197 results about "Control force" patented technology

The invention relates to the technical field of maglev motor dynamics and control, and discloses a bearingless asynchronous motor multi-frequency harmonic vibration cooperative suppression method, which comprises the following steps: acquiring radial displacement signals and mechanical angular velocity of a rotor in an x axis and a y axis under a stator fixed coordinate system, and stacking double-axis displacement signals into an observation vector; calculating a weighting coefficient of the normalized residual error through a Huber robust cost function, and outputting the amplitude, the phase and the confidence of each order of harmonic wave; estimating the total disturbance of the system by using a linear extended state observer with harmonic energy injection, wherein the total disturbance is obtained by weighted summation according to the confidence, order and mechanical angular velocity of each order of harmonic; calculating a feedback control force according to the total disturbance estimated value, and superposing the feedforward compensation force and the feedback control force to generate a total suspension force instruction; and according to the total suspension force instruction, inversely calculating a suspension winding current reference value and outputting a control instruction. According to the invention, a mutually complementary cooperative suppression mechanism is formed, and the suppression precision of multi-frequency vibration is improved.

The invention discloses a starling group-based unmanned aerial vehicle cluster safety control and steering consensus method, which comprises the following steps of: firstly, constructing a three-layer leader-follower network simulating a pigeon group hierarchical structure, and establishing a dynamic physical neighbor topology in combination with a communication radius; an improved artificial potential field method is designed, a damage area, a rejection area, an alignment area and an attraction area are divided, a smooth concave-convex function is introduced to adjust a potential field, and cooperative force and obstacle rejection force for maintaining an expected distance are generated; an inertial spin mechanism is introduced, when a maneuvering intention is detected, expected spin and perceptual spin are calculated, a spin kinetic equation is utilized to update a state, and a transverse consensus coupling force for driving a cluster to rapidly and synchronously steer is generated. And finally, for different levels of unmanned aerial vehicles, differentially fusing the control force, and updating the cluster state through numerical integration. According to the method, the internal safety distance of cluster high-density flight is guaranteed, the overall steering speed and consistency of the cluster facing sudden obstacles are remarkably improved, and the robustness of the system in a dynamic environment is enhanced.

The invention provides an anti-disturbance unmanned aerial vehicle trajectory tracking control method, system and device and a medium. The method comprises the following steps: establishing a six-degree-of-freedom rigid body dynamic model of a four-rotor unmanned aerial vehicle; selecting an unmanned aerial vehicle feasible trajectory of the kinetic model as an expected trajectory, and introducing a coupling attitude; designing a position loop control law, generating a virtual control force based on a smooth terminal sliding mode control surface, and calculating an actual thrust; designing an attitude loop control law, representing an attitude error based on Lie group SO (3), constructing an attitude tracking controller with a stable global index, and outputting a control torque; constructing a four-order state observer to estimate the angular velocity and the angular acceleration of the coupling attitude, and replacing a true value with an estimated value to participate in control law calculation; thrust and control torque are converted into thrust input of the unmanned aerial vehicle, the unmanned aerial vehicle is driven to track an expected trajectory, and the problems that in trajectory tracking control of the four-rotor unmanned aerial vehicle, dependence on position acceleration measurement is high, noise is amplified through differential derivation, disturbance rejection is weak, and real-time calculation burden is large are solved.

The invention relates to a transverse movement berthing control method considering main thrust idling, which comprises the following steps of: dividing transverse movement berthing control into a controller part and a thrust distribution part, designing a three-degree-of-freedom controller for ship movement by utilizing a nonlinear disturbance observer in the aspect of the controller, and calculating force and moment required for transverse movement of a ship; in the aspect of thrust distribution, the discontinuity of a main thrust idle speed area and rudder force is considered, a thrust action range is decomposed into nine action ranges according to the thrust state of double main thrusters, multi-domain switching logic is designed to complete thrust distribution of control force, and therefore closed-loop control over ship transverse moving berthing is achieved. A simulation result proves the effectiveness of the method.

The invention belongs to the technical field of multi-agent cooperative intelligent control, and particularly relates to a multi-mechanical-arm cooperative control method under unknown disturbance, which comprises the following steps: establishing a dynamic model of a strict feedback nonlinear multi-mechanical-arm cooperative system; constructing a communication topological structure of the system through a virtual coupling mechanism; constructing a unified system collaborative error model; introducing a preset time function and a global preset performance function, mapping errors to an adjustable constraint performance space, and ensuring that all error signals are dynamically converged in a performance range set by a user; a task layer shape control force and a pose control force are designed under a distributed gradient descent method framework, a task layer overall control force is constructed, and multiple mechanical arms are driven to move cooperatively and keep a stable formation in a task space; designing a joint layer distributed adaptive control law under unknown disturbance through passive characteristics of a nonlinear system; according to the method, cooperative convergence and stable control of the multi-mechanical-arm system within the preset time can be achieved under unknown disturbance.

The invention discloses a prestress tension control method and system based on a multi-parameter coupling loss law, a medium and a processor, and relates to the technical field of prestress tension control. Common and specific parameters of a bridge and a tunnel are collected and preprocessed, and a scene-divided multi-parameter coupling loss law model is constructed; friction resistance, retraction, aging and environment loss rate are accurately quantified; determining a tension control force sequence in combination with an effective pre-stress target value, and constructing by adopting a hierarchical regulation and control strategy; and through post-construction effective prestress detection and deviation verification, parameter iteration and closed-loop management are realized. The problems that in the prior art, multi-parameter coupling loss quantization is insufficient, scene adaptability is deficient, and construction-detection closed loop fracture occurs are solved, the tensioning control precision is greatly improved, the method is adaptive to multiple scenes of bridges and tunnels, and the safety and stability of an engineering structure are guaranteed.

This invention relates to the field of engineering structure performance prediction and optimization technology, specifically disclosing an engineering full life cycle performance prediction and optimization system. The system collects time-series data of load, acceleration, and strain of engineering structures under extreme loads; constructs a physical consistency mapping operator based on real-time input data and finite element prior information, outputting a predicted response field; performs intrinsic orthogonal decomposition on the finite element prior information to extract the basis vector and integral point subset, trains the time-series evolution rule of the basis coefficients, and outputs low-dimensional basis coefficient prediction values; compares the real-time input data with the predicted values, and triggers online basis assimilation update when the deviation exceeds a threshold, assimilating the low-dimensional basis coefficients to output a corrected stiffness field; substitutes the corrected stiffness field into the time-series evolution rule to extrapolate the future response field, generating a smooth control force command for the active mass damper; this invention achieves real-time closed-loop optimization control of engineering structures under extreme loads.

The clamping device comprises a clamping mechanism, a stop mechanism, a push rod, a limiting mechanism, a base and an elastic mechanism, by applying downward pressure, the clamping device is changed from a folded state to an unfolded state so as to clamp a target ornament, and after the downward pressure disappears by loosening the clamping device, the target ornament is clamped by the elastic mechanism. The clamping device automatically clamps a target ornament by means of deformation restoring force of the elastic mechanism, linear pressing motion of the push rod is converted into control force to the interior of the clamping piece through the limiting mechanism, and resetting of the elastic mechanism is converted into control force to the exterior of the clamping piece through the push rod. The opening and closing states of the clamping pieces are precisely regulated and controlled through the internal and external synergistic effect; the interactive design conforming to the operation intuition of the user that opening is achieved after pressing is conducted and closing is achieved after loosening is conducted brings pleasant operation experience and reliable psychological expectation to the user.

The application discloses a multi-power unit high-speed train control method, and relates to the technical field of train control.The method comprises the following steps: performing stress analysis on a multi-power unit high-speed train to determine a high-speed train dynamics model; determining a discrete-time nonlinear model based on the high-speed train dynamics model; constructing control force and control force change rate constraints; converting the discrete-time nonlinear model to obtain a dynamic linearization data model; designing a parameter estimation algorithm and an adaptive extended state observer; designing a discrete-time terminal sliding mode function and a hyperbolic curve type reaching law; determining a data-driven terminal sliding mode control scheme based on the dynamic linearization data model, the parameter estimation algorithm, the adaptive extended state observer, the discrete-time terminal sliding mode function and the hyperbolic curve type reaching law; and performing tracking control operation on the multi-power unit high-speed train by using the data-driven terminal sliding mode control scheme.The application improves the control precision of high-speed train operation while reducing the sliding mode chattering.

The invention discloses a composite wing unmanned aerial vehicle transition process control method based on reinforcement learning, and the method comprises the steps: building a composite wing unmanned aerial vehicle transition process control model based on a PPO reinforcement learning algorithm; in the transition process of the composite wing unmanned aerial vehicle, rotor wing virtual control force and torque output by a rotor wing mode controller, fixed wing virtual control force and torque output by a fixed wing mode controller, and rotor wing virtual control weight and fixed wing virtual control weight output by a composite wing unmanned aerial vehicle transition process control model are obtained; and after weighting processing is carried out, distribution execution is carried out, composite wing unmanned aerial vehicle transition process control parameters are obtained, and control over the composite wing unmanned aerial vehicle transition process is completed. According to the method disclosed by the invention, the stability of the subsystem is guaranteed by a classical control algorithm, and the method is oriented to multi-performance index optimization, so that the network training convergence of the transition process can be quickly realized, and the stable and high-energy-efficiency control of the strong environment adaptability of the transition process of the composite wing unmanned aerial vehicle is comprehensively realized.

The application discloses an intelligent agent reinforcement learning method for controlling vehicle driving, and aims at training a control intelligent agent for vehicle driving in the field of unmanned driving. The intelligent agent receives image data, radar data, motion data and the like observed in the process of vehicle driving as state input in real time, calculates a steering wheel turning angle, a throttle control strength and a brake control strength as action output, and obtains corresponding reward value feedback according to the motion condition of the vehicle, and the training target is to realize smooth driving of the vehicle under the premise of ensuring driving safety, to maximize the normal speed, and to avoid collision with other vehicles or objects. Driving speed, driving process loss, steering wheel turning angle / throttle / brake operation fitting degree and the like will affect the reward feedback, and the intelligent agent needs to find a suitable strategy to maximize the driving score. The application further discloses a corresponding intelligent agent reinforcement learning device for controlling vehicle driving.

The invention discloses a high-speed train prediction cascade control method and device based on PPC and MPC, and relates to the field of train control, and the method comprises the steps: obtaining a speed prediction value at a t + 1 moment according to a GS-TCN model; processing the state error at the moment t by adopting PPC to obtain an initial control force at the moment t + 1 under the initial iteration times; according to the speed prediction value at the t + 1 moment, the initial control force at the t + 1 moment under the current number of iterations and the GS-TCN model, obtaining a speed prediction value at the t + 2 moment, and further obtaining a speed error at the t + 2 moment under the current number of iterations; the speed error at the t + 2 moment under the current iteration number and the initial control force at the t + 1 moment are input into MPC, the initial control force at the t + 1 moment under the next iteration number is obtained, and finally the optimal control force at the t + 1 moment is obtained. Rolling optimization can be efficiently completed within the effective period, and the control delay problem caused by calculation time consumption is effectively relieved.

The embodiment of the invention provides an active suspension control method, system and device based on bandwidth layering and a vehicle, and belongs to the technical field of vehicle control. The method comprises the steps that vehicle body posture information and driving state information of a vehicle are obtained, a low-frequency force component is determined according to the vehicle body posture information and the driving state information, and the frequency value of the frequency domain component of the low-frequency force component is smaller than or equal to the cut-off frequency; the three-axis acceleration of a suspension controller corresponding to each wheel of the vehicle is obtained, a high-frequency force component is determined according to the three-axis acceleration, and the frequency value of the frequency domain component of the high-frequency force component is larger than the cut-off frequency; and target control force is determined according to the low-frequency force component and the high-frequency force component, and the suspension controller is controlled to adjust the posture of the vehicle according to the target control force. The embodiment of the invention aims to improve the damping effect and driving experience of the vehicle.

Aircraft performance modification system including a center-of-gravity (CG) subsystem and processor system(s) operatively integrated with components of the aircraft. The CG subsystem is configured to adjust the location of the aircraft CG within, and at times outside, a range of CG locations specific to the aircraft. The processor system(s) is / are configured to manage and concurrently adjust the aircraft CG, aircraft flight controls settings, and aircraft engine power control settings to manipulate control forces about one or more axes of the aircraft, and for automatically or autonomously controlling adjustments to (i) the aircraft CG such that the CG location is within, and at times outside, the range of CG locations, (ii) movement and positions of aircraft flight control surfaces, and (iii) aircraft engine(s) output power, to reduce drag by providing / controlling aircraft configurations that result in adjusted drag, yielding flight performance improvements attributable to decreased drag, and to prevent a stalled condition.

This application relates to a control method, apparatus, flexible parallel robot, and storage medium for a flexible parallel robot. The method includes: acquiring the target force and target motion trajectory of the robot, as well as the current output position and current output torque of each flexible drive joint in the robot; determining the position adjustment amount corresponding to the control force of the robot based on the force deviation or torque deviation between the target force and the current output torque; determining the velocity control amount corresponding to the control position of the robot based on the trajectory deviation or position deviation between the target motion trajectory and the current output position, and the position adjustment amount; and controlling the drive components of each flexible drive joint based on the velocity control amount to drive the robot's flexible motion. This method simplifies control complexity, improves control stability, absorbs impact energy, and achieves flexible actuation in both mechanics and kinematics.

The application relates to the technical field of electromagnetic suspension, and provides a multi-layer loop control method and system of an electromagnetic suspension for coordinating vehicle performance and a vehicle.The method comprises the following steps: obtaining vehicle and motor state information through a preset sensing unit; a suspension controller calculates target control force and generates a control instruction based on a vehicle performance target and a control model; after receiving the instruction, a driving layer of an electromagnetic execution unit adopts an algorithm of FOC three-closed-loop control combined with a state observer, a current loop proportional integral control is used to track a target current, a speed loop and a position loop construct a sliding mode surface based on a state deviation through sliding mode control to design a control law, and a motor is driven to output corresponding electromagnetic force through coordinate inversion and space vector pulse width modulation.The method simultaneously compensates for the interference of motor electromagnetic characteristics, vehicle posture changes and external disturbances on motor output, accurately realizes suspension actuation control through multi-layer loop coordinated control, and effectively improves vehicle driving smoothness and safety.

This application relates to the field of vehicle suspension control technology, and more specifically to a fully active suspension control method, an electronic device and a computer-readable storage medium capable of implementing the method. The method includes: receiving road surface elevation information of the road ahead of the vehicle; filtering the road surface elevation information to obtain a desired vertical motion trajectory of the vehicle body; calculating a feedforward control force for controlling the fully active suspension based on the road surface elevation information and the desired vertical motion trajectory of the vehicle body; detecting the actual vertical motion state of the vehicle body and calculating a feedback control force based on the actual vertical motion state of the vehicle body; and superimposing the feedforward control force and the feedback control force to obtain a total control force, and outputting it to the actuator of the fully active suspension.

The invention provides a mechanical system constraint following control method based on a historical state information neural network, and relates to the technical field of automatic control, and the method specifically comprises the following steps: building a constraint-containing mechanical system dynamic model, and designing a nominal control part based on a U-K theory; establishing a mechanical system lumped disturbance model, constructing a feature vector including the historical state of the mechanical system, and obtaining an offline training data set; using a multilayer perceptron neural network to learn inverse dynamic mapping in an off-line manner, and establishing a lumped disturbance estimation model; performing low-pass filtering smoothing processing on the original prediction of the multi-layer perceptron neural network, and forming a total control law in combination with nominal control force to be applied to a mechanical system; and performing simulation verification on the mechanical system. According to the technical scheme, the problems that in the prior art, robust control is high in conservative property, buffeting is likely to be caused by high gain, and characteristics are not matched and signals shake in a pure data driving method are solved.

The application discloses a three-arm space robot multi-modal shared teleoperation system and method, which comprises at least a master teleoperation system, a communication module and a slave robot system, the master teleoperation system comprises at least two force feedback hand controllers, a microphone array and upper computer software, and the slave robot system comprises two end work arms equipped with grippers, an observation arm with a double-eye camera at the end, a vision unit, a force sensor and lower computer software; an operator controls two work arms of the extravehicular robot to perform a task, and controls the observation arm to obtain a better local visual angle; the method fuses the multi-modal teleoperation control method comprising pose control, voice control and force control with the robot autonomous control through a shared control algorithm, can realize the position, posture and contact force of the robot controlled by the operator and the robot autonomously performing other relatively simple tasks according to the demand of the operator, reduces the operation burden of the operator and improves the control efficiency.

The application provides an active control method of an electro-hydraulic servo system based on finite time control. The method dynamically restricts the error boundary through a time-varying obstacle Lyapunov function, combines a backstepping recursive control framework with a fractional order convergence term, significantly improves the convergence speed and anti-interference ability of the system, effectively suppresses the overshoot problem and chattering phenomenon of PID control, designs a feedforward compensation and an adaptive disturbance observation mechanism in view of the nonlinear characteristics and anti-interference problems of the electro-hydraulic servo system, and still can quickly recover stability under strong external disturbance, thereby ensuring the accuracy and robustness of the control force output, and based on the similarity criterion, a scale experiment platform is constructed to verify the engineering universality of the control strategy, and an efficient, reliable and easy-to-deploy solution is provided for the anti-seismic control of complex structures.

This invention relates to a method, device, and electronic equipment for underwater vehicle formation control. The method includes: constructing a formation configuration maintenance framework based on rigid graph theory, and determining the relative positional relationships between vehicles through distance constraints; establishing an obstacle avoidance control model based on the artificial potential field method, calculating the potential field force based on the distance between the vehicle and the obstacle and the distance between vehicles, thereby achieving obstacle avoidance and collision prevention between vehicles; performing online identification and compensation of unmodeled dynamics and external disturbances in the vehicle dynamics model based on a radial basis function neural network, using a Gaussian function as the activation function; and vector-fusing the formation maintenance control force, potential field force, and compensation output of the radial basis function neural network to generate control commands for each vehicle, thereby achieving formation control. This invention can achieve high-precision formation maintenance, effective obstacle avoidance, and adaptive disturbance compensation capabilities in complex underwater environments.

The invention relates to the technical field of electromagnetic suspensions, and provides an electromagnetic suspension multi-layer loop control method and system for vehicle performance coordination and a vehicle. Comprising the steps that vehicle and motor state information is obtained through a preset sensing unit, and a suspension controller calculates and outputs target control force through a control model based on a whole vehicle performance target and generates a control instruction; after an electromagnetic execution unit driving layer receives an instruction, FOC three-closed-loop control is combined with an algorithm of a state observer, target current is controlled and tracked through current loop proportional-integral, a speed loop and a position loop construct a sliding mode surface design control law based on state deviation through sliding mode control, and the target current is obtained. And the motor is driven to output corresponding electromagnetic acting force through coordinate inverse transformation and space vector pulse width modulation. And meanwhile, interference of electromagnetic characteristics of the motor, vehicle posture changes and external disturbance on motor output is compensated, suspension actuation control is accurately achieved through multi-layer loop coordination control, and vehicle running smoothness and safety are effectively improved.

The present application relates to a kind of large-span steel truss beam cable-stayed bridge closure intelligent control system and method, adopt beidou positioning instrument, wireless tiltmeter and IMU sensor etc. and form closure intelligent control system, reduce the difficulty of construction survey, improve the measurement efficiency.The data obtained by the automatic monitoring system are analyzed to analyze the closure mouth state and control the force analysis of construction adjustment, the closure mouth state is adjusted using full-automatic hydraulic jack, and the large-span steel truss beam cable-stayed bridge is closed in the middle of the span by combining fine adjustment measures.The method helps to reduce the waiting operation time of large cantilever construction, solves the problems of low construction efficiency, different synchronization of closure adjustment measures, and realizes the accurate closure of large-span steel truss beam cable-stayed bridge.

This application discloses a vehicle suspension control method, apparatus, device, and readable storage medium. The method includes the steps of: acquiring the current pitch angle and current roll angle of the vehicle; calculating vehicle control parameters based on the vehicle state parameters and the current pitch angle and current roll angle; and determining the active control force that the vehicle's active suspension needs to output based on the vehicle control parameters. The vehicle state parameters include lateral acceleration, and the vehicle control parameters include pitch control torque and roll control torque. This application realizes the calculation of vehicle control parameters based on the determined vehicle state parameters and the current pitch angle and current roll angle, and the determination of the active control force that the vehicle's active suspension needs to output based on the vehicle control parameters. This active control force improves vehicle stability, thereby improving vehicle handling during driving.

The invention relates to the technical field of vehicle active suspension control, in particular to an active suspension system control method and device, a vehicle and a medium, and the method comprises the steps: obtaining elevation information of a front road surface, vehicle body motion state information, local state information of an active suspension and wheels, and driver operation information from a vehicle bus driver; generating fusion information according to the information, and predicting a vehicle state change trend and road excitation characteristics according to the fusion information; according to the vehicle state change trend, the road surface excitation characteristics and the driver operation signal, determining a weight coefficient in a next control period, and generating a target control force sequence; and controlling the active suspension actuator according to the target control force sequence. Therefore, the problems of delay, multi-target conflict and the like existing in an active suspension control method in the related technology are solved.

The invention discloses a ship active mooring control method and system based on variable stiffness gain scheduling, and belongs to the technical field of ocean engineering and ship localization, and the method comprises the steps: collecting and obtaining ship six-degree-of-freedom motion data and mooring rope tension data in real time, and obtaining a system state set; filtering high-frequency wave interference and extracting a low-frequency drift motion component by using a signal preprocessing algorithm, and generating a control deviation signal relative to a preset target position; according to a comparison result of the control deviation signal and a preset dead zone threshold value, generating a real-time rigidity gain parameter through a gain scheduling strategy; according to the real-time rigidity gain parameter and the control deviation signal, a total control force demand is calculated in combination with a virtual damping mechanism; and according to the total control force requirement and the spatial geometric distribution of each mooring rope, target tension increment of the mooring rope is generated to drive the mooring winch to adjust the original length of the mooring rope, and active mooring control is achieved. Fatigue damage of the mooring system is effectively reduced, and low-frequency resonance is restrained.

Force application apparatus 120 comprising means to receive position and force data, means to apply force to part of a human body, and means to control the application of force based on the received d

The application provides an electromagnetic damper type self-powered active / semi-active vibration reduction system and a method thereof, comprising a linear-rotation conversion device, an energy conversion device and a load, wherein the linear-rotation conversion device adopts a gear rack or a ball screw; the energy conversion device adopts a permanent magnet synchronous motor; the load is configured as a three-phase voltage type PWM rectifier and a battery; when the system works in an active mode, motor control force is solved by rolling optimization in a model predictive controller according to detected structural displacement, speed, voltage and current feedback information; when the system works in a semi-active mode, analog inertia force, stiffness force and damping force are calculated according to feedback signals, each bridge arm of the three-phase voltage type PWM rectifier is turned on and turned off to realize control of the motor, vibration is suppressed, and the energy of the vibration is recovered to the battery.

The invention provides a vehicle control method and a vehicle, and is applied to the technical field of vehicle control. The method comprises the following steps: acquiring a vertical motion parameter, a roll motion parameter and a pitching motion parameter of a vehicle under the condition that the vehicle is in a target unstable working condition; calculating a vertical control force according to the vertical motion parameter, calculating a roll control moment according to the roll motion parameter, and calculating a pitch control moment according to the pitch motion parameter; and calculating a target control force according to the vertical control force, the roll control moment and the pitch control moment, and controlling the vehicle based on the target control force. According to the method, through multi-dimensional cooperative control over the vertical direction, the side inclination and the pitching of the vehicle body, the phenomena of up-down bumping, turning side inclination, sudden acceleration, deceleration, pitching and the like of the vehicle body are effectively controlled, the driving smoothness and the handling stability of the vehicle are remarkably improved, and the driving comfort and the driving safety are both considered.

The invention discloses an unmanned vehicle path tracking control method and system, and relates to the field of unmanned vehicle control. According to the method, disturbance caused by ocean currents, sea waves and other external environments can be captured through the extended state observer, disturbance caused by model uncertainty can also be captured, feedforward compensation is carried out on the longitudinal control force basic value and the heading angle control torque basic value according to the disturbance, the influence of the factors on track tracking control is reduced, and the track tracking control accuracy is improved. The unmanned vehicle is driven to sail along the preset path, the stability of the control system is ensured, the track tracking error is reduced, and the accuracy of track speed tracking and heading angle tracking of the unmanned vehicle is improved.