the package is reconstructed, the minimum external cube size is calculated, and the fragile area is recognized. The adsorption or clamping mode and the unfolding angle are adjusted according to the information, the contact point and track of the grabbing action are optimized, and meanwhile a sorting strategy is generated by considering dynamic factors such as the flow speed of a sorting line. In the execution process, action parameters and decision weights are updated through an artificial intelligence algorithm according to the actual effect, and the sorting strategy is continuously optimized. According to the technical scheme provided by the invention, the control efficiency and precision of the logistics robot can be improved.

The invention discloses a robot control method and system for an industrial pipeline, and relates to the technical field of robot control. The method comprises the steps that a flange plate is installed on an end effector of the robot, a binocular vision camera is installed in the center of the front end of the flange plate, and four point laser displacement sensors are symmetrically arranged on the flange plate at different heights; the binocular vision camera collects a pipeline image; the upper computer determines an initial pose control result of the robot according to the pipeline image so as to control the robot to move preliminarily; the four point laser displacement sensors are used for collecting spatial displacement data of the pipeline at different height points after the robot moves preliminarily; and the upper computer determines a target pose control result of the end effector of the robot through the spatial displacement data so as to control the end effector of the robot to carry out pose adjustment. According to the method, accurate control over the axial parallelism of the robot end effector and the pipeline can be achieved, and therefore the pipeline assembling accuracy is improved.

Embodiments of the disclosure provide a solution for action planning. A method includes: generating a sequence of images for an action execution plan based on description information and a reference image related to an environment with an action executor located, the description information describing the action execution plan to be executed by the action executor; extracting a sequence of visual feature representations from the sequence of images, respectively; and for a respective visual feature representation of the sequence of visual feature representations, determining control information for controlling an action to be executed by the action executor in the environment to complete the action execution plan at least based on the respective visual feature representation, a reference visual feature representation prior to the respective visual feature representation in the sequence and observed information of the action executor in the environment during execution of a reference action prior to the action.

The invention provides a double-arm picking robot system and a control method, a robot body is mounted on a moving module through a rotary lifting waist and hip joint, and a storage module is arranged on the moving module; the moving module can drive the robot body and the storage module to move; a right multi-degree-of-freedom mechanical arm and a left multi-degree-of-freedom mechanical arm are arranged on the two sides of the robot body, a right arm tail end picking gripper is arranged at the right end of the right multi-degree-of-freedom mechanical arm, and a left arm tail end picking gripper is arranged at the tail end of the left multi-degree-of-freedom mechanical arm; an image acquisition module is arranged at the top of the robot body, a laser radar module is arranged on the moving module, and the image acquisition module and the laser radar module perform data transmission with the robot control module. Two arms are adopted for cooperative work, the structure of the humanoid mechanical arm is more suitable for the picking scene, and cooperation can be more effectively carried out.

The invention discloses a control method and device for a mounting and dismounting integrated robot of a sound barrier, and relates to the field of robot control. According to the method, a digital twinborn model and a dynamic environment model are constructed according to original sensor data, and a current environment risk value is determined; a mounting or dismounting sequence is generated based on a sound barrier topology rule, and a motion path and a mechanical arm motion track are generated according to the digital twin model, the dynamic environment model and the mounting or dismounting sequence; rehearsing the motion path and the motion track of the mechanical arm in the digital twin model, and when a rehearsing result meets a preset requirement, generating an initial instruction based on the motion path and the motion track of the mechanical arm; the current clamping force is determined through an impedance control algorithm, the current hoisting parameter is determined according to the load weight, the initial instruction is adjusted according to the current clamping force, the current hoisting parameter and the current environment risk value to obtain a control instruction, and the control instruction is executed. By implementing the technical scheme provided by the invention, the mounting and dismounting efficiency is improved, and the operation safety is enhanced.

The invention relates to the technical field of artificial intelligence welding, and discloses an automatic welding robot based on artificial intelligence and a welding system thereof. The system comprises a welding feature acquisition module, a process parameter generation module, a real-time regulation and control module and a motion planning module. The welding characteristic acquisition module is used for acquiring three-dimensional contour data, material component information and welding seam geometric parameters of a workpiece to be welded, and generating a welding characteristic spectrum through characteristic fusion; a process parameter generation module retrieves a matching template from a welding knowledge base according to the parameters, and outputs a reference welding process parameter combination in combination with an environment temperature and humidity compensation coefficient; the real-time regulation and control module dynamically corrects the reference parameters to generate an optimization instruction according to the molten pool form, thermal radiation distribution and electric arc voiceprint characteristics in the welding process; and the motion planning module calculates a motion track, attitude parameters and a speed curve of the welding execution mechanism according to the optimization instruction to form a robot control instruction set. The system can improve the intelligent level of welding, guarantees stable welding quality, and is suitable for welding scenes of multiple manufacturing industries.

The invention discloses a multi-robot task conflict resolution and dynamic scheduling system and method, and belongs to the technical field of robot control. The system comprises a perception detection layer which is used for acquiring operation information of a plurality of robots and a space-time semantic map of a to-be-executed task executed by the robots, and generating conflict information under the condition that at least two target robots are detected to conflict; the decision scheduling layer is used for determining task execution priorities of the to-be-executed tasks according to the conflict information and priority factors and value functions of the to-be-executed tasks corresponding to the target robots so as to rearrange the to-be-executed tasks corresponding to the target robots and generate a task sequence; and the execution control layer is used for generating a dynamic scheduling instruction according to the task sequence, the space-time semantic map and the operation information of the target robot and issuing the dynamic scheduling instruction to the corresponding target robot. The system can flexibly cope with a dynamic scheduling scene of multiple robots in real time.

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 technical field of robot control, and particularly discloses a robot indoor moving path planning method and system. An environment sensing module collects environment data through a multi-mode sensor array and constructs a three-dimensional semantic map; the dynamic decision-making module generates a candidate path set based on the three-dimensional semantic map data output by the environment perception module, and transmits a decision-making result to the path optimization module through a priority arbitration mechanism; the path optimization module performs multi-objective optimization according to candidate paths of the robot kinematics constraint and dynamic decision module; the execution control module is used for converting an optimized path into a bottom layer driving instruction through kinematics calculation and feeding back an execution state to the dynamic decision-making module in real time to form closed-loop control. A dynamic environment modeling mechanism is adopted, through variable resolution perception and semantic map construction, a layered decision-making framework and multi-target collaborative optimization are adopted, and the dynamic environment modeling mechanism is optimized; and the adaptive control system is selected to significantly improve the environmental adaptability and reliability of the system.

The invention relates to the technical field of robot control, in particular to a force control method and system based on an impedance model for a rope-driven robot. Comprising the steps of system dynamics modeling and parameter identification, real-time axial compression sensing and model updating, Cartesian space impedance model design, adaptive impedance parameter adjustment, inner ring position control based on feedback linearization, force error compensation and expected trajectory generation, real-time control circulation and anti-interference processing and experimental verification and iterative optimization. According to the method, through real-time axial compression sensing and equivalent Young modulus dynamic updating, the problem of model mismatch caused by material rigidity sudden change in the phase change process of the hyperelastic material is solved. Specifically, a traditional method depends on static material parameter calibration, and real-time compensation of the nonlinear characteristic of the material is achieved through online estimation of the axial compression amount of the main beam and iterative correction of the Young modulus.

The invention provides a robot self-adaptive grabbing and assembly track planning method for complex working conditions, and relates to the technical field of robot control, and the method comprises the steps: building a workpiece three-dimensional feature data model, constructing a deep reinforcement learning network comprising a working condition recognition layer, a strategy generation layer and a reward calculation layer, and generating an optimal grabbing strategy. And an assembly task is decomposed into three stages of pre-alignment, initial assembly and accurate assembly, so that parameter dynamic optimization is realized. The manipulator can adapt to complex working conditions such as workpiece pose uncertainty, shielding and deformation, and the grabbing precision and the assembly success rate are improved.

The invention discloses a robot hierarchical reinforcement learning variable impedance control method based on vision and touch, and belongs to the field of robot control. According to the method, an upper-layer planning system and a lower-layer variable impedance control system are included, the upper-layer planning system fuses information from a visual sensor and a tactile sensor, feature coding is conducted on a visual image through a variational self-coding model, tactile feedback and mechanical arm state information are combined, and an action strategy adapting to the current environment and corresponding impedance parameters are formulated. And the lower-layer variable impedance control system realizes variable impedance control of the robot based on a depth kinematics and dynamics model, and allows the robot to dynamically adjust the motion and contact force according to the sensed information when interacting with the environment, thereby improving the adaptability to the environment change and the task execution efficiency. According to the method, the success rate and execution efficiency of the robot for executing the complex assembly task in the unstructured environment are remarkably improved, and high robustness is shown.

The invention relates to the technical field of robot intelligent control, in particular to a concrete construction digital intelligent robot control system. Comprising a concrete data acquisition module, a construction path planning module, a robot control module, a quality monitoring module and an interactive feedback module. The data acquisition module acquires information such as concrete height and humidity of a construction area through a sensor; the path planning module generates an optimal construction path by using an artificial bee colony algorithm; the robot control module accurately executes a path instruction through sliding mode control and a fuzzy adaptive algorithm; the quality monitoring module adopts a support vector machine to evaluate construction quality and generate a report; and the interaction feedback module realizes path adjustment and information interaction through a visual page. Through combination of digital intelligence and algorithm optimization, integration of intelligent planning of a construction path, precise control of the robot and quality monitoring and evaluation is realized, and the intelligent level and engineering quality of concrete construction are remarkably improved.

The invention provides an industrial robot multi-station intelligent sorting and stacking method based on visual guidance, which relates to the technical field of robot control, and comprises the following steps: determining a target station by acquiring multi-station real-time state information, calculating an optimal grabbing time window by using a graph neural network state predictor, and obtaining a target grabbing time window; and the placement strategy is dynamically adjusted based on the visual image and the force sensing data in the article stacking process. According to the multi-station intelligent stacking system, the sorting efficiency in a multi-station scene can be improved, the grabbing precision of dynamic objects is enhanced, and stable and reliable intelligent stacking operation is achieved.

The invention aims to provide a multi-robot cooperative building operation method and system, and belongs to the technical field of intelligent building robot cooperative control, and the method comprises the steps: firstly obtaining a building operation task and a building three-dimensional model, decomposing the task into a plurality of sub-tasks based on the model, and generating an initial task allocation scheme according to the operation type and the spatial position of the subtask, and allocating the initial task allocation scheme to a plurality of robots. In the operation process, the operation state and environment data of each robot are collected in real time, and a dynamic conflict detection matrix is constructed. And when a path conflict or a resource conflict is detected, dynamically adjusting the conflict task according to a preset priority rule, generating an updated task allocation instruction, sending the updated task allocation instruction to the corresponding robot, and controlling the corresponding robot to execute the adjusted sub-task. According to the invention, through a dynamic conflict detection and intelligent adjustment mechanism, the problem of path and resource conflict of multiple robots in a dynamic building construction environment is effectively solved, and the construction efficiency and safety are improved.

The invention discloses a building robot task planning and skill learning method and system, and relates to the technical field of intelligent construction and building robot control, and the method comprises the steps: receiving construction target description and construction environment information through a multi-modal task-skill collaborative generation model; and generating a structured semantic vector and reasoning a construction planning graph containing a plurality of subtasks. And collecting operation behavior parameters of the building robot in each subtask according to the construction planning graph, constructing an operation sample, and inputting the operation sample into the multi-modal task-skill collaborative generation model for skill learning and action instruction generation. And in the construction execution process, various sensing data are fused for real-time judgment, and if the task execution state is inconsistent with the planning state, the multi-mode task-skill collaborative generation model adjusts an instruction based on the latest state and outputs a reasoning chain. According to the method, the deployment efficiency and task adaptability of the robot system in actual construction are improved, and a foundation is laid for man-machine cooperation and unmanned construction in the field.

The invention discloses a robot control method based on a multi-modal large model, solves the problems of generalization and adaptability of an existing robot control method based on a large model, and belongs to the field of robot control. The method comprises the steps that a multi-modal planning large model deconstructs a subtask sequence according to the requirements of a user and historical behavior records, and an instruction generation model generates a corresponding control instruction according to the deconstructed subtask sequence and stores the control instruction into a pointer drawing queue; the planning pointer manages the control instructions in the planning pointer queue, and the normally executed control instructions are moved into a historical behavior record; during training, planning control is judged through man-machine interaction, planning content with a good execution effect is stored in an excellent data set, and the excellent data set is utilized to perform fine tuning optimization on the multi-modal planning model and the instruction generation model; and controlling the robot by using the two optimized models and the planning pointer.

The invention provides a robot control instruction analysis method and system fusing continuous instructions, and relates to the technical field of electric digital data processing, and the method comprises the steps: carrying out the quantitative evaluation of the instruction execution quality and resource consumption according to prediction data, and generating a quality score and a consumption estimation; constructing a multi-objective optimization framework based on the quality score and the consumption estimation, and determining a candidate path set; switching cost and execution time are calculated according to the candidate path set, and optimized path schemes meeting constraint conditions are screened; the instruction execution priority and the resource allocation proportion are adjusted in combination with the real-time environment data, and a scheduling strategy is generated. According to the method, environment changes and task quality are monitored in real time, instruction priorities and resource allocation are dynamically adjusted, when execution efficiency is lower than a threshold value, online updating of a prediction model is triggered, model parameters are finely adjusted to adapt to new conditions, and through continuous optimization and monitoring, the execution efficiency and task quality of an instruction sequence can be improved, and the execution efficiency is improved. And intelligent instruction scheduling and resource allocation are realized.

The invention relates to the technical field of robot training. The robot self-adaptive training method based on reinforcement learning comprises the steps that task sub-target information is generated through a high-level strategy network, the task sub-target information is input into a low-level execution network, an action control instruction is generated according to the task sub-target information, interaction feedback information is collected in the execution process, and the action control instruction is sent to a robot through a robot. Calculating a reward value according to the interaction feedback information, carrying out association processing on the reward value and the scene complexity parameter, executing a dynamic reward shaping operation, generating an adjusted reward signal, generating a strategy model optimized by meta-learning based on the adjusted reward signal, loading the strategy model in a simulation environment, and carrying out dynamic reward shaping. A target strategy model optimized through simulation training is generated, the target strategy model is loaded to the robot, and the robot is controlled to execute task operation in the actual interaction scene. The method has the effect of realizing adaptive task learning of the robot in a multi-interaction scene.

The invention relates to the technical field of robot control, in particular to an energy consumption optimization and automatic charging control system for an intelligent pet robot. The system comprises a robot data acquisition module, an energy consumption state evaluation module, an energy efficiency optimization module, an automatic charging scheduling module and a battery health management module which are in communication connection in sequence. The system is driven by multi-dimensional sensing data, energy consumption characteristics are extracted in combination with potential energy field mapping and sparse modeling, and an energy consumption influence map is constructed; joint optimization of the path and the servo strategy is realized by using a quantum behavior particle swarm algorithm; scheduling a charging path through a graph attention network; and the battery health state is dynamically predicted based on an incremental learning algorithm, and the operation efficiency, the cruising ability and the system intelligence of the robot are improved. According to the method, a closed-loop control process is constructed from energy consumption evaluation to energy efficiency optimization, automatic charging and battery health management, good system integration and intelligent response capability are achieved, and the cruising ability and operation stability of the pet robot in a complex application scene are remarkably improved.

The invention discloses a finite time tracking adaptive control method for a mechanical arm based on a neural network, and relates to the technical field of industrial robot control. The method comprises the following steps: constructing a kinetic model of the mechanical arm, obtaining an existence form of an unknown nonlinear term in the model, and defining a joint position tracking error and an error change rate of the mechanical arm; constructing a sliding mode dynamic equation based on the tracking error and the error change rate; a BP neural network is adopted to approach the unknown nonlinear dynamic state of the mechanical arm, and the mapping relation between a network input vector and an output vector is determined; combining a sliding mode dynamic equation with BP neural network output, and designing a finite time control method including adaptive gain; and a self-adaptive updating method of BP network weight and sliding mode gain is deduced, so that the tracking error of the mechanical arm is converged to a zero neighborhood within preset time, and self-adaptive control of the mechanical arm is completed. According to the method, high-precision trajectory tracking within the preset time can be realized, and the anti-interference capability is high.