19 results about "Iterative learning algorithm" patented technology

The invention discloses a robot compliance force control interaction method, system and device for a collaborative sewing scene and a storage medium. The method comprises the steps of setting a preset period of a contact force track, a speed track and a pose track; before the task is started, collecting data of a plurality of groups of force sensors, completing dynamic gravity compensation, and calculating real contact force in real time; in the task process, the real contact force is continuously obtained, the speed deviation and the pose deviation are obtained, the real contact force, the speed deviation and the pose deviation are input into a compliant controller based on an admittance model, and the target pose of the mechanical arm in the next step is dynamically adjusted; the compliant cooperation is repeatedly executed until the whole cooperation sewing task is completed; after the task is finished, a parameter optimization algorithm based on smooth iterative learning is constructed, the real contact force trajectory, the speed trajectory and the pose trajectory are used as input, the smooth iterative learning algorithm autonomously optimizes parameters of a compliant controller, and the optimal force-position trajectory tracking performance is achieved. According to the invention, a safe and stable robot cooperation sewing task is realized.

The invention provides a five-axis contour error control method based on variable gain iterative learning. The method comprises the following steps: 1, establishing coordinate systems of a five-axis machine tool; 2, constructing a kinematic model of the machine tool by adopting a D-H parameter method; 3, estimating a contour error by using an arc fitting method, and ensuring the synchronism of a tool nose position contour error and a tool attitude contour error through a pose proportion; step 4, correcting the position and attitude of the cutter based on a variable gain iterative learning control algorithm, and adaptively adjusting control input by optimizing learning gain to reduce contour errors; according to the method, the variable gain iterative learning algorithm is introduced, so that the control precision is optimized, the convergence speed is also improved, and the contour error is more effectively inhibited in a complex and high-precision processing scene.

The present invention belongs to the technical field of probe attitude tracking control, and specifically relates to a method for attitude tracking control of a probe orbiting an asteroid based on adaptive iterative learning. The method comprises the following steps: Step 1: The probe mathematical model outputs two state variables, namely attitude and angular velocity; Step 2: The attitude and angular velocity are fed back to the input of the control system, and the error attitude and error angular velocity are respectively subtracted from the given desired attitude and desired angular velocity; Step 3: A probe attitude tracking error system is established based on the two error states, where the uncertainty and external interference in the system are regarded as total disturbances and estimated using an adaptive law; Step 4: An attitude tracking controller is designed based on an adaptive iterative learning algorithm. The present invention proposes a design method for an adaptive iterative learning controller based on an all-wheel drive system approach and sliding mode control. The designed adaptive iterative learning controller has excellent control performance and a small number of adjustment parameters.

The application provides an iterative learning impedance control algorithm for a flexible driving exoskeleton, and the algorithm comprises the following steps: a dynamics model of an upper limb rehabilitation robot is established by measuring parameters such as the length, mass and stiffness of a series elastic actuator of an exoskeleton joint; secondly, the model is processed by using singular perturbation theory to simplify the complexity of the system; on this basis, a human-computer interaction control strategy is designed by combining impedance control and iterative learning algorithm, and collaborative rehabilitation training of the machine active and human active modes is realized; finally, the effectiveness of the algorithm is verified through simulation and experiment platform, and the results show that the method can adapt to the flexible driving characteristics, and improve the tracking accuracy and human-computer interaction safety of the exoskeleton.

The invention relates to the technical field of pulse power and electromagnetic manufacturing, and discloses a pulse magnetizer which comprises a charging module, a capacitance matrix module, an acquisition module and a control module. The acquisition module adopts a Kelvin four-wire system structure to synchronously acquire a voltage and current discrete sequence at a discharge moment; the control module is based on a heterogeneous architecture of a digital signal processor and a field programmable gate array, equivalent resistance and inductance parameters of a load are identified in real time by using a regularization least square algorithm, and a closed-loop control logic based on single waveform reverse analysis is adopted by a system core: a nonlinear equation is solved by using a Newton iteration method to reconstruct a capacitance matrix; the pulse width is accurately locked; and meanwhile, an iterative learning algorithm is combined to correct the charging voltage, compensate the capacitance discrete quantization deviation and lock the peak current. According to the invention, the self-adaptive compensation of the magnetizing waveform to the load parameter drift is realized, the current repetition precision is obviously improved, and the service life of the system is obviously prolonged.

The invention discloses an imaging department medical technician scheduling system, method and device and a storage medium, and the system comprises a rule modeling module which converts a scheduling rule from a text form into a computable form; the rule priority module is used for setting priorities for the scheduling rules; the conflict detection module is used for carrying out conflict detection on the computable scheduling rule and identifying a conflict rule; the conflict solving module is used for providing solution suggestions for the conflict rules according to the priorities; the user interaction module is used for graphically outputting solution suggestions and prompting a scheduling manager to modify a scheduling rule so as to solve conflicts; and the reinforcement learning optimization module is used for continuously optimizing the scheduling scheme by utilizing an iterative learning algorithm. According to the scheduling method, the scheduling rule is subjected to conflict detection and the conflict is solved, so that the scheduling rule is more reasonable, employee preferences and work requirements are comprehensively considered, the scheduling scheme is learned and optimized, and the more reasonable scheduling scheme is quickly obtained.

The invention discloses a piezoelectric actuator compensation method based on an LSTM inverse model and iterative learning, and relates to a piezoelectric actuator compensation method. The invention aims to solve the problems of obvious nonlinearity and time-varying characteristics during frequency control speed output of a piezoelectric actuator. The method comprises the steps of 1, data acquisition and LSTM inverse model training; firstly, a controller program is written, then data is cleaned, abnormal values are eliminated, samples are recombined, finally, a double-layer LSTM network is constructed, and a model is exported after a training set is trained through a Huber loss function; step 2, iteratively learning a control flow; the iterative learning process is used as an independent medium-low priority task, is triggered after one operation cycle is ended, and reads a speed tracking error sequence in the whole operation cycle after the operation of the LSTM inverse model is ended from the shared memory to obtain data; and step 3, designing an LSTM-based inverse model and an iterative learning algorithm. The invention belongs to the technical field of precision motion systems.

This invention discloses a PAM-driven dual-section CM angular position tracking control system and algorithm, belonging to the technical field of CM angular position control systems. It addresses the problems of model uncertainty and computational burden, as well as unpredictable residual sets, inherent in multi-input multi-output and nonlinear CM angular position control systems, caused by neural networks, fuzzy logic systems, adaptive techniques, and iterative learning. A simple state feedback controller is designed. This controller integrates the adjustment function with error transformation technology, eliminating the need for adaptive mechanisms, neural networks, fuzzy logic systems, and iterative learning algorithms. It also avoids limitations on the initial value of the tracking error, ensuring not only pre-set transient and steady-state tracking performance but also continuous control signal without abrupt increases. The controller designed in this invention can effectively realize PAM-driven dual-section CM angular position tracking control.

The present invention relates to the field of intelligent mechanical equipment and is a robot polishing method based on posture recognition and iterative learning, which solves the problem of poor generalization ability of the robot arm in the prior art. The present invention teaches trajectory reconstruction; posture recognition; when the posture of the workpiece changes, the robot arm calls the changed trajectory information and performs processing according to the trajectory information; and iterative learning of contact force. The present invention binds the robot arm path with the end posture and the point cloud data of the workpiece, compares the point cloud information before and after the posture of the workpiece changes, obtains the posture change of the workpiece through point cloud registration, and calls the new trajectory information for processing; the force sensor at the end records the interactive force data, and iteratively updates the path according to the set iterative learning algorithm update rate, so that the interactive force reaches the expected value and completes the precise polishing task.

The invention relates to the field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle predefined time fault-tolerant control method based on an iterative learning neural network, and the method comprises the following steps: S1, building an outer ring position system model and an inner ring attitude system model of a three-rotor unmanned aerial vehicle under external interference and actuator faults; s2, designing a neural network structure to realize disturbance real-time estimation compensation, and updating a neural network weight matrix through an iterative learning algorithm; and S3, respectively designing an outer ring position pre-defined time fault-tolerant controller and an inner ring attitude pre-defined time fault-tolerant controller by adopting an inner and outer ring tracking control strategy, estimating and compensating an actuator fault online by adopting a self-adaptive algorithm, and ensuring that the unmanned aerial vehicle tracks quickly and stably while the system pre-defined time is converged. According to the invention, through collaborative design of neural network compensation and the predefined time adaptive fault-tolerant controller, the influence of the actuator fault on the system stability is reduced, and the control performance of the unmanned aerial vehicle during external disturbance is improved.

The invention proposes a method for determining a tooth color, wherein an analysis evaluation device has an iterative learning algorithm using a CNN, which, in a preliminary step, acquires images at different illuminations and shooting angles on the basis of at least one previously known, if necessary virtually generated, sample tooth color and analyzes and evaluates the images and learns a correspondence with the correct sample tooth color. In the analysis evaluation step, a shooting device is provided, with which an image of a support having a previously known, if necessary virtually generated, sample tooth color together with at least one tooth is shot. The shooting device acquires at least two images of the combination of the tooth to be determined and the support from different shooting angles and transfers the images to the analysis evaluation device. The analysis evaluation device analyzes the acquired images on the basis of the learned correspondence with the correct sample tooth color and outputs the tooth color of the tooth to be determined from a reference value, such as a commonly used tooth color chart, for example A1, B2, etc.

The invention provides an iterative learning impedance control algorithm for a flexible driving exoskeleton, and the algorithm comprises the steps: building a kinetic model of an upper limb rehabilitation robot through measuring the length and mass of an exoskeleton joint, the rigidity of a series elastic driver and other parameters; secondly, the model is processed through the singular perturbation theory, and the system complexity is simplified; on the basis, an impedance control and iterative learning algorithm is combined, a man-machine interaction control strategy is designed, and collaborative rehabilitation training of a machine active mode and a human body active mode is achieved. Finally, the effectiveness of the algorithm is verified through a simulation and experiment platform, the result shows that the method can adapt to the flexible driving characteristic, and the tracking precision of the exoskeleton and the man-machine interaction safety are improved.

The application discloses a trajectory tracking control method for an unmanned system without an initial stabilizing learning strategy, relates to the technical field of unmanned systems, and can overcome the dependence of an initial stabilizing control strategy on a system model and ensure that an unmanned vehicle system realizes trajectory tracking of a leader under a designed reinforcement learning controller. First, an unmanned system model is established; a trajectory tracking controller of a follower and a corresponding cost function are designed to realize optimal trajectory tracking of the follower; an offset factor is introduced on the basis of an augmented system of the unmanned system to construct a new closed-loop system; system data are generated according to the new closed-loop system, and the system data are collected; and a policy iteration learning algorithm is used to find an optimal control strategy to realize model-free trajectory tracking control without dependence on an initial stabilizing control strategy.

Systems and methods for generating formulations for product formulation are disclosed. The method includes receiving, from at least one device associated with a user, a request for a proportioning for designing a product. Related data associated with the product is received from a plurality of data sources. Proportions for designing the product are generated by applying one or more features of the trained machine learning model. The trained machine learning model has been trained by: inputting the relevant data associated with the product into the machine learning model; adopting a supervised learning technology to optimize the machine learning model through an iterative learning algorithm; and evaluating the performance of the machine learning model using a verification technique. The ratio is presented in a user interface of at least one device associated with the user.

The application discloses a kind of LVDT sensor measurement precision compensation method based on key component position adjustment, the position of lever fulcrum is optimized by finite element analysis, the mechanical model containing temperature variable is constructed, the influence of the thermal expansion coefficient of sensor material at different working temperatures on the mechanical properties of lever is considered, to ensure that the lever is uniformly stressed within the full range, and maintain the optimal stress state within the full temperature range, reduce the core movement deviation and nonlinear error caused by lever structure and temperature change, improve the measurement accuracy of sensor. Iterative learning algorithm is used to compensate the measurement data, based on multiple measurement data, the residual is corrected in reverse, the compensation parameters are corrected in reverse, and the momentum term is introduced to accelerate the algorithm convergence, and the momentum coefficient is adaptively adjusted according to the dynamic response characteristics of the sensor. This dynamic adjustment compensation method can better adapt to the measurement error change under different working conditions, greatly improves the compensation effect.

The invention provides a multi-point sensor and algorithm-optimized absorption tower slurry density balance regulation and control method. The method comprises the following steps: mapping a low-density slurry region at the upper part of an absorption tower according to a dynamic change trend of density difference, and processing the region by adopting a grouping clustering algorithm to obtain a clustering result of spatial distribution change; according to the clustering result of the spatial distribution change and the corrosion environment influence compensation factor in the absorption tower, whether the factor reduces the monitoring precision or not is judged, and an adjusted slurry density measurement value sequence is obtained; extracting a process stability index from the improved desulfurization process efficiency monitoring data, and processing the index by adopting a dynamic adjustment algorithm to obtain an optimized slurry density equilibrium state; and updating deployment parameters of a multi-point sensor array according to the optimized slurry density equilibrium state, and processing the parameters by adopting an iterative learning algorithm to obtain enhanced real-time monitoring precision.

The invention discloses a track correction and control method for a multi-axis electro-hydraulic servo system of a radial forging machine, and provides a cooperative control scheme fusing an extended state observer and online iterative learning for the composite control problem of coexistence of random high-frequency forging impact and workpiece axial plastic extension accumulated deviation in the radial forging process. According to the method, by establishing and discretizing a system model, on one hand, real-time estimation and feed-forward compensation are carried out on total disturbance including random impact by using an extended state observer; on the other hand, multi-step state prediction is carried out based on the discrete model, an online iterative learning algorithm is adopted to carry out iterative correction on prediction errors caused by slow change factors such as workpiece extension, and track correction is generated. Finally, disturbance compensation and trajectory correction jointly act on a controller, synchronous suppression and compensation of high-frequency random interference and low-frequency trend deviation are achieved, and trajectory tracking precision and robustness of the multi-axis system under strong impact and nonlinear working conditions are remarkably improved.

The present application relates to the technical field of pulse power and electromagnetic manufacturing, and discloses a pulse magnetizing machine, which comprises a charging module, a capacitor matrix module, an acquisition module and a control module; the acquisition module synchronously acquires voltage and current discrete sequences at the discharge moment in a Kelvin four-wire structure; the control module is based on a heterogeneous architecture of a digital signal processor and a field programmable gate array, uses a regularized least square algorithm to identify equivalent resistance and inductance parameters of a load in real time, and adopts a closed-loop control logic based on single waveform reverse analysis as a system core; a Newton iteration method is used to solve nonlinear equations to reconstruct the capacitor matrix and accurately lock the pulse width; at the same time, the charging voltage is corrected by combining an iterative learning algorithm, the capacitor discrete quantization deviation is compensated, and the peak current is locked. The present application realizes adaptive compensation of the magnetizing waveform to load parameter drift, and significantly improves the current repetition accuracy and the operation life of the system.