29 results about "Stabilizing controller" patented technology

The invention discloses an aircraft self-learning optimization control method based on adaptive dynamic programming, and the method comprises the steps: firstly, designing a basic stability controller under a backstepping method frame in order to guarantee the stable control of an aircraft under the uncertain and unknown external disturbance of a model; then, considering that the tracking performance of the system has a weakening trend along with disturbance change, introducing an ADP-based parameter self-adjustment intelligent optimization algorithm to design an auxiliary controller, and obtaining a compensation control signal by executing and evaluating updating adjustment of a network weight along with tracking deviation so as to further reduce the tracking error; and the self-learning and self-adaptive capabilities of the controller to environmental changes are realized. And finally, integrating the basic controller and the auxiliary optimization controller to finally obtain a complete robust self-learning optimization controller. The method is high in universality and high in calculation precision, and has important engineering practice reference significance.

The invention discloses a stable control method for a deformation process of an unmanned aerial vehicle in a complex environment, and relates to the technical field of unmanned aerial vehicle flight control. The state-space equation is used for describing flight dynamics parameters in the deformation process of the unmanned aerial vehicle in the complex environment; constructing a disturbance observer based on the state-space equation; the disturbance observer is used for observing a lumped disturbance quantity caused by a complex environment and wing tip folding deformation; constructing a sliding mode surface equation according to the attitude angle tracking error; constructing an attitude stability controller according to the disturbance observer and the sliding mode surface equation; and based on the Lyapunov stability theory, performing stable control on the unmanned aerial vehicle by using an attitude stability controller. The lumped disturbance quantity caused by the complex environment and wing tip folding deformation is considered during flight control of the unmanned aerial vehicle, and the control precision of the deformable unmanned aerial vehicle under the complex environment condition can be improved.

The invention provides a lifting movement stability control system and method for a large vertical tool of a long-distance production line. The system comprises a production line, a tool and a lifting movement stability control system. The lifting movement stability control system comprises 2n wheel sets distributed in a two-row n-column matrix mode, 2n displacement sensors distributed in a two-row n-column matrix mode, (n-1) attitude sensors distributed in a one-row (n-1)-column matrix mode and a lifting movement stability controller. The tool lifting movement stability control method comprises a high-low position solving method of the initial state of the wheel set, a compensation method of synchronous lifting of the wheel set and a synchronous lifting movement coordination control method.

The invention discloses a spacecraft attitude stability control method based on a liquid neural network. The method comprises the following steps: S1, establishing a spacecraft attitude dynamic model under multi-source complex disturbance; s2, designing a conventional spacecraft attitude stability controller, and constructing a data set in combination with the attitude dynamics model; s3, training and obtaining a liquid neural network interference observer by adopting a liquid time constant network and a neural circuit strategy architecture; s4, designing a spacecraft attitude stability controller based on a liquid neural network disturbance observer, and proving the stability of a closed-loop system by using a Lyapunov function; s5, verifying the estimation effect of the liquid neural network interference observer and the stability effect of the spacecraft attitude control system through simulation; results show that the method has the advantages that interference of the spacecraft attitude can be effectively estimated, stable control over the spacecraft attitude can be achieved, high real-time performance, low power consumption and high robustness are achieved, and the spacecraft attitude stable control efficiency can be improved.

This invention discloses a zero-sum game optimal tracking control method for UAV swarms under denial-of-service attacks. It establishes a communication topology model of the UAV swarm and constructs a nonlinear dynamics model of the UAVs containing unknown external interference. Based on a zero-sum game strategy, it constructs a stabilizing controller and a cooperative tracking controller. It identifies whether a follower UAV is under a denial-of-service attack based on the communication topology state, switches to stabilizing control for attacked UAVs to suppress state divergence, and reconstructs the communication topology for unattacked UAVs while maintaining cooperative tracking control. Simultaneously, a memory self-triggering mechanism is introduced to determine the communication update time based on the combined effect of historical and current information. This invention can improve the anti-attack capability, topology recovery capability, and cooperative flight stability of UAV swarms under conditions of communication link obstruction and external disturbances, while reducing continuous detection and communication resource consumption.

The application discloses a multi-unmanned aerial vehicle control method based on reinforcement learning and belongs to the technical field of unmanned aerial vehicle cooperative control. The application designs a control algorithm for a double-time-scale multi-agent, effectively avoids possible calculation errors in the process of solving a matrix equation by introducing a novel coefficient matrix, and proposes a hybrid iteration algorithm which is independent of initial stable controller gain and has a fast convergence speed and is used for solving an approximate solution of a game algebraic Riccati equation based on a communication topology between agents. The application can realize consistency between unmanned aerial vehicles, enables the unmanned aerial vehicles to work cooperatively, and completes a task.

The application provides an aero-engine integral type constant differential pressure control system and method. In the system, a controlled object control system controls the controlled object based on an instruction oil pressure increment of a disturbance change, an incremental flow area of a metering valve, and a current incremental flow area of an actuating valve output by a stabilizing controller, and outputs an incremental pressure of the metering valve; the stabilizing controller obtains a new incremental flow area of the actuating valve based on a first adjustment difference between the incremental pressure of the metering valve output and an incremental spring cavity pressure of the actuating valve output by an integral controller; and the integral controller processes a second adjustment difference between the incremental pressure of the metering valve output by the controlled object control system and an instruction oil pressure increment input into the system by using a preset integral control algorithm, and obtains the incremental spring cavity pressure of the actuating valve. The system realizes robust servo tracking without static error.

The present invention is applicable to the field of electric vehicle control technology, and provides a learning predictive control method for electric vehicle yaw stability considering external disturbances, which includes the following steps: building a data-mechanism hybrid model, aiming at the problem of large error in the traditional two-degree-of-freedom vehicle model, using a machine learning model to compensate for the error of the mechanism model to obtain a high-precision data-mechanism hybrid model; designing a yaw stability controller according to the control objectives of the vehicle yaw stability controller, considering the constraints of the vehicle actuators and the safety constraints of vehicle driving; aiming at the uncertainty in the environment, considering the propagation problem within the uncertainty prediction horizon, using the method of converting probabilistic constraints into deterministic constraints to transform the original stochastic optimization problem into a deterministic nonlinear programming problem. The yaw stability controller design is completed by solving the nonlinear programming problem. The present invention ensures the safety of electric vehicles during driving and further improves the performance of the electronic body stability system.

The embodiment of the invention provides a target image tracking control method and system for a photoelectric turret, computer equipment and a medium, and belongs to the technical field of automatic control, and the method comprises the steps: S1, obtaining the altitude of a task area; s2, calculating an angular velocity compensation value; s3, selecting an interest target; s4, calculating an azimuth tracking miss distance and a pitching tracking miss distance; s5, calculating an initial angular velocity; s6, calculating the compensated angular velocity; and S7, the angular velocity difference value is input into a stable controller, the stable controller outputs a motor driving amount after receiving the angular velocity difference value, and a stable platform torque motor is driven to rotate. According to the method, the relative movement of the aircraft and the target is overcome, the visual axis translation caused by the linear displacement angular velocity in the flight state is counteracted, the target capturing and tracking difficulty in the flight is greatly reduced, the tracking performance of the target in the dynamic state is ensured, and the target capturing efficiency is improved. The method is small in calculation amount, low in cost, high in portability and suitable for being used on the photoelectric turret.

The invention discloses an intelligent pressure reducing valve and a remote dynamic control system, and relates to the field of pressure reducing valve control, and the intelligent pressure reducing valve comprises a main valve body arranged on a water pipeline, a digital pressure stabilizing controller used for locally operating and setting a target pressure value, and a sensor module used for collecting pressure and flow real-time data, the electric pilot valve is used for receiving a control signal of the digital voltage stabilization controller and adjusting the opening degree of the main valve body; closed-loop control logic is adopted in the whole working process, accurate and stable control over the outlet pressure is achieved through the cooperative working mode of real-time collection of the sensor module, analysis and judgment of the digital pressure stabilization controller and dynamic adjustment of the electric pilot valve, and the core technical effects of working condition self-adaption, accurate and stable pressure and intelligent linkage operation and maintenance are achieved. Data are collected in real time through the front-end pressure / flow sensor and the rear-end pressure / flow sensor, when the rear-end pressure deviates from the target pressure value, the opening degree of the electric pilot valve is dynamically adjusted, pressure drift and oscillation of a traditional valve are avoided, and the pressure stability of a water supply pipe network is remarkably improved.

The present invention belongs to the field of robot motion control, and discloses a trajectory tracking control method, device and medium for a mobile robot based on the high-order fully actuated theory, including: constructing a two-wheeled bicycle model of the mobile robot and converting the state space model into a fully actuated model; designing a fully actuated stabilizing controller for the trajectory tracking control problem of the two-wheeled bicycle model of the mobile robot to obtain a trajectory tracking control system error model of the mobile robot; according to the trajectory tracking control system error model of the mobile robot, introducing a system increment equation to simplify the mathematical model; designing an MPC controller to solve the optimal control sequence, and obtaining the final input of the motion control system of the mobile robot through inverse transformation. The present invention uses the high-order fully actuated theory to convert a complex state space model into a more concise second-order fully actuated model, which not only retains the physical meaning of the system, but also avoids the ill-conditioned matrix problem that may be encountered in traditional methods, greatly simplifying the controller design process.

The present application relates to a control method, device, teleoperation system, storage medium and computer program product for an instrument. The method comprises: obtaining a command signal sent by an operating workbench, wherein the command signal carries the initial angles of each joint of the target instrument. Determine the command torque for the current time according to the initial angles of each joint of the target instrument, and control the movement of the target instrument according to the command torque. The contact force generated after the movement of the target instrument is theoretically calculated and actually detected to obtain the theoretical contact force and the actual contact force. If the difference between the theoretical contact force and the actual contact force is not within a threshold range, a new command torque is determined based on the initial angle and the difference. The new command torque is used as the next command torque, and the target instrument movement is controlled according to the command torque until the difference is within the threshold range and stops, so as to realize the dynamic balance control of the target instrument, so that the instrument reaches the most uniform control state, and ensures that the instrument can be stably controlled.

The present application relates to the technical field of power system stability control, and particularly relates to a power system stability control method based on input and output data optimization control parameters. The method first collects power system input data, output data and oscillation response data; then constructs a parameterized stability controller with several undetermined coefficients, and sets the initial value of the to-be-optimized coefficients; then superimposes the controller output to the additional control channel of the controlled object, constructs an optimization function with the minimum system oscillation as the target, and sets the constraint condition that the average value of the parameters is 0, so as to ensure that the controller output is 0 after the oscillation disappears; then iteratively updates the to-be-optimized coefficients under the constraint condition by using an optimization algorithm, and obtains the target control coefficients; finally, writes the optimized target control coefficients into the parameterized controller, and forms the final stability controller. The simulation results show that the present application can effectively suppress the subsynchronous oscillation and low-frequency oscillation of the synchronous generator, reduce the oscillation amplitude, and speed up the oscillation decay process.

The invention discloses a control method for a direct-drive type grid-forming wind generating set with an inertia self-sustaining function, and relates to the technical field of wind generating set control. The method comprises the steps that a grid side or machine side power reference value is adjusted during a fault period; acquiring a control output angular frequency of a grid-side converter, obtaining a virtual power or voltage compensation value for inertia self-sustaining control after the control output angular frequency passes through a first transient stability controller and an amplitude limiter, and feeding back the virtual power or voltage compensation value to a grid-side reference adjustment module; the grid-side virtual compensation value is fed back to a machine-side converter, and a machine-side compensation value for inertia self-sustaining control is obtained after the grid-side virtual compensation value passes through a second transient stability controller and an amplitude limiter; and the angular frequency error output by the grid side synchronization link is converted into a torque compensation current through a third transient stability controller and a torque-current mapping coefficient, and the torque compensation current is fed forward to a machine side reactive current reference. The system inertia can be effectively maintained, the power imbalance of the machine side and the network side is adjusted, and the transient stability and the fault ride-through capability of the system are improved.

The embodiment of the invention provides a tower crane control method, an unmanned tower crane control method and a stable hook controller. The tower crane control method comprises the following steps: acquiring a first control signal for indicating the tower crane to move; detecting a motion trend of the tower crane in response to the first control signal; under the condition that the motion trend is determined to be the variable-speed motion trend, superposing a pulse signal on the first control signal so as to convert the linear first control signal into a nonlinear second control signal; and inputting the second control signal into a tower crane controller, so that the tower crane controller controls the tower crane to move based on the second control signal. According to the technical scheme provided by the embodiment of the invention, automatic hook stabilizing control on the tower crane is realized.

An economical optimization strategy construction method and system for lateral stability control of an electric vehicle, and a computer-readable storage medium are provided. The method includes: firstly constructing a lateral stability control system model with active allocation optimization to achieve the coordinational allocation between effective working areas of each part by means of a coordinational variable L; secondly, on the basis of the system model with active allocation optimization, designing a lateral stability controller under a model predictive control framework, where the designed objective function J makes the motion state of the vehicle track the expected value at steady state; and finally, considering the mapping relationship between the additional yaw moment and the coordinational variable L, constructing a three-dimensional surface, and analyzing the influence of the coordinational variable L, and determining the workflow of a regulator of the coordinational variable L.

The invention provides an oscillation suppression method and device of a wind storage combined grid-connected system and control equipment, and relates to the technical field of power system control. According to the specific technical scheme, two stable controllers are additionally arranged in the wind storage combined grid-connected system and act on a wind turbine generator system and an energy storage system respectively, and the total equivalent damping of the wind storage combined grid-connected system is controlled to be larger than a preset value by setting stable control gains of the wind turbine generator system and the energy storage system. Therefore, the wind storage combined grid-connected system is prevented from oscillation and instability; by adopting the scheme, when the wind-storage combined grid-connected system has an oscillation trend, unbalanced torque in the system can be counteracted through the stability controller, low-frequency oscillation is effectively suppressed, the operation stability and reliability of the wind-storage combined grid-connected system in a complex power grid environment are improved, and the risk of off-network is reduced.

The invention discloses an online intelligent self-adaptive parameter adjustment method for a missile stability controller. The method comprises the following steps: S1, constructing a numerical simulation model of a missile nonlinear dynamic model; the parameters influencing the stability of the system are subjected to pulling deviation of different combinations, and state output of the missile dynamics system under different pulling deviation combinations is obtained; s2, constructing a training data label by quantifying the stable state of the missile stability control system; s3, performing frequency domain feature extraction on the training input data; s4, performing correlation mapping on the stability of the missile system and a rudder instruction time sequence signal, and constructing a neural network for steady-state sensing; and S5, designing a controller parameter adaptive adjustment strategy. Real-time stability perception of missile flight is realized, and the defect that stability judgment cannot be performed on a missile when a traditional missile flies is overcome; under the condition that the stability margin of the preset controller is insufficient, the margin boundary of the system is expanded, and the robustness of the controller is improved.

The invention discloses a spacecraft attitude preset time control method based on adjustable preset performance, and the method comprises the steps: building a kinematics and dynamics model for spacecraft attitude control, and obtaining a spacecraft attitude control system model; thirdly, designing an adjustable preset performance function and a new error conversion function, and converting the original system into a new second-order system; and finally, designing a non-singular sliding mode controller for the converted second-order system to realize accurate control of system errors, so that the system still has the capabilities of keeping the system stable and enabling the controller to take effect under the condition that the system is subjected to certain sudden change disturbance. According to the method, by introducing an adjustable preset performance boundary, designing a new error conversion function and combining nonsingular sliding mode control, accurate control over system errors is achieved, meanwhile, the robustness and the disturbance rejection capacity of the system are improved, and solid technical support is provided for spacecraft attitude control.

The application discloses a spacecraft attitude preset time control method based on adjustable preset performance, first, a kinematics and dynamics model is established for spacecraft attitude control to obtain a spacecraft attitude control system model. Then, an adjustable preset performance function and a new error conversion function are designed to convert the original system into a new second-order system. Finally, a non-singular sliding mode controller is designed for the converted second-order system to realize accurate control of system error, so that the system still has the ability to keep stable and the controller can take effect under certain sudden disturbance. The method introduces an adjustable preset performance boundary, designs a new error conversion function, and combines the non-singular sliding mode control to realize accurate control of system error, improve the robustness and anti-disturbance ability of the system, and provide solid technical support for spacecraft attitude control.

The application discloses an event-triggered air-ground dual-purpose unmanned aerial vehicle semi-Markov smooth model prediction control method and system, belongs to the technical field of control theory and control engineering, and solves the problems of input jitter and high calculation amount of the air-ground dual-purpose unmanned aerial vehicle control.The method comprises the following steps: a dynamic event-triggered model prediction smooth control framework is proposed; a transition-dependent smooth controller and a mode-dependent stabilizing controller are designed; and the optimal control of the system in a disturbed environment is realized.Through state observation and a dynamic triggering mechanism, a model prediction control law is directly designed, complex online optimization calculation is not needed at each time, and efficient control of the random switching system can be realized.The application is suitable for the control of complex engineering systems such as unmanned aerial vehicles and power systems.

The present invention discloses a high-precision turning machining method, device and medium for non-circular contour parts, including: for the rotation system, a motor voltage excitation signal is given, the tool position output signal is collected, and the second-order state space model of the rotation system in the time domain is obtained by using the system identification toolbox; aiming at the problem that the change of the time period will significantly reduce the control performance of the rotation system, the definition of the position domain is given, and the time domain model of the rotation system is converted into the position domain model; according to the obtained position domain model, the position domain model is discretized and converted into a high-order full-drive model, and then the corresponding full-drive stabilizing controller is designed to stabilize the rotation system; the model predictive controller is designed to solve the optimal control law and inversely transform it into the full-drive control law through the corresponding relational expression. The present invention uses the full-drive system model to represent the rotation system in the position domain, which can not only maintain the physical interpretability of the model, but also avoid the bad matrix problems that often occur in traditional methods.

The invention provides a rapid frequency support controller of a droop control type network construction converter, which comprises a differential link, a stability controller and a network construction converter system, and is characterized in that the network construction converter system comprises a network construction converter and a power-voltage formula; in the differential link, the differential component of the voltage deviation is obtained by making a difference between the reference direct-current voltage and the actual output direct-current voltage, the stability controller receives the differential component of the voltage deviation and regulates and controls the differential component in a stability domain, a control signal is output through the stability controller, and the control signal is imported into the grid-forming converter system to control the grid-forming converter system. Combining the active power output of the source side and the demand of the load side to quantify the output of the network construction converter, and iteratively outputting voltage by means of a power-voltage relation formula; according to the method, the nonlinearity of the internal state variable of the network construction converter and the deviation of the frequency deviation value are corrected, and the droop control type network construction converter is allowed to keep the maximum support for the system frequency under the condition of avoiding the direct current link voltage collapse.

The invention discloses a multi-person cooperation teleoperation method based on self-adaptive permission and a robot. The method comprises the steps that according to a first motion state corresponding to a local end actuator and a second motion state corresponding to a remote end robot, an interval type-2 polynomial fuzzy model is constructed based on a multi-person cooperation kinetic equation; based on the evaluator neural network and the interval type-2 polynomial fuzzy model, dynamically allocating execution instruction weights to the external input of the local end executor; performing deviation state conversion processing on the first motion state and the second motion state to obtain a converted first motion state and a converted second motion state; according to the transformed first motion state and the transformed second motion state, a controller is constructed through an interval type-2 fuzzy logic system and a multi-person cooperation kinetic equation; and performing reverse adjustment on the controller parameters based on the feedback state of the controller to generate a stable controller. Therefore, a solution with robustness and safety can be provided for multi-person cooperative teleoperation.

This invention relates to a vehicle lateral stability control method based on online state compensation (MPC), belonging to the field of vehicle safety control technology. The objective of this invention is to design an online adaptive neural network based on a linear two-degree-of-freedom vehicle dynamics model to compensate for uncertainties caused by low-adhesion road conditions, thus establishing a vehicle lateral stability control method based on online state compensation (MPC). This invention designs an online adaptive neural network based on a linear two-degree-of-freedom vehicle dynamics model to compensate for uncertainties caused by low-adhesion road conditions. Based on this compensation model, a vehicle lateral stability controller is designed within a model predictive control framework. The control objectives include tracking yaw rate and suppressing lateral velocity, and constraints are imposed on lateral velocity and control variables. This invention can effectively compensate for the system's state variables without extensive offline training, exhibiting better versatility.

The application provides a Gaussian process regression method and device for system identification, and belongs to the technical field of automatic control. The method comprises the following steps: obtaining an original data set of a model to be identified in an automatic control system; assigning an importance factor to each group of original data; updating the importance factor based on the Euclidean distance between input data; removing original data with low importance in the original data set to obtain a uniformly distributed data subset; grouping input data of the data subset into a training input matrix and grouping output data into a training output matrix; performing Gaussian process regression training on the data subset to obtain a covariance matrix considering data noise; and calculating an output prediction result corresponding to the input data to be predicted, so as to realize system identification. The application can extract a small sample set with uniform distribution, solve the problem of large calculation amount of Gaussian process regression when applied to system identification, and promote the realization of a high-precision stable controller.

The invention provides a multi-unmanned aerial vehicle cooperative formation network stability controller design method, system and device and a medium, and belongs to the field of new energy inspection, and the method specifically comprises the steps: building a mathematical model of a multi-unmanned aerial vehicle cooperative formation network; derivation and expectation operation are carried out on the generic function, and an expectation expression is obtained; extracting an intermediate judgment condition of system synchronization stability; defining parameters related to upper and lower limits of time delay and state variables, constructing a diagonal matrix, carrying out pre-multiplication and post-multiplication on an intermediate judgment condition, and adjusting an expression form; and equivalently converting the inequality group which still comprises the nonlinear term after adjustment into a linear matrix inequality form, and completing the design of the stability controller of the multi-unmanned aerial vehicle autonomous cooperative formation network. Through accurately depicting complex environmental factors, optimizing a mathematical form of a stability determination condition and solving by means of a numerical tool, stable cooperation of the multi-unmanned aerial vehicle formation under complex interference is finally realized, and the reliability and adaptability of the formation in an inspection task are improved.

The utility model relates to the field of alternating-current power controllers, and discloses an alternating-current voltage-stabilizing current-stabilizing power-stabilizing controller, which comprises a sampling unit used for sampling output voltage, current and power and generating sampling signals; the control unit is connected with the sampling unit, and the control unit is used for analyzing and comparing the sampling signals; and the driving unit is connected with the control unit. According to the utility model, the load state can be monitored in real time through the load open circuit detection unit, output voltage or current is prevented from abnormally rising, a controller and a subsequent circuit are effectively protected from being damaged, and the AC-input-free detection unit can monitor whether input AC voltage exists or not and the amplitude of the input AC voltage in real time, so that a user can know the state of equipment in time; through the current isolation detection unit, the current state can be monitored under the condition of electrical isolation, the current isolation state is effectively detected, and the electromagnetic compatibility of the system is improved.