12 results about "Corrective control" patented technology

This application relates to the field of traveling sludge suction machine technology, and particularly to a method, system, equipment, and program product for corrective control of a traveling sludge suction machine. The method involves installing radar ranging devices at the four corners of the traveling sludge suction machine's working bridge. The method includes: using the radar ranging devices to detect the position and height information of each corner of the traveling sludge suction machine relative to the track; calculating the position distance difference and left-right height difference of the traveling sludge suction machine relative to the track during its movement based on the position and height information; determining a corrective control strategy for the traveling sludge suction machine based on the position distance difference and / or left-right height difference, and controlling the traveling sludge suction machine based on the corrective control strategy. This solves the problem of the significant threat to equipment safety, personnel safety, and water supply safety posed by deviation or tilting during the actual operation of the traveling sludge suction machine, achieving automatic correction of the traveling sludge suction machine, and improving production efficiency and the level of automation in the production process.

The invention relates to the technical field of hydraulic forming, and discloses a pose recognition method and a compensation correction control method and system for a hydraulic machine movable beam. The method is applied to a hydraulic machine pose recognition compensation system which comprises a movable beam, a main cylinder and four hydraulic compensation cylinders. According to the method, after a movable beam begins to descend, the pressure sudden change time difference of the diagonal hydraulic compensation cylinders is calculated; taking the first contact in the opposite angles as a reference cylinder, collecting the piston rod displacement of the main cylinder and the reference cylinder in the corresponding time difference, calculating the inclination angle of the movable beam in the diagonal direction, and identifying the position and posture state of the movable beam, so as to calculate the position compensation amount for safety judgment, and if the maximum inclination value is within a preset threshold value, judging the safety of the movable beam. If so, calculating control parameters of the next forming period according to the position compensation amount, and performing compensation correction control on the movable beam. According to the method, the inclination condition of the movable beam can be rapidly recognized, the non-synchronous pose of the movable beam is corrected, impact oscillation caused by initial inclination of the movable beam is reduced, and the precision of the subsequent leveling stage is improved.

A method and apparatus for dynamically routing traffic from a first network device to a second network device is described. In one implementation, the method includes sampling one or more qualified packets from an ingress stream using neural network classification criteria, appending a flow encapsulation header with the path information, providing the path information to the path computation engine at user approved time intervals, transmitting the packets to destination network device, and collecting real-time network telemetry data. Real-time network telemetry data is stored in a network health database. When a new network transfer is introduced, the system correctly identifies the characteristics of the transfer, in terms of size, throughput, jitter, latency, loss, and completion times. A corrective action is introduced that inputs the current network health information and transfer characteristics by generating corrective control rules to allow the transfer to be optimally routed (e.g., automatically) through the network.

Embodiments directed to designing corrective control signals are described. When implemented to drive a quantum operation, such corrective control signals can implement quantum gates that are insensitive to different noise types associated with the quantum operation. In one example, a method can include deriving noise cancellation conditions that are to be satisfied to define a corrective control signal that cancels different noise types associated with performing a quantum operation when the corrective control signal is used to drive the quantum operation. The method can further include constructing a space curve that satisfies the noise cancellation conditions in a multidimensional space. The space curve can be representative of the corrective control signal. The method can further include defining the corrective control signal based on the space curve.

The application discloses a sitting posture monitoring control system of an intelligent learning desk and chair, and relates to the technical field of smart home and ergonomics equipment. The system comprises a sitting posture data acquisition module, which is used for acquiring multi-dimensional original data of a user's body posture in real time; a sitting posture state analysis module, which is used for fusion calculation and feature extraction based on the multi-dimensional original data collected by the sitting posture data acquisition module, accurate identification of a current sitting posture state and judgment of whether the current sitting posture state deviates from a health benchmark; a control strategy generation module, which is used for generating optimal correction control instructions according to the type and degree of sitting posture deviation, in combination with historical sitting posture data and personalized parameters; and an actuator driving module, which is used for accurately driving corresponding mechanical execution units of the desk and chair to adjust according to the control instructions. The application evolves from passive correction to active prevention through multi-sensor fusion perception, real-time feature extraction and personalized control strategy generation, and comprehensively monitors and dynamically corrects the sitting posture state of a user.

This invention relates to the field of flight control technology, specifically to a method, system, device, and medium for controlling single-engine failure in unmanned aerial vehicles (UAVs). The method includes: single-engine failure control during the UAV takeoff phase, which involves aborting takeoff, shutting down the other engine, using the brakes, and employing the rudder and nose wheel for corrective control; single-engine failure control during the UAV's cruising phase, which involves using an automatic lateral heading compensation controller and the flight control system to compensate for the additional yaw torque caused by the single engine, bringing the aircraft to a balanced state and adjusting the UAV's course to the designed emergency route for recovery; and single-engine failure control during the UAV landing phase, which involves maintaining the aircraft's balance using the automatic lateral heading compensation controller before landing, immediately shutting down the other engine after touchdown, and using the rudder and nose wheel for corrective control to track the ground path. This invention ensures the flight safety of UAVs and improves their emergency response capabilities in the event of a single-engine failure.

The invention provides an intelligent parking lock management method and system for multi-parking-space cooperative control and abnormity self-adaption, and relates to the technical field of intelligent parking lock management. The method comprises the steps of performing synchronous sampling on an original parameter set of a plurality of parking lock nodes, and mapping the original parameter set into a first sub-feature reflecting physical energy consumption of a single body and a second sub-feature reflecting a perception environment background; using the first sub-feature and the second sub-feature to calculate a collaborative robust state entropy through a sliding window entropy increase algorithm; based on a comparison result of the collaborative robust state entropy and a preset threshold value, executing a nonlinear branch decision so as to dynamically adjust and control the topological weight matrix; based on the topological weight matrix and the first sub-feature, virtual damping compensation is introduced, parking lock driving parameters are reversely adjusted, and a correction control instruction is generated and executed; and a state feedback result after the correction control instruction is executed is collected, and a monotonic mapping function is evolved as an evaluation index to serve as a judgment basis of the system collaborative health degree in combination with the collaborative robust state entropy.

This application relates to the field of industrial automatic control technology, and in particular to a method and system for correcting fabric deviation based on non-contact sensing data. The method includes: acquiring the deflection angle of the fabric on the first guide roller, the transmission line speed of the fabric production line, and the original edge deviation value of the fabric at the second guide roller; determining the lateral migration rate of the fabric using the deflection angle, transmission line speed, and a preset fabric slip coefficient; performing weighted integral processing based on the lateral migration rate to determine the expected coupling deviation value of the fabric's movement from the first guide roller to the second guide roller; determining the intrinsic deviation amount of the fabric using the expected coupling deviation value; and adjusting the angle of the second guide roller based on the intrinsic deviation amount. Through the above technical solution, more accurate correction control of fabric deviation can be achieved.

The application provides an encoder-driven automatic take-up and pay-off machine regulation method, equipment and medium, relates to the technical field of automation control, installs an encoder on a driving motor of a target take-up and pay-off machine, determines a target take-up length according to production demand information, converts the target take-up length into a target rotational displacement, arranges a perception sensor group to obtain a pay-off multi-dimensional perception data stream, obtains a pay-off pulse signal stream through encoder reading, analyzes and processes the pay-off multi-dimensional perception data stream and the pay-off pulse signal stream and predicts a displacement, adopts a PID controller to regulate and analyze the rotational predicted displacement and the target rotational displacement, obtains a displacement correction control quantity, and performs closed-loop feedback regulation on the target take-up and pay-off machine. The technical problems that the regulation of the take-up and pay-off machine in the prior art relies on manual or simple mechanical control, resulting in low regulation accuracy and low efficiency are solved. The technical effects that the encoder and the sensor work cooperatively, the take-up and pay-off accuracy and the control efficiency are improved are achieved.

The application discloses a correction control method and an automatic correction device for flexural deformation of an injection molded part. The control method comprises the following steps: obtaining matched heating parameters according to an input injection molded material to control the heating of a heating tube, slowly lowering a servo cylinder, and obtaining pressure detection values collected by pressure sensors; checking whether the pressure detection values pass the check; if not, controlling the servo cylinder to continue slowly lowering; if yes, updating the number of passes; if the number of passes is greater than a threshold, completing the correction process; if the number of passes is not greater than the threshold, controlling the servo cylinder to stop working and slowly lowering again after a preset interval. The correction control method collects pressure detection values of the injection molded part during correction by pressure sensors and checks the values. According to the checking result, the lowering correction process of the servo cylinder is automatically adjusted, thereby realizing intelligent correction control of the injection molded part and greatly improving the correction efficiency of the injection molded part.

This invention discloses a control system for intelligent medical mobile devices, relating to the field of intelligent medical device control technology. The system includes a data acquisition module, a feature acquisition module, a dynamic model, an error compensator, and a torque synthesis module. The data acquisition module collects kinematic data such as the device's three-axis acceleration, three-axis angular velocity, and center-of-mass coordinates. The feature acquisition module collects biometric data such as the pulse wave, respiratory rate, and blood oxygen saturation of the patient being served. Both types of data are simultaneously injected into an embedded dynamic model with preset no-load and load coupling inertial parameters. An ideal torque vector is obtained through forward dynamic inference. A closed-loop error compensator compares torque differences to generate torque correction increments, which are then superimposed to form a corrective control command. This system can adapt to different load conditions of the device, reduce torque output deviation, and improve the accuracy and adaptability of device control.

This application relates to the field of intelligent control technology and discloses a virtual-physical interactive digital twin full-closed-loop control method and system. The method includes: collecting multimodal time-series sensor data from manufacturing equipment to establish an execution-layer semantic benchmark library and a cognitive-layer confidence parameter set; selecting corresponding commands from three levels of control commands based on the confidence level and issuing them to the actuator; registering a semantic blind zone hypothesis when the confidence level is below a threshold and calculating the optimal detection action to superimpose and issue it to the actuator; and continuously issuing corrective control commands after updating the execution-layer semantic benchmark library using the detection residual vector. This application solves the problems of existing digital twin control systems being unable to recognize semantic mapping failures under operating condition drift and the virtual model being unable to actively apply directional detection to the physical actuator to eliminate cognitive blind zones.