14 results about "Learning controller" patented technology

The invention provides an underwater robot multi-task rapid adaptive control method based on meta reinforcement learning. The method comprises the following steps: constructing an underwater robot dynamic model and a thrust distribution strategy matrix; three reinforcement learning controllers, namely, a non-overshoot position controller, an overshoot allowing position controller and a propeller flexible control controller, are respectively designed according to diversified task requirements; introducing a meta-learning mechanism to build a meta-training platform, and training the three reinforcement learning controllers to obtain a group of optimal initialization parameters; and deploying the obtained optimal initialization parameters and the subtask reinforcement learning controller to the underwater robot, and carrying out two-stage training according to different tasks. Finally, when the controller is deployed in engineering practice, the output of the propeller can be intelligently adjusted according to the relative distance and speed information, calculated in real time, between the controller and the target position, and it is ensured that accurate position control can be achieved in various task scenes. The method aims at meeting the requirement for rapid self-adaption of multiple tasks of the underwater robot in the complex and changeable underwater environment, and accurate and flexible response to position control is achieved through the control method based on meta reinforcement learning when the task requirements change.

This invention discloses a method for predicting and protecting the overheating risk of asynchronous motors, specifically relating to the field of motor control and protection technology. Based on an intelligent fusion model, it estimates temperature and thermal stress field, calculates the rate of change of thermal stress, non-uniformity, and hotspot trends, and calculates a dynamic health index using historical data. These parameters are then input into a multi-objective reinforcement learning controller to optimize long-term health and short-term performance, generating a thermal shaping control vector to regulate the motor. This invention combines physical mechanisms with data-driven approaches through an intelligent fusion model, utilizing a graph neural network to learn the structure of the heat conduction graph and verify physical laws, thereby improving the accuracy of thermal state estimation. It achieves a multi-dimensional risk characterization combining transient impact and cumulative effects through the rate of change of thermal stress, non-uniformity, hotspot trends, and dynamic health index. By optimizing long-term health and short-term performance losses, a thermal shaping control vector is generated to achieve regulation from passive protection to active prevention, extending the motor's service life.

The application discloses a kind of reinforcement learning batch process control methods based on improved AC algorithm, it is related to the field of deep reinforcement learning and batch process control field.The method will be based on reinforcement learning method The batch process control is modeled as an optimal control problem on the basis of Markov decision process;Control action constraint is introduced in the reward function of reinforcement learning controller, the number of effective reward samples is increased to improve the learning rate of reinforcement learning controller, and the control cycle is shortened.Priority sampling method is introduced in the Actor-Critic algorithm of deep reinforcement learning, and a soft actor-critic algorithm with priority sampling is proposed to improve the sampling efficiency in the experience replay pool.The present application does not depend on prior knowledge and process model, and can realize model-free control of batch process.

The application discloses a safety control method and system for unmanned loader based on machine learning, and relates to the technical field of safety control.The method comprises the following steps: processing multi-source sensor data by using an anti-vibration feature extraction network to generate a safety evaluation coefficient; analyzing a safety state based on a variational autoencoder model to generate structured safety warning information; applying reinforcement learning for multi-objective optimization to output a safe driving trajectory; and finally converting the trajectory into a control instruction through a deep learning controller and adjusting the control parameters in real time based on an online learning mechanism.The application realizes intelligent safety control of the unmanned loader under complex working conditions by constructing a whole-process machine learning processing chain from data acquisition to control execution, and solves the problems of poor safety control adaptability, insufficient cooperation between control modules and vibration interference affecting the accuracy of judgment in the prior art.

Disclosed is an optimal AP connection method including transmitting, by an IoT device, a probe request message to a plurality of iAPs; transmitting, by each of the plurality of iAPs that receives the probe request message, the probe request message to an iAP controller and transmitting local information to each iAP to the iAP controller; performing, by the iAP controller, reinforcement learning for IoT device using global information that is updated from the local information; selecting, by the iAP controller, an optimal iAP based on RLreinforcement learning and transmitting recommended Tx power value information on the IoT device and a probe response message to the selected corresponding iAP; and transmitting, by the corresponding iAP that receives its selection as the optimal iAP in response to the probe request from the iAP controller, the probe response message and the recommended Tx power value information to the IoT device.

This invention relates to the field of power systems and energy storage technology. It discloses a method, system, equipment, and medium for constructing a power storage system based on new energy operation. The method includes: classifying the operating conditions of energy storage battery clusters according to operating data; identifying the state transition cost of each energy storage battery cluster using a state transition model based on the classified operating conditions and operating data; performing rolling optimization with the goal of minimizing the total system operating cost, wherein the total system operating cost includes a cost item calculated based on the state transition cost, and outputting a reference power command for each energy storage battery cluster; and generating the final power control signal for each energy storage battery cluster using a reinforcement learning controller based on the reference power command and the state transition cost. This method can quantify the internal electrochemical state transition cost of the battery and deeply integrate it with system-level economic dispatch and device-level intelligent control to maximize the value of the energy storage system throughout its entire lifecycle.

The present application relates to the technical field of energy storage control, in particular to a hybrid energy storage device cooperative control method and system based on machine learning. First, the expected workload spectrum is obtained according to historical operation data and service period, then the dominant control mode is determined and the mode health degree is generated according to the expected workload spectrum and the current health state of the system, the real-time state characteristics are extracted to input the reinforcement learning controller branch to obtain power distribution instructions, the life consumption increment and the comprehensive quality coefficient are calculated, and finally the effectiveness of the instructions is evaluated according to the quality coefficient threshold to realize control mode switching or controller parameter updating. By implementing the present application, the optimal balance of asset health and comprehensive benefit of the mobile storage and charging device hybrid energy storage system in the whole life cycle can be realized.

The invention provides a control method and system of a die bonding head and electronic equipment, relates to the technical field of semiconductors, and is used for improving the positioning precision of a die bonding process and guaranteeing the consistency of firmware results. The method comprises the steps of obtaining a control instruction of a (k + 1) th round of die bonding motion of a die bonding head based on a position error of the die bonding head after the k-th round of die bonding motion, a control instruction of the k-th round of die bonding motion and an iterative learning controller; wherein the iterative learning controller is used for optimizing a control instruction; and controlling the die bonding head to move based on the control instruction of the (k + 1) th round of die bonding motion and the basic controller.

This invention discloses an adaptive robust control method and system for auxiliary equipment systems in thermal power plants under varying operating conditions, specifically relating to the field of auxiliary equipment system control technology in thermal power plants. The method includes the following steps: S1, constructing a dual-network architecture; S2, using the prediction model network to perform multi-step prediction of the controlled variables of the auxiliary equipment system, obtaining a predicted state sequence; S3, inputting the current operating condition characteristics, the controlled variable deviation, and the predicted state sequence into the controller network, and the controller network outputting the original control quantity. This invention achieves accurate prediction and adaptive control of the dynamic characteristics of the auxiliary equipment system; ensures the stability of the reinforcement learning controller output, overcoming the insufficient security of traditional black-box models; utilizes a covariance matrix adaptive evolution strategy to perform offline optimization and incremental updates of controller parameters, achieving continuous self-optimization of the controller; and simultaneously realizes equipment degradation perception and active compensation, ensuring continuous operation of the system under sensor failure conditions.

The present application relates to a device for dispensing a determined weight of a cosmetic product into a receptacle from a reservoir, the device comprising at least one electromechanical member capable of moving an amount of cosmetic product from said reservoir installed in the device to a dispensing zone of said device where said amount of product may be transferred through a nozzle into the interior of the receptacle received in said dispensing zone, the device comprising a controller configured to deliver a control signal to the electromechanical member according to an error with respect to a setpoint corresponding to the determined weight of cosmetic product to be delivered, said error being determined on the basis of a weighing datum obtained by at least one weighing cell for the reservoir installed in the receiving zone, the device being characterized in that the controller is a reinforcement-learning controller.

An infant care device comprising: an infant support; a multi-drive-axis drive section coupled to the infant support and having a plurality of motors configured to produce actions and movements of the infant support; a biometric sensor configured to observe at least one characteristic of the infant; and a learning controller arranged to learn how to soothe the infant and configured to use a neural network or a state machine communicatively coupled to the biometric sensor and the multi-drive-axis drive section, wherein the learning controller records sensor data from the biometric sensor and effects changes in the actions and movements of the infant support through the neural network or the state machine, which effects soothing and calming of a state of the infant within the infant support, wherein the learning controller is configured to learn, based on biometric sensor input over time, a reaction of the infant to a change in one or more of the actions and movements of the infant support in order to adjust a subsequent application of the change.

The application discloses a mechanical arm system fault-tolerant distributed learning control method based on a loose alignment condition. The method designs an auxiliary system of loose alignment condition and compensation input saturation, constructs inverse step error, a virtual controller and compensation error. An iterative learning controller is designed to control the mechanical arm system and compensate for unknown inertia matrix, parameter uncertainty and disturbance, and convergence of the error system is analyzed by constructing a composite energy function. Compared with traditional control methods requiring accurate mathematical models, the control algorithm is more novel and the application condition is simple. When the method is used for repetitive task operation, the control precision is effectively improved, and the method has good engineering application value.