12 results about "Real-time Control System" patented technology

The application relates to the technical field of automatic sunshade of sunshades, and proposes a full-automatic sunshade based on sun azimuth and shadow identification and a use method thereof, which comprises a handle and a supporting handle, a connecting seat is fixedly installed on the handle, a fixing seat is fixedly installed on the connecting seat, a supporting seat is fixedly installed on the supporting handle, and the connecting seat, the fixing seat and the supporting seat are all triangular prism structures. Through the multi-sensor fusion technology of a gyroscope, an illumination sensor and a camera, in combination with a real-time control system, the sunshade can continuously keep the sunshade surface shadow coinciding with the human body without manual change of the handle direction, the sunshade precision is remarkably improved, the problem that a local body is directly irradiated by sunlight is effectively avoided, and the automatic adjustment of the sunshade surface direction is realized.

This invention discloses a remote real-time control system for ceramic membrane magnetic filtration based on the Internet of Things (IoT). The system comprises a sensing layer, an edge computing layer, a cloud control layer, a user interaction layer, and a modular access unit. The sensing layer, centered on a gradient field permanent magnet magnetic filtration module, integrates a Hall sensor array, an electromagnetic flowmeter, and a temperature sensor to collect key data such as magnetic saturation index, syrup flow rate, and liquid temperature in real time, achieving comprehensive perception of the ferromagnetic impurity capture status and raw material parameters. The gradient field permanent magnet magnetic filtration module is connected in series with the syrup delivery pipeline and includes a multi-stage detachable magnetic rod assembly, a Hall sensor array, a servo rotating sleeve, and a slag discharge channel. Compared to traditional iron removal equipment, this IoT-based remote real-time control system for ceramic membrane magnetic filtration is specifically designed for the characteristics of high-sugar, high-acid liquids. The gradient field magnetic circuit improves the capture efficiency, stably controlling the iron content below 0.3 ppm, effectively preventing product blackening, sedimentation, and a metallic taste.

This invention provides a real-time control system and method for a humanoid robot that integrates electromyography (EMG) and monocular vision, relating to the field of multimodal humanoid robot control technology. The system includes a posture data acquisition system, an EMG data acquisition system, an EEG data acquisition system, a computer system, a data transmission module, and a humanoid robot. The posture data acquisition system acquires two-dimensional motion videos of the user's entire body; the EMG data acquisition system acquires EMG signals from the user's arm movements; the EEG data acquisition system acquires EEG signals from the user's motor imagery; the computer system converts the received two-dimensional motion videos, arm movement EMG signals, and motor imagery EEG signals into corresponding control commands to control the humanoid robot to perform corresponding actions. This system and method integrate multiple information sources, enabling precise control of the humanoid robot's major joint movements using monocular vision information and precise control of the humanoid robot's hand and wrist movements using EMG information.

This invention discloses a three-beam coaxial adaptive laser melting and forming method and apparatus, relating to the fields of metal additive manufacturing and laser welding technology. The method includes: activating a protective gas system; starting a high-precision real-time control system, acquiring multi-source signals from the molten pool region in real time through a monitoring module; transmitting the multi-source signals acquired by the monitoring module to a central processing unit to establish a mapping relationship model between monitoring features and process quality; based on the output of the mapping relationship model, real-time feedback control of laser power, wire feeding speed, and powder feeding rate; and repeating the adaptive control steps to achieve full-process adaptive closed-loop control of the forming path. This invention employs the aforementioned three-beam coaxial adaptive laser melting and forming method and apparatus, which has the advantages of good synergy and multiple control dimensions, enabling online real-time closed-loop control of the manufacturing process and solving the problems of uneven gap repair and forming defects and composition control in the preparation of functionally graded materials.

A system for time-controlled optimization and real-time control of dynamic systems under constraints, based on the time to loss of control, consisting of: a data acquisition module configured to receive real-time data from one or more technical sources representing the state of a monitored dynamic system; a preprocessing module connected to the data acquisition module and configured to process and filter the received real-time data to generate a structured representation of the current system state; a state prediction module connected to the preprocessing module and configured to receive the preprocessed data and, based on the structured representation of the current system state, calculate predicted future states of the monitored dynamic system over a defined time horizon;a Time to Loss of Control (TLC) module connected to the state prediction module and configured to compare predicted future states with predefined thresholds representing acceptable operating limits, determine a Time to Loss of Control (TLC) that represents an estimated time at which the monitored dynamic system is expected to reach an unstable or undesired state, and continuously updates the TLC as new real-time data becomes available;a constraint modeling module configured to identify operational constraints related to the monitored dynamic system, wherein the operational constraints include resource constraints, safety thresholds, and operating rules, and wherein the constraint modeling module is further configured to transform the operational constraints into a structured representation that defines a feasible set of permissible actions;An optimization module connected to the constraint modeling module, comprising a quantum processing unit configured to perform constrained optimization using energy minimization-based computational techniques to determine executable control actions within the remaining time until loss of control, wherein the optimization module is configured to determine one or more executable control actions that improve system stability based on the current system state, predicted future states, determined TLC, and structured representation of the operational constraints, wherein the one or more executable control actions are determined based on the remaining time until loss of control (TLC) such that only actions possible within this time window are considered;A control signal generation module connected to the optimization module is configured to convert the results of the optimization module into executable output signals, which are executed by a control execution module connected to the monitored dynamic system. After the signal is executed, control actions are automatically performed on the monitored dynamic system within the remaining time until loss of control.

This invention proposes a graph neural network-based method and system for sensing the thermal state of a motor, integrating physical mechanisms. The method first uses a low-order thermal network model to solve the thermal balance equation to obtain a physical temperature reference. Then, the thermal network topology is mapped to a graph neural network structure to establish a physical correspondence between information transmission and heat conduction. Spatial thermal coupling features are extracted through graph convolutional layers, and a bidirectional gated recurrent network is used to capture the thermal inertia evolution features over time. Finally, a residual learning strategy is employed to construct a composite loss function containing physical constraints for optimization. The data-driven prediction residual is linearly superimposed with the physical reference temperature to achieve high-precision estimation. This invention effectively overcomes the shortcomings of traditional thermal network models, such as difficulty in parameter identification and the lack of physical interpretability in purely data-driven models. It significantly improves the robustness and accuracy of temperature monitoring of key motor components and is suitable for embedded real-time control systems.

The application discloses a kind of robot end side model online gray scale update and safe rollback method, belong to robot control and data processing technical field.The method generates state vector and executability constraint by collecting sensor and actuator data, and difference coding is carried out to new and old model parallel inference;Local feasible pipeline and safe transition kernel function are constructed, and model action smooth mapping is realized;Dynamic envelope verification is executed using hard real-time safety monitor, and the physical safety of update process is guaranteed;Model stable takeover is realized based on the joint determination of progress, safety and convergence evidence, and when abnormal, it is quickly rolled back through reverse safe transition kernel function.The application solves the problems of action step, rollback discontinuity and real-time deficiency in traditional model switching, realizes the online update of end side model without interruption, low disturbance and high safety, and is suitable for real-time control systems such as industrial robots and collaborative robots, to improve the continuity and reliability of equipment operation.

The described technology solves the problem of implementing key agreement without the need to exchange separate key agreement messages, and thus meets the stringent start-up time requirements of real-time control systems. This is achieved using a group-wide key counter, each node storing the latest value of this counter observed via the last received secure message. The counter value is monotonically increasing, and the nodes keep synchronized by sending the counter value (or a representation of the counter value) in each secure message.

This invention relates to the field of industrial automation control technology, specifically disclosing a method for collaborative scheduling of motion control and logic control in industrial real-time control systems. This method acquires operational state parameters such as processor resources, network congestion, and task urgency to construct a non-cooperative game model between motion control and logic control tasks and establish a payoff function. It then configures a deep reinforcement learning intelligent scheduling agent, using a neural network to predict system gains under different decisions. Based on the agent's output, it dynamically adjusts the time slice weights and execution priorities of the two types of tasks, causing the system state to tend towards Nash equilibrium. Finally, it executes the scheduling and corrects the agent parameters in real time based on feedback. This invention achieves dynamic optimization of resource allocation, improves the response speed of logic control while ensuring the hard real-time performance of motion control, reduces computational jitter, and enhances the determinism, stability, and adaptability of the system under complex operating conditions.

This invention discloses a fuzzy PID control method and system for antennas based on particle swarm optimization, comprising: S1: establishing a simulation model of a radio telescope servo system; S2: constructing a parameter encoding vector for a fuzzy PID controller; S3: using a particle swarm optimization algorithm to globally optimize the parameter encoding vector; S4: when a preset termination condition is met, outputting the globally optimal particle position vector and decoding it to obtain the optimal fuzzy PID controller parameter set; S5: deploying the optimal fuzzy PID controller parameter set into the real-time control system of the radio telescope, and adjusting the PID control gain online based on the real-time acquired antenna angle error and its rate of change to drive antenna movement. This invention's radio telescope servo control method based on particle swarm optimization of fuzzy PID parameters solves the problems of existing technologies where controller parameters rely on human experience, are difficult to globally optimize, and have limited adaptive capabilities.