38 results about "Control disorders" patented technology

The invention discloses an acupuncture intervention method after a pancreatic cancer operation, and aims to solve the problem that autonomic nerve regulation based on heart rate variability in an acupuncture stimulation closed loop is difficult to objectize and safely optimize. Heart rate variability time-frequency and time-domain features and respiratory coherence are extracted based on synchronous compression wavelets, a reliability score is generated, short-time heart rate variability and uncertainty are predicted through a neural state space model, a hierarchical control barrier function is generated through a context gating network, a safe MPC is constructed, and real-time quadratic programming solution is carried out. The heart rate variability enters the target domain and the stimulation parameter change rate and the energy consumption are reduced under the condition that safety constraints such as current, pulse width, charge quantity and impedance are met by combining safety verification and self-adaptive updating, and the reliability and the robustness of closed-loop control are improved.

The invention provides an agent security control network training method, a control method, electronic equipment and a medium, and relates to the technical field of agents. The training method comprises the steps of obtaining a training sample set; inputting the training sample set into a pre-constructed heterogeneous graph control barrier function network, outputting barrier function values corresponding to the intelligent agents, and updating parameters of the heterogeneous graph control barrier function network based on a preset first loss function; inputting the training sample set into a task controller, and outputting an action sequence; correcting the action sequence through quadratic programming under the forward security constraint model to obtain a central security action sequence of each agent; taking the central security action sequence as a supervision label, and training the distributed control network together with the training sample set; and repeating the steps until a preset iteration termination condition is met. According to the invention, the intelligent agents can still output high-reliability safety control actions under the condition of only depending on local observation.

The invention discloses a vertical farm integrated control system and method and a computer readable storage medium, and the system comprises a sensor group which is used for collecting environment and crop growth data; the quality prediction module is used for predicting future quality indexes of crops; the joint optimization module is used for constructing a multi-objective function containing quality indexes and energy cost and calculating first control input; the security shielding module defines a security set based on the control barrier function, and projects the first control input into the security set to generate a second control input when the first control input violates a security constraint. And the actuator group is used for adjusting the farm environment according to the second control input. According to the invention, through establishing the quality prediction model and performing joint optimization with the real-time energy cost, the system can actively use the electricity price valley period to adjust the environment on the premise of ensuring the target quality, the energy cost is significantly reduced, and the economic benefit is maximized.

The application discloses a vehicle control method and device based on a composite control barrier function, relates to the technical field of safety-critical control, and comprises the following steps: performing smoothing operation on an original control barrier function of a mixed relative degree to obtain a smoothed modified control barrier function; acquiring road geometry information and traffic rule information corresponding to a current driving scene of a vehicle to determine a candidate drivable area of the target vehicle; determining a road boundary constraint set participating in combination according to the boundary of the candidate drivable area, and determining a smoothed modified control barrier function set participating in combination and corresponding Top-r combination parameters; performing overall smoothing combination on the smoothed modified control barrier function set based on an implicit smoothing Top-r combination strategy to obtain an overall road composite safety function, so as to construct a safety constraint and generate a safety control instruction of the vehicle, thereby helping to reduce the constraint scale in complex intersection scenes and taking into account safety and control flexibility.

The present disclosure proposes an actuator failure compensation and safety control method based on a double-layer evaluation architecture, a storage medium and a system. The bottom layer uses an adaptive model predictive control to output a nominal instruction, a reinforcement learning model is built thereon to output a high-frequency residual compensation instruction to eliminate model mismatch, and a second evaluation network is independently built to evaluate the future global loss-of-control risk of the vehicle. The Lagrange dual optimization is introduced to convert the macro risk evaluation into a mathematical constraint to guide the first strategy network correction; the global risk index is used to feed forward the target cost weight of the predictive control to realize continuous flexible degradation, and the control barrier function is triggered at the limit boundary to realize discrete quadratic programming instruction projection. The present application overcomes the conservative strategy degradation defect caused by the traditional mixed reward, establishes an absolute safety line, and improves the control accuracy and safety robustness of the vehicle in complex nonlinear working conditions.

The present invention relates to methods and devices for providing a treatment recommendation based on a prediction of at least one Impulse Control Disorder occurrence for a patient undergoing a treatment with Dopamine Agonists.

The invention provides a robot manipulator control method, device and equipment and a medium, and relates to the technical field of manipulator control, the method comprises the steps that according to obtained stress data of two hands of a robot, a trained neural network model is adopted, and the mass center position of a carried object is determined; on the basis of the double-hand force-moment balance condition, according to the mass center position, the grabbing point position of the carried object grabbed by the double hands of the robot is determined, and the double hands of the robot are controlled to grab the carried object; on the basis of a model prediction control technology and a control obstacle function technology, according to the obtained obstacle information, a collision-free translational motion track of the carried object and collision-free translational motion tracks of the two hands of the robot are generated; and according to the obtained translation acceleration when the two hands of the robot carry the carried object, the expected posture when the two hands of the robot carry the carried object is determined. According to the method, through logic connection among the steps, the defects of object grabbing, two-hand posture adjustment and obstacle avoidance links in the prior art are comprehensively overcome.

The invention provides a control method of a motor system and electronic equipment, the motor system comprises a target motor and a plurality of sensors, the plurality of sensors comprise a target sensor and an associated sensor, the target sensor is used for detecting a target parameter to obtain a detection value of the target parameter, and the associated sensor is used for predicting the target parameter to obtain a detection value of the target parameter; the method comprises the following steps: determining a weight coefficient according to the working state of a target sensor; the weight coefficient represents the credibility of the detection value of the target parameter; determining a final control parameter corresponding to the current moment of the target motor according to the weight coefficient, the detection value and the predicted value; and controlling the target motor based on the final control parameter. Therefore, the working state of the sensor, such as a fault, can be quantified into the weight coefficient, and the final control parameter is determined based on the predicted value corresponding to the associated sensor in combination with the weight coefficient, so that control disorder or shutdown of the motor can be avoided when the sensor fails, and the system stability is improved.

The application discloses a decorative plate production line stacking process automation industrial control system, and belongs to the technical field of automation control, and comprises the following steps: collecting and standardizing processing multi-dimensional data representing system operation state in real time through a system state monitoring module, at least including production efficiency indexes representing unit time output, dimensionless end effector performance drift, normalized environmental humidity, and execution certainty indexes quantifying single task completion quality; a dynamic risk assessment module receives multi-dimensional operation data, calculates based on a preset correction entropy model and a dynamic safety baseline model, quantifies control disorder caused by execution certainty decrease and environmental humidity interference, and generates correction entropy representing current control disorder degree, so that the application provides more accurate and timely early warning before failure occurs.

The invention provides a continuum robot safety critical control method which comprises the following steps: establishing a dynamical model of a continuum robot, and designing a control barrier function; designing an extended state observer; designing a nominal control law; a safety critical control law is designed, and the safety critical control law is obtained by integrating Lyapunov-like constraints into a quadratic programming problem of a control barrier function; obtaining a nominal control signal after the initial control input and the state observation result pass through a nominal control law; the nominal control signal passes through a safety critical control law to obtain control input after safety critical control; the control input after safety critical control and external interference input are input to the continuum robot, and control output is obtained; and after the control output and the control input after the safety critical control are input to the extended state observer, a state observation result is output. According to the method, the safety of the system state can be always kept without violation even under a complex safety boundary condition.

The invention discloses an interface rolling control method and device, electronic equipment and a storage medium, and relates to the technical field of interface interaction.The method comprises the steps that when the wrist of a target user rotates, accumulated rotation angle information is calculated according to angular velocity information of wrist rotation, interface rolling parameter information is mapped according to the accumulated rotation angle information, and the interface rolling parameter information is obtained; and controlling the interface rolling of the target equipment according to the interface rolling parameter information. Screen rolling is controlled based on the accumulated rotation angle corresponding to the angular velocity, so that screen rolling control does not depend on the gravity direction, the problem of rolling control disorder caused by horizontal and vertical posture changes of a wrist is solved, and the stability of interface rolling control is improved.

The invention relates to an automatic driving control method based on elastic safety critical control. The method comprises the steps that the vehicle state, the reference path and the environment information of automatic driving are obtained in real time; establishing a control-oriented automatic driving vehicle dynamics controlled model, and establishing an optimization problem considering time-varying safety constraints; designing an elastic safety critical control model: estimating time-varying disturbance, converting safety and stability into constraints through an elastic control barrier function of a self-adaptive penalty parameter, establishing a unified constrained optimization framework, and integrating safety constraints, stability constraints and control input constraints into a quadratic optimization problem; and solving a secondary optimization problem of the elastic safety critical control model in real time to obtain safety control input of the automatic driving vehicle. Compared with the prior art, the method has the advantages that the vehicle can accurately track the reference path under the time-varying disturbance, and the system safety and stability are ensured.

Aiming at the problem of missile terminal guidance with no-fly zone and impact angle constraints, a model predictive static programming guidance law based on control barrier function is proposed. The proposed algorithm inherits the high efficiency of the traditional model predictive static programming while ensuring the no-fly zone constraints. The main reasons are as follows: first, the projection operator under the linear inequality constraints has an explicit expression, avoiding additional optimization solving; second, due to the forward invariance of the control barrier function, the no-fly zone constraints do not need to be imposed at all discrete nodes, but only at one or a few discrete nodes, reducing the scale of solving. The simulation results show that the proposed algorithm can meet the no-fly zone and impact angle constraints while ensuring high computational efficiency. Subsequent research will focus on trajectory optimization and guidance problems with multiple no-fly zone constraints, even dynamic no-fly zone constraints.

The invention provides a reinforcement learning-based hybrid vehicle queue safety control method and system, and the method comprises the steps: constructing a communication framework of a hybrid vehicle queue, and obtaining the state data of the hybrid vehicle queue in real time; dividing the mixed vehicle queue into a plurality of sub-queues; constructing a dynamic model of each sub-queue; a trained parameter sharing SAC reinforcement learning algorithm is used to generate a preliminary control action for the networked autonomous vehicle in each sub-queue; and constructing a safety control layer fusing a control obstacle function and quadratic programming, performing safety correction on the preliminary control action based on the safety control layer, generating a control output instruction meeting a collision avoidance constraint, and issuing the control output instruction to each networked autonomous vehicle. And then a safety control layer formed by a control barrier function and quadratic programming is adopted to provide safety guarantee, and the safety of automatic driving control is improved.

A computer includes a processor and a memory, and the memory stores instructions executable by the processor to formulate a plurality of control barrier functions for a host vehicle, each control barrier function based on a respective kinematic state of a respective target vehicle; determine respective CBF input accelerations based on the respective control barrier functions; formulate a control-Lyapunov function for the host vehicle based on a target speed for the host vehicle; determine a Lyapunov input acceleration based on the control-Lyapunov function; select an input acceleration from an acceleration set including the CBF input accelerations and the Lyapunov input acceleration; and actuate a component of the host vehicle according to the selected input acceleration.

The disclosure relates to a therapeutic method of treating an impulse-control disorder comprising administering to an individual afflicted with an impulse- control disorder an amount of at least one glucagon-like-peptide-1 (GLP-1) effective to reduce or eliminate at least one of the symptoms of the impulse- control disorder.

This invention discloses a source-tracing auxiliary system and method for ankle joint motor control disorders. The source-tracing auxiliary system includes an electromyography (EMG) acquisition system, a database, and a main control device. The main control device further includes an EMG processing and feature value extraction module and a muscle state assessment module. The source-tracing auxiliary method includes the following steps: acquiring surface EMG signals of the ankle-related muscles during active dorsiflexion and plantar flexion, static standing, and passive stretch reflex; extracting relevant feature values ​​from the acquired surface EMG signals; autonomously and accurately classifying the state types of each ankle-related muscle based on the feature values, and generating a report. This invention can assess and determine the state type of the ankle-related muscles on the affected side and generate a source-tracing auxiliary report, providing assistance for the source-tracing diagnosis of ankle joint motor control disorders and improving the accuracy and efficiency of source-tracing diagnosis of ankle joint motor control disorders.

The application discloses a hydraulic arm safety control method and device based on ASMO and CBF, adopts a D-H method to establish a kinematics model of a hydraulic heavy-load mechanical arm, converts a kinematics uncertainty problem into an equivalent disturbance estimation problem, designs an adaptive sliding mode observer for the uncertain kinematics system to realize estimation on the kinematics disturbance, and automatically approaches a real value of an upper limit of the disturbance through adaptive adjustment, constructs an ASMO-CBF with an observer correction term based on a disturbance estimation result, and realizes safety correction on a desired joint speed by solving a constraint optimization problem QP.

This invention provides a multi-tugboat cooperative assisted safe berthing control system, comprising: a kinematic control module that generates speed commands based on finite-time control theory to rapidly converge pose errors; a speed constraint optimization module that optimizes speed through quadratic programming, using safe distances between tugboats and speed limits as constraints; a disturbance observation module that uses a robust precise differential extended state observer to estimate the system and environmental disturbances online; a dynamic control module that combines optimized speed and disturbance estimates, using a finite-time sliding mode control algorithm to generate control commands for precise tracking; and a safety constraint module that transforms safe distance requirements into linear inequality constraints based on a control obstacle function, and integrates control commands through quadratic programming to achieve dynamic collision avoidance and safe operation. This invention improves berthing efficiency, accuracy, and safety, enhancing control precision and operational stability during the berthing process while ensuring the safety of tugboat cooperation.

The invention provides a multi-tug cooperative auxiliary safe berthing control system, which comprises a kinematics control module for generating a speed instruction based on a finite time control theory so as to quickly converge a pose error; the speed constraint optimization module is used for outputting an optimized speed through quadratic programming by taking the safety distance between tugs and the speed limit as constraints; the disturbance observation module is used for estimating system and environment disturbance on line by adopting a robust submicron expansion state observer; the dynamic control module is used for generating a control instruction by using a finite time sliding mode control algorithm in combination with the optimized speed and the disturbance estimation value to realize accurate tracking; and the safety constraint module is used for converting a safety distance requirement into linear inequality constraint based on a control obstacle function, and realizing dynamic collision avoidance and safe operation through quadratic programming fusion control instructions. According to the system, the berthing efficiency, precision and safety are improved, and the control precision and operation stability in the berthing process can be improved while the cooperative safety of the tug is guaranteed.

The invention discloses a spacecraft relative motion obstacle avoidance control method based on an anti-interference control obstacle function, and the method comprises the steps: 1, initializing a spacecraft system state and sampling time through building a spacecraft relative motion model with external disturbance; 2, designing an unknown dynamic estimator, and adopting a low-pass filter to obtain estimation of external disturbance; 3, constructing a relative motion tracking error of the spacecraft, designing a Lyapunov function, and designing a nominal controller by adopting a Lyapunov stability theory; and 4, constructing an obstacle function of the spacecraft and the obstacle, designing an obstacle avoidance controller according to the safety constraint of the control obstacle function, and finally integrating the obstacle avoidance controller to actual control input. According to the invention, formalized guarantee of safety is realized, and a robust item based on an unknown dynamic estimator is embedded into a control obstacle function constraint condition, so that a system state can be strictly prevented from entering a dangerous area even under continuous disturbance, and a provable obstacle avoidance method is provided.

The invention discloses a micro-grid cluster cooperative safety control method and system. The system comprises a micro-grid cluster MGC system, a switching control barrier function SCBF, a switching barrier Lyapunov function SBLF, a task allocation decision-making layer, a time-varying communication topology and a distributed secondary safety control protocol. An instruction switching signal output by the task distribution decision-making layer and a topology switching signal output by the time-varying communication topology are respectively input into an SCBF, an SBLF and a distributed secondary safety control protocol; the output of the SCBF and the output of the SBLF are used as the input of a distributed secondary safety control protocol; the output of the distributed secondary safety control protocol is input into the MGC system; and the output of the MGC system is respectively input to the SCBF, the SBLF and the distributed secondary safety control protocol. According to the system provided by the embodiment of the invention, the inertia and damping of the MGC system are improved, the voltage support is enhanced, the MGC system has the characteristics of high inertia, high damping and strong voltage support, and the strength and stability margin of the MGC system are improved.

The present application belongs to the technical field of intelligent control and amphibious robot, especially relates to a control method for tracking and obstacle avoidance of amphibious tracked vehicle based on control barrier function and reinforcement learning, which comprises the following steps: establishing a dynamic mathematical model of amphibious tracked vehicle in different environments; introducing a pre-aiming point mechanism to convert the position obstacle avoidance constraint into a speed constraint; deriving a safety constraint condition based on the control barrier function, and solving a safety reference speed with the nominal reference speed as the optimization goal; taking the safety reference speed as the tracking target, and using a reinforcement learning network to estimate unknown disturbances online and output a control law; detecting the water entry state and executing water-land mode switching, switching the corresponding dynamic model and network parameters; and iteratively executing until the tracking and obstacle avoidance task is completed, so that the present application realizes the unification of real-time global optimal obstacle avoidance and high-precision trajectory tracking in complex cross-domain environments.

The application provides a floating offshore wind turbine safety control method, steps comprising: constructing a mechanical-electrical coupling model based on a secondary problem description, converting into a control affine system, and studying the relationship between the blade pitch angle and the generator rotor angular velocity; constructing a control Lyapunov function candidate according to the speed reference value under the over-rating operation mode, constructing a control barrier function candidate according to the platform pitch angle threshold, and verifying the rationality; taking the control Lyapunov function and the control barrier function as constraints, constructing a control framework of a nonlinear optimization problem, and solving the blade pitch angle control output. The method can effectively improve the generator speed tracking performance under the over-rating condition on the premise that the platform pitch angle does not exceed the safety boundary, enhance the adaptability of the control strategy to the complex marine environment and model uncertainty, and has good engineering application value and popularization prospect.

The invention relates to the technical field of control engineering, in particular to a robot control method and system based on a predefined time control obstacle function, and the method comprises the steps: constructing a high-order control obstacle function; constructing a robot dynamic model; the robot dynamic model is converted into a nonlinear affine system standard form with uncertain items; designing a predefined time adaptive law, and constructing a high-order adaptive control barrier function; and controlling the robot by adopting the high-order self-adaptive control obstacle function. According to the embodiment of the invention, the parameter adaptive law with the predefined time convergence characteristic is designed, so that the estimation of the uncertain parameter is quickly converged within the set time, and the response speed and decision accuracy of the control barrier function are remarkably improved. According to the embodiment of the invention, accurate uncertain boundary knowledge is not required, and even if the estimation of the upper bound is relatively conservative, the system can still realize a non-conservative control effect on the premise of ensuring safety.

The invention discloses an aircraft guidance and control integrated design method based on a control barrier function. The method comprises the following steps: constructing dynamics and kinematics equations of a hypersonic aircraft; constructing a hypersonic flight vehicle gliding section guidance and control integrated model; constructing an expected attack angle instruction limiting range; acquiring an expected speed inclination angle and track yaw angle instruction; setting a control scheme for designing guidance and control integration of the gliding section of the hypersonic flight vehicle based on a barrier Lyapunov function in combination with a dynamic surface control method; the overall performance of the guidance control system for the gliding section of the hypersonic aircraft is obviously improved; designing an expected attack angle instruction limiting range to restrain the state quantity of the aircraft, thereby ensuring the safety performance of the aircraft and improving the stability of the system; the method can overcome the difficulties of strong coupling, nonlinearity, uncertainty, strong constraint and the like of a hypersonic aircraft system, and ensures that the system can accurately reach a target position under the condition of meeting various constraints.

The application discloses a kind of based on deep reinforcement learning's vehicle networking cooperative driving control method and system, belong to the field of automatic driving, the method includes: receiving the state data of main car itself and the state data of at least one neighbor car received by vehicle networking communication, generate smooth state estimation and the uncertainty feature for representing data credibility;The main car and neighbor car are constructed as dynamic vehicle interaction graph;Dynamic vehicle interaction graph is input to graph attention feature extraction model to extract space-time interaction features;Space-time interaction features are input to deep reinforcement learning strategy network to generate preliminary reference control instruction;A quadratic programming problem is constructed, the optimization target of quadratic programming problem is to minimize the deviation of reference control instruction, and the safety constraint derived from meeting control barrier function is constraint condition;Solve quadratic programming problem to obtain final control instruction.The application can realize efficient, intelligent cooperative driving under the premise of ensuring safety.

The application discloses a strict safety trajectory optimization control method and system of an underactuated bridge crane, and the method comprises the following steps: constructing a bridge crane system variable rope length dynamics equation model; setting end point constraints, speed constraints and acceleration constraints for a given initial trolley and rope length desired operation trajectory; optimizing the trolley reference speed and the rope length reference speed based on the end point constraints, the speed constraints and the acceleration constraints; using a first control barrier function to perform angle constraints, setting the upper limit and the lower limit of the swing angle state variable; using a second control barrier function to perform obstacle avoidance constraints; obtaining a cost function according to the first control barrier function and the second control barrier function; and obtaining the control input mu that satisfies the angle constraints and the obstacle avoidance constraints after correction through the solution of a QP problem. The strict safety trajectory optimization control method and system of the underactuated bridge crane provided by the application can effectively avoid obstacles and reduce the swing of the load while optimizing the operation trajectory.

The application provides a robot manipulator control method, device, equipment and medium, and relates to the technical field of manipulator control. The method comprises the following steps: determining the center of mass position of a carried object by using a trained neural network model according to obtained force data of robot hands; determining the grasping point position of the robot hands for grasping the carried object according to the center of mass position based on a hand force-torque balance condition, and controlling the robot hands to grasp the carried object; generating a collision-free translation motion trajectory of the carried object and a collision-free translation motion trajectory of the robot hands based on model predictive control technology and control barrier function technology according to obtained obstacle information; and determining the expected posture of the robot hands when carrying the carried object according to obtained translation acceleration of the robot hands when carrying the carried object. The application comprehensively makes up for the deficiencies of the prior art in the aspects of object grasping, hand posture adjustment and obstacle avoidance through logical connection between steps.

The disclosure relates to a therapeutic method of treating an impulse-control disorder comprising administering to an individual afflicted with an impulse- control disorder an amount of at least one glucagon-like-peptide-1 (GLP-1) effective to reduce or eliminate at least one of the symptoms of the impulse- control disorder.