6751 results about "Motor control" patented technology

The method can include: recording a neural state, determining a latent array, and determining an output based on the latent array. In variants, the method can function to enable a subject to use neural signals to navigate within a high-dimensional concept space to control an output (e.g., image generation, motor control, communication, etc.).

The invention discloses a quadruped robot robust motion control method based on deep reinforcement learning, and belongs to the technical field of robot motion control, and the method comprises the steps: constructing a deep reinforcement learning model which comprises a state estimation network, a strategy network and a value network; interaction between the quadruped robot and the simulation environment is carried out, and standard observation information, historical observation information and privileged observation information of the quadruped robot at all moments are obtained; inputting the standard observation information, the historical observation information and the privilege observation information of the moment into a deep reinforcement learning model, and training based on a total loss function until convergence is carried out to obtain a trained deep reinforcement learning model; inputting standard observation information and historical observation information at corresponding moments in an actual scene into the trained deep reinforcement learning model to obtain output features of a corresponding strategy network; and the target position of each joint motor is calculated to complete the motion control of the quadruped robot. And efficient training and robust motion on various complex and unstructured terrains can be realized.

The present disclosure provides a system for generating motor control commands for a humanoid robot, comprising an alpha model with over 1 billion parameters that processes visual observations and language instructions at a first frequency to generate contextual embeddings, and a beta model operating at a higher second frequency. The beta model includes an embodiment-specific state encoder projecting robot state information into a shared embedding space, a diffusion transformer module generating denoised action sequences through iterative flow-matching that cross-attends to the alpha model's contextual embeddings, and an embodiment-specific action decoder converting denoised sequences into motor control commands. The beta model generates action chunks comprising future action sequences over a predetermined time horizon in a single inference step, with the complete system having less than 5 billion parameters.

The invention relates to the technical field of two-dimensional motion control, and discloses a high-precision two-dimensional motion error prediction compensation iteration method. The method comprises the following steps: on an operation interface of a motion control system, generating a motion track overview containing a target position sequence and corresponding expected motion parameters according to parameters set by a user, and connecting the motion track overview with an actual motion execution device; measuring the deviation between the actual position and the target position of the motion platform at the current sampling moment, and calculating a motion error vector; constructing a prediction model based on the motion error vector and the expected motion parameter, and predicting a two-dimensional motion error at a future moment through iterative optimization; generating a compensation control instruction according to the prediction result and applying the compensation control instruction to the motion execution device; and monitoring the compensated motion state in real time, updating the prediction model and outputting a compensation log. According to the method, by predicting the error in advance and iteratively optimizing compensation, the two-dimensional motion control precision can be effectively improved, the real-time performance and adaptability of compensation are enhanced, and the method is suitable for a high-precision motion control scene.

The invention relates to the technical field of warehouse logistics equipment, and discloses an omni-directional intelligent mobile forklift control system. The system comprises a dynamic path planning module, an omnidirectional motion control module, a load stability monitoring module, an environment adaptive adjustment module and an intelligent obstacle avoidance decision module. The dynamic path planning module collects storage environment multi-dimensional spatial data and constructs a dynamic environment topological map; the omni-directional motion control module analyzes driving wheel parameters based on the map and generates an omni-directional motion composite motion instruction set; the load stability monitoring module collects a fork pressure distribution matrix and a cargo gravity center offset according to an instruction set, and calculates a stability compensation parameter; the environment adaptive adjustment module fuses illumination and slope data to generate a motion control constraint condition; and the intelligent obstacle avoidance decision module predicts a dynamic obstacle trajectory, outputs an obstacle avoidance path re-planning instruction and updates a map. The system can meet the requirements of modern intelligent warehousing.

The invention relates to the technical field of micro-nano motion control, and discloses a self-adaptive control method of a micro-nano high-precision motion platform. The method comprises the following steps: acquiring real-time pose feedback data and target trajectory data of a motion platform, and extracting dynamic response features; calling the trained motion feature analysis network to perform multi-modal feature separation, and generating a platform pose feature set; based on the set, performing space-time coupling analysis on the working environment parameters through an environment disturbance perception model to obtain a fusion result containing mechanical deformation characteristics and environment disturbance characteristics; inputting a fusion result into a dynamic compensation model to calculate a track correction amount, and outputting a driving compensation instruction; and calibrating the target trajectory data in real time according to the compensation instruction, and generating an actual control signal. According to the method, through multi-modal feature analysis, space-time coupling perception and dynamic compensation, the control precision and stability of the motion platform in a complex environment are improved, and the method is suitable for a micro-nano high-precision motion control scene.

The invention discloses a high-precision laser positioning unpacking path planning system, and belongs to the technical field of robot automatic control and industrial automation. The system comprises a data synchronization module used for multi-sensor hardware synchronization and data alignment; the high-precision positioning module is used for outputting a precise pose based on environment skeleton characteristics and sliding window optimization; the semantic map construction module is used for fusing vision and laser data to generate a dynamic semantic grid map; the global path planning module is used for planning a smooth path in the skeleton channel by utilizing a mixed potential field improved A * algorithm; the motion control module is used for realizing closed-loop motion control and safety monitoring through model prediction and tracking; and the operation execution module is used for finishing millimeter-level precise stopping of an operation point by adopting visual servo and triggering unpacking operation. According to the method, the positioning robustness under dynamic shielding is improved through the environmental skeleton features, the safety and the high efficiency of the path are ensured by utilizing semantic understanding and intelligent planning, and the full-process automation from navigation to precise operation is realized.

The invention relates to the technical field of motor control, and discloses a permanent magnet synchronous motor control system and method for an electric drive hydraulic source, and the system comprises a data collection module which is used for collecting the state parameters of the electric drive hydraulic source and the operation data of a permanent magnet synchronous motor in real time through multiple sensors; the sensor redundancy and fault-tolerant module is used for performing parallel sampling through hardware dual-redundancy configuration and dual sensors; the self-adaptive fuzzy logic control module is used for dynamically adjusting a rotating speed instruction and a torque instruction of the permanent magnet synchronous motor based on the collected state parameters; a data driving optimization module; and a digital twinning prediction maintenance module. A self-adaptive fuzzy logic control module is used for dynamically generating a motor rotating speed and torque instruction based on parameters such as real-time pressure, flow deviation and change rate, a fuzzy logic algorithm is used for processing a nonlinear time-varying load and quick response, a self-adaptive fuzzy logic control rule weight and a power conversion circuit parameter are optimized through a reinforcement learning algorithm, and the control precision is improved. And dynamic self-optimization of permanent magnet synchronous motor control is realized.

The invention relates to the technical field of robot joint control, and discloses a robot dog mechanical joint control method. The method comprises the following steps: acquiring a whole-body joint real-time sensing data set which covers the torque reading of a multi-axial force sensor, the angle data of a joint encoder and the body attitude data of an inertial measurement unit; motion state compensation is carried out on the sensing data based on a dynamic window mechanism, and a dynamic compensation matrix containing kinematics compensation parameters is generated; and inputting the multi-modal motion control data set into a gradient descent model under space-time constraint to complete multi-modal motion feature fusion so as to obtain a fusion motion control data set. Joint behavior abnormity is detected through a Lyapunov stability analysis algorithm, and abnormal nodes are identified; and iteratively optimizing control parameters through a fuzzy PID optimization algorithm to generate an optimized control parameter set, finally constructing a three-dimensional virtual motion space model, and establishing a dynamic mapping relationship between a virtual track and an actual mechanism.

The invention relates to the technical field of flight simulation control and precision measurement, provides a precise positioning method, system and device for a three-dimensional adjustable connecting piece of a flight simulator and a storage medium, and solves the problems of flight simulation control and precision measurement. The method comprises the following steps: acquiring the real-time stroke length of a motion control rod, rotation angle change data and a connecting piece vibration image, outputting a displacement prediction parameter through a platform kinematics model, and generating three-dimensional point cloud data; performing sub-pixel identification and distortion compensation on the image through a mark point identification algorithm to generate a three-dimensional visual coordinate; fusing the point cloud and the visual data, and processing through a Kalman filter to obtain a space pose offset; finally, a displacement compensation instruction is generated, the pose of the connecting piece is adjusted through a closed-loop controller, and accurate positioning of the connecting piece relative to a flight target reference coordinate system is achieved. According to the invention, the accuracy and stability of the pose control of the connecting piece in the dynamic vibration environment are improved.

The invention relates to the technical field of modern precision machining and optical scanning, and discloses a motion control method and system of a laser galvanometer. The method comprises the steps that a real-time position signal is collected and preprocessed, and a filtered position signal is obtained; the deviation between the trajectory model and a preset trajectory model is calculated, when the deviation exceeds a threshold value, the trajectory model is identified as a complex trajectory segment, offset vector data is generated, and an offset compensation demand signal is generated accordingly; driving an instruction device in combination with historical data to obtain compensation parameters; when the deviation is lower than an updating threshold value, updating the trajectory model; a stable control signal is generated based on the optimization model and filtering signal prediction, and the environmental interference is calibrated and the signal smoothness is enhanced through the stable control signal; and finally, comparing the enhanced signal with the optimization model, and repeating the compensation process when the deviation exceeds the standard to obtain a final stable control signal. According to the method, through multi-level deviation processing and iterative optimization, the control precision and the anti-interference performance of the laser galvanometer in complex track movement are improved.

The invention discloses a humanoid robot motion control method based on gait planning and reinforcement learning, and belongs to the technical field of humanoid robot motion control. According to the method, the problems of low convergence speed in a training process and poor stability and reliability of a trained model of an existing reinforcement learning method are solved. According to the method, a reinforcement learning model runs in parallel under multiple environment instances, in the running process, a target foothold is generated in real time based on feedback state information, the generated reference foothold and a mass center track serve as an action reference or reward target of a controller, and a continuous and dynamically-adjusted foot end track is constructed in combination with gait phase information. And after the reinforcement learning model is trained by utilizing all the collected trajectory data, motion control can be performed on the humanoid robot through the trained reinforcement learning model, so that the robot realizes stable walking similar to human beings under the condition of not depending on a predefined template. The method can be applied to motion control of the humanoid robot.

The invention relates to the technical field of robot control and somatosensory interaction, and particularly discloses a knee joint exoskeleton robot control system based on somatosensory interaction. The system comprises a somatosensory signal acquisition and fusion module, a personalized dynamic model construction module, an adaptive intention decision module, a variable impedance compliance control module and a rehabilitation process evaluation and parameter self-tuning module. Through cooperative work of the modules, precise recognition of the motion intention of a wearer, online construction of an individualized biomechanical model and flexible and adaptive motion control are achieved, control parameters can be automatically adjusted according to the rehabilitation process, and therefore the flexibility and individuation level of man-machine interaction and the effectiveness of rehabilitation training are improved.

The invention provides a robot arm motion control strategy network training method and device based on deep reinforcement learning, and relates to the technical field of sensors and robots. The method comprises the following steps: in a deep reinforcement learning training environment, obtaining a jacobian sub-matrix corresponding to a robot arm; calculating an operability index under the current attitude based on a product between the Jacobi sub-matrix and a transpose matrix of the Jacobi sub-matrix; inputting the operability index into the activation function to obtain a penalty factor; and scaling the difference value between the current action output by the policy network and the action at the last moment based on the penalty factor to obtain a penalty term, and using the penalty term as a part of a reward function for training the policy network. According to the method, the self-adaptive punishment mechanism based on the operability is introduced, so that the strategy network can recognize and actively avoid the singular postures in the training process, and the stability of robot arm motion control is improved.

The invention provides an exoskeleton man-machine motion intention cooperative control method and device and a storage medium, and relates to the technical field of robot motion control signal processing. According to the method, signal phase differences are acquired through energy buttons at human joints, and an original phase difference sequence is formed and preprocessed; calculating a cross correlation coefficient of the target and the associated joint based on the preprocessed sequence, and constructing a motion feature set in combination with the joint speed and acceleration; constructing a prediction model by using a long-short-term memory network, and outputting motion intention and joint position prediction information through multi-feature fusion training; and analyzing the prediction information by adopting a second-order linear impedance model, generating a driving torque instruction and sending the driving torque instruction to the servo motor for execution. According to the method, signal phase difference analysis, LSTM prediction and impedance control are fused, the problems of response lag and poor adaptability of a traditional exoskeleton are solved, the control real-time performance, accuracy and cooperative fluency are improved, and the use requirements of different users in different scenes are met.

The utility model relates to the technical field of precise driving and positioning, and discloses a linear motor type electric screw which comprises a screw rod, a shell, a driving mechanism, a mechanism seat and a threaded pipe. The driving mechanism is arranged in a cavity defined by the shell and the mechanism base. The driving mechanism comprises a flexible hinge, piezoelectric ceramics and a pre-tightening spring. The flexible hinge comprises a first clamping jaw and a second clamping jaw. And the first clamping jaw and the second clamping jaw provide rotating force for the screw rod under the action of the piezoelectric ceramics. And the pre-tightening springs are used for applying pre-tightening force between the first clamping jaw and the screw rod and between the second clamping jaw and the screw rod. The threaded pipe is used for supporting the threaded rod and being connected with an external mechanism. According to the utility model, the piezoelectric ceramic is adopted for driving and the flexible hinge is combined, so that precise linear motion control is realized, and the touch-free adjusting device has the characteristics of high resolution, high precision and compact structure, and realizes touch-free adjustment of the optical adjusting bracket which is difficult to touch. A plurality of linear motor type electric screws are combined for use, so that two-dimensional or three-dimensional motion control can be realized.

The invention provides a sensorless motor stator temperature protection method, device and equipment, and relates to the technical field of motor control, and the protection method comprises the steps: obtaining the current voltage, current and rotating speed of a motor when the motor in a motor control system is in a running state, determining a corresponding temperature rise coefficient and a heat balance temperature according to the current voltage, the current current and the current rotating speed; obtaining a current estimated temperature according to the current voltage, the current current and the current rotating speed; determining a current predicted temperature according to the current estimated temperature, the heat balance temperature, the last estimated temperature and the corresponding temperature rise coefficient; and comparing the current predicted temperature with a preset protection threshold value, and performing temperature protection control on the motor according to a comparison result. A temperature sensor does not need to be installed in the motor, so that the installation difficulty and cost are reduced, the reliability and durability of the system are improved, and the risk that the sensor breaks down or is damaged is avoided.

The invention discloses a focusing control method and related equipment, and the method comprises the steps: synchronously collecting the data of a multi-modal sensor, extracting features and parameters, inputting a multi-modal fusion model subjected to joint optimization to generate a motor control instruction, and achieving the focusing through the hierarchical control of rough adjustment, fine adjustment and dynamic tracking. The dynamic weight fusion network deploys the weight of each modal data according to the environmental sensitivity coefficient, and combines the historical memory and trajectory pre-judgment of the alignment trajectory prediction network to reduce invalid calculation and repeated search and greatly improve the focusing speed; the multi-mode sensor works cooperatively to replace a special hardware function, so that the cost and the power consumption are reduced; the dynamic weight fusion network can adjust the modal confidence according to the environment and enhance the anti-interference capability in the complex light environment; the alignment trajectory prediction network and the fuzzy self-adaptive PID controller are in hierarchical cooperation, the problem of erroneous judgment of a complex scene is solved, and the micro-distance and dynamic tracking precision is improved; the model is subjected to pruning, distillation and quantitative optimization, light weight is achieved, and low-cost equipment is adapted.

The invention relates to the technical field of quadruped robot control, and discloses a quadruped robot anti-disturbance motion control method based on a cost weight adaptive mechanism, which comprises the following steps: constructing a quadruped robot state equation based on a single rigid body dynamic model, a model prediction control problem with cost weight, friction cone constraint and gait constraint is formed through discretization; a cost weight self-adaptive mechanism based on disturbance observation is introduced on the basis of a model prediction control framework, the disturbance intensity is estimated by taking the attitude error of the fuselage and the corresponding change rate as disturbance observation values, and the cost weight corresponding to the attitude in the model prediction control problem is dynamically adjusted; by solving a model prediction control problem, an expected ground reaction force and a target state are calculated in real time, and motion control of the quadruped robot is realized in combination with a gait planner and a joint controller. According to the invention, the motion stability and anti-disturbance performance of the quadruped robot under external disturbance are improved based on a cost weight adaptive mechanism of disturbance observation.

The invention discloses a degradation trend prediction method for a brushless direct current motor, and relates to the technical field of motor control, current, temperature and vibration signals of motor operation are collected, a characteristic signal transmission path model of a winding, a bearing and magnetic steel is constructed, and the degradation trend of the brushless direct current motor is predicted by analyzing the reflection characteristics and attenuation conditions of degradation signals of all parts. Extracting characteristic quantities of mutual influence among current, temperature and vibration; according to the method, a multi-component characteristic signal transmission path model is constructed, degradation signals are regarded as fluctuation quantity transmitted and reflected among different components, mutual influence of current, temperature and vibration signals is comprehensively analyzed, false characteristics are separated in combination with heat transfer lag characteristics and cooperative change critical points, accurate extraction of real degradation characteristics is realized, and the accuracy of the degradation characteristic extraction is improved. The problem that weak degradation signals are covered by interference is solved.

The invention relates to a large-span bridge flutter critical wind speed prediction method based on an L-P perturbation method. The method comprises the following steps that S1, physical characteristics and aerodynamic parameters of a bridge are obtained through a wind tunnel test to serve as basic data; s2, establishing a bridge motion control matrix equation based on modal displacement; s3, deriving an explicit solution of the modal frequency, the damping ratio and the modal characteristic of each modal branch based on an L-P perturbation method; s4, iteratively solving the modal frequency, directly solving the damping ratio, the amplitude ratio and the phase difference on the basis, observing the change of the modal damping ratio xi in each direction along with the wind speed U, and determining the flutter critical wind speed; the problems that an explicit closed solution of an existing three-degree-of-freedom flutter starting condition needs to carry out multiple times of iterative calculation on modal frequency, damping ratio and vibration mode, the influence of structural parameters and pneumatic parameters on pneumatic instability is difficult to clearly explain, and the flutter critical wind speed of a large-span bridge cannot be well predicted are solved.

The invention provides a cleaning robot multi-mode motion control system, method and equipment and a medium, and belongs to the technical field of intelligent robot control. The system comprises a multi-modal data acquisition module used for performing multi-source synchronous acquisition on an environment image, distance information, a voice signal and a collision signal and outputting original sensing data; the data fusion and processing module is used for receiving the original sensing data, sequentially executing space-time alignment correction, multi-source feature fusion and instruction semantic analysis, and outputting an environment sensing result and a structured control instruction; the motion planning and decision-making module is used for executing global path planning, dynamic obstacle trajectory prediction and motion parameter decision-making based on the environment perception result and the structured control instruction, and outputting a motion control instruction containing a motion mode, a speed and an angle; and the motion control execution module is used for receiving the motion control instruction, converting the motion control instruction into an execution mechanism driving signal, realizing closed-loop control through real-time state feedback and outputting a motion execution result.

The invention discloses a real-time whole-body remote operation control system for a humanoid robot, and the system comprises a motion instruction generation subsystem which is located at an operation end and is used for receiving and analyzing input instructions of various modes, and carrying out the unified conversion and complementation of the input instructions into a whole-body joint motion instruction which is suitable for a skeleton structure of a target humanoid robot; the body motion control subsystem is located at the robot end and used for receiving the whole-body motion instruction, adaptively generating a whole-body motion coordination control signal capable of ensuring the stability of the robot in combination with the state of the robot and environment information, and driving and executing the whole-body motion coordination control signal; through cooperation of the two subsystems, the system provides a dual guarantee path for realizing whole-body teleoperation: a complete whole-body instruction can be generated based on the instruction generation subsystem, and whole-body motion can be adaptively generated by the body motion control system according to a received local instruction; and the robustness, the flexibility and the reliability of the system under different conditions are ensured.

The invention discloses a joint motor control method and system based on an angle sensor, a medium and a product, and relates to the field of general control or adjustment systems.The method comprises the steps that real-time angle data and temperature data of the angle sensor are collected, and a real-time data packet is generated; determining a rapid compensation parameter corresponding to the temperature value, performing preliminary compensation on the angle value to obtain a preliminary compensation angle value, and generating a motor control parameter to control a joint motor to operate; storing a plurality of continuous real-time data packets into a compensation cache region; when the number of the data packets in the compensation cache region reaches a preset number threshold value, temperature change features and angle drift features are extracted, and accurate compensation parameters are calculated; and performing secondary compensation on the preliminary compensation angle value based on the accurate compensation parameter to obtain an accurate compensation angle value, and updating the motor control parameter based on the accurate compensation angle value. According to the invention, response delay caused by a compensation algorithm can be reduced.

The invention discloses an embedded motor torque ripple real-time compensation system based on deep learning, and relates to the technical field of motor control and artificial intelligence crossing, the system comprises a hardware data acquisition module, a signal feature extraction module, a deep learning compensation model module, an embedded real-time compensation module and a compensation effect verification module; a motor position and a current signal are acquired in real time through the hardware data acquisition module, dynamic analysis and multi-dimensional feature construction are performed on torque ripple related features by combining the signal feature extraction module, and the deep learning compensation model module learns a fluctuation rule in historical data by using a space-time sequence model to generate a high-precision compensation current. The model adapts to an embedded environment through lightweight design, real-time deployment and dynamic optimization of a compensation algorithm are achieved, compared with traditional PID control, the scheme can adapt to changes of motor operation conditions, the influence of torque fluctuation on system stability is reduced, and the smoothness and efficiency of motor operation are improved.

The invention relates to the technical field of motor control and sensors, and discloses a displacement error dynamic compensation method, device and equipment and a medium, and the method comprises the steps: obtaining the temperature data and original displacement data of a plurality of temperature measurement points in a moving part and an environment, calling a pre-built nonlinear mapping relation between a temperature gradient and a displacement deviation, and calculating the displacement error of the moving part; based on the nonlinear mapping relation, adaptive filtering is carried out on the temperature data and the original displacement data, a trend component representing temperature drift is separated out, displacement data without the trend component is obtained, and a dynamic compensation instruction is generated based on the trend component and the nonlinear mapping relation; and adjusting the displacement data of which the trend component is removed and outputting compensation displacement data. According to the method, the nonlinear mapping model of the temperature gradient and the displacement deviation is constructed, the drift trend is extracted in combination with multi-point temperature sensing and self-adaptive filtering, the compensation instruction is dynamically generated, accurate real-time correction of the displacement error is achieved, and the responsiveness and accuracy of compensation are improved.

The invention relates to the field of energy storage loop monitoring, and particularly discloses an innovative energy storage loop system for an operating mechanism of a medium-voltage switch cabinet, which realizes soft start and contactless control of an energy storage motor by introducing a dynamic motor control circuit. The multistage stroke detection device replaces a mechanical limit switch, provides real-time and continuous position monitoring for the energy storage mechanism, and thoroughly eliminates feedback lag and inaccuracy. The core energy storage state analysis unit can identify abnormal working conditions in the energy storage process in real time through intelligent acquisition, analysis and decision making of signals. Besides, the power supply unit provides a stable operation basis, and the bidirectional communication interface ensures information interaction between the system and the external environment, so that an intelligent energy storage ecological system with controllable closed loop, feedback of state and early warning of fault is jointly constructed, and the response speed and the operation stability of the medium-voltage switch cabinet operating mechanism are comprehensively improved.

The invention belongs to the technical field of legged robot control, and provides a quadruped robot robust motion control method and system based on joint learning, and the method comprises the steps: obtaining the body observation information and privileged observation information of a quadruped robot; extracting key features in the obtained privileged observation information by adopting a privileged encoder to obtain a first potential feature vector; obtaining a second potential feature vector matched with the first potential feature vector based on the obtained historical ontology observation information and an adaptive network; constructing a joint loss function according to the obtained first potential feature vector and the second potential feature vector, and performing strategy network updating training by taking the minimum joint loss function as a target; and robust motion control of the quadruped robot is completed according to the trained strategy network.