68 results about "Backstepping controller" patented technology

The invention relates to a prediction backstepping control method for a hypersonic flight vehicle. The method comprises the following steps: S1, constructing a mathematical model of the hypersonic flight vehicle; s2, decoupling the mathematical model of the hypersonic aircraft into a speed subsystem and a height subsystem; s3, taking the weighted sum of the speed tracking error and the accelerator opening degree as an optimization performance index, and solving through the speed subsystem to obtain the accelerator opening degree of the hypersonic aircraft; s4, designing a prediction backstepping controller for the height subsystem, and solving the rudder deflection angle of the hypersonic aircraft through the prediction backstepping controller by taking the weighted sum of the height tracking error and the trajectory inclination angle as an optimization performance index; and S5, the hypersonic aircraft is controlled according to the rudder deflection angle and the accelerator opening degree.

The invention discloses a multivariable high-coupling hydrogen-oxygen double-loop coordination control method for a hydrogen fuel cell, and the method comprises the following steps: S1, building a flow and pressure coupled nonlinear high-order system model according to cathode and anode gas kinetic equations of a fuel cell system; s2, designing a multivariable coordinated backstepping controller by taking the peroxide ratio, the hydrogen ratio and the positive and negative pressure difference as control targets; s3, considering the influence of load change and model uncertainty on the hydrogen-oxygen double loop, introducing a self-adaptive control method, and designing control instructions of an air compressor, a circulating pump and an electromagnetic valve; s4, selecting a proper Lyapunov function to prove the stability of the system, and completing the self-adaptive backstepping control of the pressure difference between the anode and the cathode and the gas flow; according to the method, the fuel cell hydrogen-oxygen double-loop multivariable high-coupling coordination control problem is solved, the transient and steady-state performance of the system can be kept under the condition of sudden load change, and the safety and reliability of system operation are improved.

The invention discloses an interference compensation control method for a heave compensation winch. The method comprises the following steps: based on a state space model of a heave compensation winch system comprising a heave compensation winch and a pump station, establishing a backstepping controller through a backstepping control method; establishing a parameter online estimation module of the hydraulic motor of the heave compensation winch, and inputting and processing the initial identification parameters of the hydraulic motor and the dynamic regression vector of the hydraulic cylinder to obtain the identification parameters of the hydraulic motor updated in real time; and inputting the identification parameters and the operation parameters of the heave compensation winch into a backstepping controller, outputting a valve core displacement control instruction of a proportional servo valve of a pump station after processing, and further realizing interference compensation control of the heave compensation winch by controlling the pump station. By means of the method, high-precision tracking and robust stability control over the heave compensation winch system can be still kept under the conditions that the model is uncertain and disturbance is strong, and support is provided for the underwater high-precision operation requirement.

The invention provides an orchard tracked vehicle pure tracking control method and system, and the method comprises the steps: constructing a geometry-vector dynamic coupling model based on path geometric features and vector field analysis, quantifying local steering characteristics through a direction change sensing mechanism, introducing a curvature factor to represent path accumulated deformation, inhibiting noise interference in combination with a direction consistency correction term, improving curvature estimation of a three-point arc method, and generating multi-scale curvature characteristic data; based on a tracked vehicle kinematics model, analyzing a coupling relationship between a transverse tracking error and a course tracking error, and generating coupling error state data; and inputting the multi-scale curvature characteristic data and the coupling error state data into a backstepping controller, analyzing and exporting a dynamic look-ahead distance explicit expression through a simultaneous kinematics equation and a backstepping control equation, generating a self-adaptive angular velocity control instruction, and outputting the self-adaptive angular velocity control instruction to an execution mechanism to drive a vehicle to steer. And adaptive optimization of the look-ahead distance along with the path curvature, the vehicle state and the tracking error is realized.

The application relates to a kind of linear vernier permanent magnet motor deadbeat control methods, on the basis of traditional DTC control, by introducing speed backstepping controller to replace traditional speed loop PI regulator, using deadbeat controller to replace current loop PI regulator, using improved double-sampling double-update SVPWM to replace traditional SVPWM modulation module.According to multiple simulation, the application can effectively reduce the overshoot of the system and effectively reduce the thrust ripple of the system on the basis of maintaining traditional DTC control, and improve the dynamic performance of the system.

The invention discloses a seabed crawler trajectory tracking control method based on adaptive backstepping control. The method comprises the following steps: firstly, according to the Newton-Euler principle, establishing a mathematical model of the seabed crawler under external interference; then, according to the mathematical model and the expected value of the seabed crawler system under the external interference, a tracking error function of the seabed crawler is obtained; then, a backstepping control method is used, and the tracking error function is combined to design a self-adaptive backstepping controller of the seabed crawler; and finally, estimating external interference by using the radial basis function neural network, replacing an external interference item in the self-adaptive backstepping controller with an output value of the radial basis function neural network, obtaining the self-adaptive anti-interference controller and an actual control quantity of self-adaptive backstepping, and realizing tracking control of the seabed crawler.

The application discloses a construction method of a flat wire permanent magnet wheel hub motor backstepping model predictive controller, first derives an angular velocity deviation, constructs a corresponding Lyapunov function one, calculates q-axis current, obtains a q-axis current estimation value according to a calculation formula of the q-axis current and a torque estimation error, and constructs a backstepping controller with the angular velocity deviation input and the q-axis current estimation value output; then constructs a corresponding Lyapunov function two according to the angular velocity deviation and a current tracking error, solves acceleration, constructs an acceleration control module with the angular velocity deviation and the current tracking error input and the acceleration output, and finally evaluates each d-axis current prediction value, q-axis current prediction value and acceleration prediction value through a value function formula, selects a voltage vector corresponding to a prediction value that makes the value function minimum; the method realizes rapid and accurate acquisition of motor given current under complex working conditions, improves the response capability of a hub driving system, and is favorable to improvement of collaborative control performance of a distributed driving system.

The invention discloses a permanent magnet synchronous motor sliding mode integrated control method based on a disturbance observer, and the method comprises the steps: constructing a hyperlocal mathematical model of a speed ring of a permanent magnet synchronous motor, combining a state equation with a first-order nonlinear model, and reducing the dependence on a precise system model; designing a model-free adaptive fast integration terminal sliding mode controller; a current loop backstepping controller is designed, electromagnetic parameter disturbance is considered, d-axis and q-axis current control laws are recursively constructed, and the anti-interference capability of a current loop is enhanced; and designing an extended super-spiral disturbance observer, estimating total unknown disturbance of the system and performing feed-forward compensation, thereby improving the control precision. The speed tracking precision of the system is improved, and the steady-state error is reduced.

This invention discloses a multivariable, highly coupled hydrogen-oxygen dual-loop coordinated control method for hydrogen fuel cells, comprising the following steps: S1, establishing a nonlinear high-order system model with flow rate and pressure coupling based on the cathode and anode gas dynamic equations of the fuel cell system; S2, designing a multivariable coordinated backstepping controller with the excess oxygen ratio, excess hydrogen ratio, and anode-cathode pressure difference as control objectives; S3, considering the influence of load changes and model uncertainties on the hydrogen-oxygen dual loop, introducing an adaptive control method and designing control commands for the air compressor, circulating pump, and solenoid valve; S4, selecting a suitable Lyapunov function to prove the stability of the system and completing the adaptive backstepping control of the anode-cathode pressure difference and gas flow rate; This invention solves the problem of multivariable, highly coupled coordinated control of the hydrogen-oxygen dual loop in fuel cells, and can maintain the transient and steady-state performance of the system under sudden load changes, improving the safety and reliability of system operation.

The invention discloses an intelligent automobile transverse control method based on a radial basis function neural network. The method comprises the following steps that a vehicle-road coupling dynamics tracking model considering steering characteristics is established; constructing a fixed time preset performance function to express the tracking error; establishing a self-adaptive backstepping controller model based on a radial basis function neural network; determining steering wheel output torque; and determining a final front wheel steering angle control instruction. According to the method, the radial basis function neural network and the adaptive disturbance estimation strategy are combined, the system uncertainty disturbance existing in the intelligent vehicle path tracking process is considered, the path tracking precision can be remarkably improved, the robustness of intelligent vehicle control is ensured in the complex road environment, and especially under the conditions of different vehicle speeds and roads, the intelligent vehicle path tracking precision is improved. The path tracking error of the vehicle can be always kept within the preset precision range. According to the invention, rapid convergence of the path tracking error in finite time can be ensured, so that the stability and reliability of the system are remarkably improved.

The present invention discloses a decentralized event-triggered adaptive backstepping control method, which defines a nonlinear system and sets three conditions that the system must satisfy; secondly, coordinate transformation is carried out to design a decentralized adaptive backstepping controller under continuous state feedback, and finally, a decentralized adaptive backstepping controller under event-triggered conditions is constructed on the basis of the decentralized adaptive backstepping controller under continuous state feedback. By combining the principle of the modified aggregation method based on variables with the special treatment of non-triangular structure uncertainties, this method avoids the derivation of time-varying parameters, eliminates the limitation of the triangular structure condition, thereby broadening the application scope; directly replaces the continuous state with the triggered state, completely eliminating the non-differentiability problem of the virtual controller caused by intermittent state feedback, enabling each subsystem of the interconnected system to exchange information only with adjacent subsystems and only using intermittent states and inputs, ensuring the balance between communication resource usage and control performance.

The invention belongs to the related technical field of hydraulic servo control systems, and discloses a humanoid robot hydraulic driver position servo self-adaptive control method and system oriented to variable load working conditions. The method comprises the following steps: establishing a mathematical model of a to-be-controlled object hydraulic driver and converting the mathematical model into a state-space equation; according to the state-space equation of the to-be-controlled object, establishing an extended state observer to observe the state and total disturbance of the to-be-controlled object in real time; according to the state and the total disturbance observed by the expansion state observer in real time, a backstepping controller is designed to control the to-be-controlled object, so that the actual displacement of the to-be-controlled object is close to the expected displacement, when the backstepping controller is designed, an adaptive law is established for the load quality in the backstepping controller, and the actual displacement of the to-be-controlled object is controlled. Therefore, the dynamic change of the load in practical application is met. According to the invention, the problems of low tracking precision and weak robustness of hydraulic driver position control under a variable load working condition are solved.

The present invention discloses an adaptive event-triggered control method for the flight attitude of a quadrotor unmanned aerial vehicle, which relates to the technical field of unmanned aerial vehicle tracking control. The method includes constructing a dynamic model of the flight attitude system of the unmanned aerial vehicle and dividing the unmanned aerial vehicle system into a position system and an attitude system. On the basis of a classical backstepping controller, combined with the dynamic surface control method, the approximation characteristics of the RBF neural network are used to reduce the influence of external disturbances on the system. An event-triggered mechanism is introduced. On the basis of adopting a fixed threshold strategy to trigger control, a switching threshold event-triggered strategy including a fixed threshold strategy and a relative threshold strategy is proposed to achieve effective control of the flight attitude system of the quadrotor unmanned aerial vehicle and avoid the Zeno phenomenon. By designing an adaptive event-triggered control method for the flight attitude of the quadrotor unmanned aerial vehicle, the tracking trajectory error of the flight attitude of the unmanned aerial vehicle is greatly reduced, and effective tracking control of the attitude of the quadrotor unmanned aerial vehicle is achieved.

This invention discloses a multi-strategy traffic following control system based on an onboard edge-cloud collaborative architecture, comprising: an onboard control unit, a roadside communication identification unit, and a cloud-based control management platform. The roadside communication identification unit is deployed at key road nodes to identify road types and communication status in real time, and issues control strategy switching commands based on these real-time identifications. The onboard control unit integrates an adaptive sliding mode controller and a nonlinear self-stabilizing backstepping controller, used for following control on straight and curved road sections, respectively. The cloud-based control management platform periodically updates controller parameters using historical data and pushes this information to the onboard control unit and the roadside communication identification unit. This invention achieves automatic switching of control strategies based on the road structure type where the vehicle is located, employing a hybrid weighted fusion control structure to ensure a smooth transition between different controller signals during switching, thereby improving the control accuracy and stability of mixed vehicle fleets in complex traffic environments.

A finite-time anti-interference control method for a universal pneumatic flexible manipulator includes the following steps: The universal pneumatic flexible manipulator is simplified to a link model using the D-H method, and kinematic analysis is performed to obtain the kinematic analysis model and the position of the end point of the universal pneumatic flexible manipulator; based on the kinematic analysis model of the universal pneumatic flexible manipulator, a dynamic model of the universal pneumatic flexible manipulator is established using the Euler-Lagrange method; the uncertainty of the universal pneumatic flexible manipulator is considered as a disturbance, and a second-order mathematical model of the pneumatic flexible manipulator is established; a finite-time extended state observer is designed to estimate the disturbance, and based on the disturbance estimate, a finite-time backstepping controller is designed to compensate for the influence of the disturbance on the system; to address the model uncertainty, a finite-time control method is designed to compensate for the influence on the position control accuracy of the pneumatic flexible manipulator, exhibiting strong robustness and high control accuracy.

The invention relates to an electric steering engine fine anti-interference control method under the constraint of a rudder deflection angle, and belongs to the technical field of electric steering engine control. Firstly, an electric steering engine mathematical model considering unknown frequency multi-source interference is established; secondly, aiming at multi-source interference generated by cogging torque, magnetic flux harmonic waves, PWM dead zone torque and the like, constructing an unknown frequency interference observer to estimate interference; and finally, designing a backstepping controller based on a obstacle Lyapunov function, and completing the design of the fine anti-interference control method of the electric steering engine under the constraint of the rudder deflection angle by combining the interference and the estimated value of the first derivative of the interference. The method realizes fine anti-interference control of the electric steering engine, has the characteristics of high control precision and good reliability, and is suitable for the control problem of the electric steering engine under multi-source interference and constraint.

The invention discloses a DNN-based adaptive optimization control method for a fractional order single-machine infinite bus system, and relates to the field of single-machine infinite bus system control. The method comprises the following steps: establishing a dynamical model of a single-machine infinite bus system, and converting the dynamical model of the system into a state model; designing a function approximate DNN architecture, approaching an unknown function in the system, and designing a weight updating law based on a first-order Taylor series to reduce the mathematical difficulty; in the backstepping process, a virtual controller and an actual controller are constructed by utilizing an optimization backstepping technology, and the overall control optimization of the system is realized; an event trigger function is designed, and consumption of system communication resources is reduced; and carrying out Lyapunov analysis to ensure that each signal of the system is bounded. The adaptive optimization backstepping controller based on the DNN architecture is designed for a fractional order single-machine infinite bus system, unknown nonlinear terms in the system can be compensated, and a single-machine power angle tracks a given reference signal.

The present application relates to the field of flexible actuator driven rehabilitation robot control, and discloses a rehabilitation robot control method based on interference compensation and finite time instruction filtering, which comprises a finite time instruction filter, a finite time filtering error compensator, a first finite time disturbance observer, a second finite time disturbance observer and a composite anti-interference finite time backstepping controller. Compared with the traditional backstepping control method, the present application solves the problem of 'derivative explosion' in the traditional backstepping control method and the problem of filtering error reducing system control performance in the dynamic surface control method. In addition, the control method of the present application uses a finite time disturbance observer to estimate system disturbance, and can be applied to various types of interference suppression situations through disturbance compensation, thereby improving the anti-interference ability of the system.

The present invention provides an improved position control method for a direct-drive electrostatic-hydraulic actuator, and belongs to the field of control. The method comprises: firstly, considering the nonlinear characteristics of the direct-drive electrostatic-hydraulic actuator system, establishing a state space model of the system, and reasonably simplifying it into a strict feedback form, wherein the power unit of the direct-drive electrostatic-hydraulic actuator system is a permanent magnet synchronous motor, and the hydraulic pump is a gear pump. Secondly, the obstacle Lyapunov function is combined with the backstepping controller, and the constraint boundary is introduced to ensure the output constraint performance of the system. Finally, the adaptive rate of the uncertain parameters is established to adjust the parameter disturbance in real time online and perform stability analysis. The present invention can solve the problem of system parameter uncertainty in the position control process of the direct-drive electrostatic-hydraulic actuator, and improve the control accuracy and robustness of the direct-drive electrostatic-hydraulic actuator control system.

The application discloses a kind of active suspension nonlinear mapping constraint control methods based on fixed time, comprising the following steps: step 1: constructing nonlinear mapping function: by nonlinear mapping function, asymmetric time-varying constraint to system state variable is realized by coordinate conversion;The application is by constructing nonlinear mapping function to carry out coordinate conversion, and the constraint problem of original system state is converted into the bounded problem of new system state, while realizing asymmetric time-varying state constraint, avoid the limitation of feasibility condition to control design.On this basis, combined with fixed time stability theory to construct backstepping controller, so that closed-loop system realizes fixed time stability under time-varying asymmetric constraint condition, and its convergence time avoids dependence on initial state, so as to improve the rapidity, robustness and safety of active suspension system.

In order to improve the stability and anti-interference performance of a single-inverter double-parallel motor driving system, the invention provides a self-adaptive speed control method for a single-inverter double-parallel permanent magnet synchronous motor. Firstly, an equivalent mathematical model of a system is established through non-singular coordinate transformation, and the coupling relation between inverter output voltage and a system control target is disclosed. On the basis, the design idea of a backstepping controller is combined, and a basic control framework of the double parallel motor driving system is constructed. Based on a deterministic equivalence principle, a self-adaptive average speed controller is designed, and the robustness of the system to parameter mismatch and load disturbance is remarkably enhanced. A self-adaptive differential speed controller is designed in combination with a gradient search method, and the risk of instability caused by system singular points is effectively avoided through online gradient correction of controller gain.

The invention relates to a tandem unmanned aerial vehicle suspended load system load state estimation and control method, which comprises the following steps of: establishing a dynamical model of an unmanned aerial vehicle suspended load system based on a Newton's law of motion, including dynamical models based on two different state variables respectively; an observer based on a Kalman filter is constructed based on the dynamical model corresponding to the first state variable, cable tension acting on the load is measured through a multi-axial force sensor and serves as known input of the observer, and load state estimation is achieved; and converting the load state estimation result under the first state variable into a second state variable, and constructing a nonlinear backstepping controller with a saturation mechanism in combination with a kinetic model corresponding to the second state variable, thereby realizing trajectory tracking control of the unmanned aerial vehicle suspended load system. Compared with the prior art, the change characteristics of the load state can be accurately captured, the complexity caused by sensor installation and wiring is avoided, and the method has the advantages of being high in real-time performance, high in control precision and the like.

This invention discloses a backstepping-based variable iterative learning control method for robotic arms, applicable to motor-driven robotic arm systems. This method converts the robotic arm system model into a third-order strict feedback form. By defining coordinate transformation error and introducing a command filter, an auxiliary system is constructed to compensate for the deviation between the filter and the virtual control law. A radial basis function neural network is used to approximate the unknown dynamics and disturbances of the system online. A backstepping controller is designed based on the compensation error, and a parameter learning law with variable iteration length is constructed to ensure system stability and convergence of the compensation error. By dynamically adjusting the iteration length during the control phase, this method can reduce computational resource consumption while maintaining high-precision tracking performance. When used for robotic arm control of repetitive tasks, it significantly improves the tracking accuracy and robustness of the system, demonstrating good engineering application value.

This application discloses a fuzzy optimization formation control method and system for multiple unmanned systems, belonging to the field of unmanned control technology. The method includes: after confirming the positions of the target system and neighboring systems, calculating the position errors of the target system and neighboring systems; inputting the position data and position errors of the target system into a fuzzy identifier to obtain dynamic data; designing an adaptive backstepping controller based on the target system's position data, position errors, and dynamic data to obtain the adaptive backstepping control input; obtaining the optimal control input based on the new error dynamic system, and applying smooth saturation constraints to the adaptive backstepping control input and the optimal control input to obtain the control signal. This application utilizes the universal approximation characteristic of fuzzy logic systems to establish a fuzzy identifier that approximates the dynamic data of the target system and a formation optimization controller with a single evaluation structure, solving the technical problem of control performance degradation caused by input saturation in unmanned formations.

The application discloses a hovercraft filtering backstepping trajectory tracking control method based on RLESO, and first establishes a three-degree-of-freedom mathematical model of hovercraft movement, compares actual trajectory information of the hovercraft with reference trajectory information to obtain a position error dynamics model of the hovercraft; designs RLESO estimation and compensation of unknown environmental disturbance of the hovercraft based on state information of the mathematical model of the hovercraft; combines the position error dynamics model to construct a time-varying BLF with a position error constraint function; then designs a position error constraint-based hovercraft command filter backstepping controller according to the BLF, backstepping technology and a second-order command filter, and completes a trajectory tracking control target of the hovercraft. The hovercraft filtering backstepping trajectory tracking control method based on RLESO can improve safety performance and controllability of the hovercraft, can make the hovercraft obtain better performance under the influence of external marine environment, and can improve tracking precision of the hovercraft.

The application belongs to the technical field of flight control. The application provides a predetermined time robust control method for an aircraft attitude angle under unknown disturbance. The method comprises: establishing an attitude angle and angular rate dynamics equation of the aircraft according to wind tunnel experiment data; considering the uncertainty and unknown disturbance existing in the dynamics equation, modeling the uncertainty and unknown disturbance into a form of comprehensive disturbance, and converting the dynamics equation into an affine linearization form containing the disturbance; designing a predetermined time disturbance estimator to realize accurate estimation of the comprehensive disturbance, designing an attitude angle predetermined time backstepping controller, and compensating the disturbance in the controller to realize predetermined time control of the aircraft attitude angle. The method of the disclosed embodiment has the advantages of simple structure and strong robustness, simplifies the parameter adjustment requirement for the convergence time, and can realize predetermined time control of the aircraft attitude angle under unknown disturbance.

The invention provides a hydrogen supply control system and method of a hydrogen fuel cell, the cell and a vehicle. The control system comprises a backstepping method controller; the backstepping method controller is connected with the hydrogen circulating pump, the pressure difference of hydrogen in the hydrogen supply pipeline serves as input, hydrogen partial pressure of an anode channel of the electric pile serves as feedback input, backstepping method control over the hydrogen excess ratio is achieved, and the pressure difference of the hydrogen supply pipeline is obtained through the hydrogen excess ratio. By means of the technical scheme, high-precision tracking control over the hydrogen excess ratio can be achieved, and therefore the control precision and the response speed of the hydrogen excess ratio under the dynamic working condition are improved.

The present application relates to a kind of cone-shaped dielectric elastomer actuator (Dielectric Elastomer Actuator, DEA) implicit inverse control method for input square state constraint, aims at effectively reducing the influence of hysteresis on system control performance, and solve the input square full state constraint problem in DEA control, including the following steps: based on traditional Preisach model, input square Preisach model is proposed, the hysteresis modeling when input is voltage square term in DEA system is realized.Combined with Fuzzy Logic System (FLS) and Barrier Lyapunov Function (BLF), a new adaptive fuzzy segmented implicit inverse controller is proposed for DEA system with input square state constraint.Based on the control platform built, compared with the controller without implicit inverse and the traditional Backstepping controller, the accuracy and effectiveness of the designed control strategy are verified, the proposed control algorithm has higher precision, and the high-precision control goal of dielectric elastomer actuator is realized.

The present invention discloses a method and control system for reducing the sway of a suspended UAV in flight. The method uses an observer to observe the suspended UAV flight system using a control signal U and an observer, and adds an estimated value obtained by the observation when generating a new control signal to offset the adverse effects of disturbances on the flight control of the system. Furthermore, the control signal U output by the control method of the present invention enables the UAV to achieve high-precision position tracking and suppress the sway of the suspended load, ensuring that the UAV position tracking error and the load sway angle vary within a constrained range. The UAV suspended flight reduction control system of the present invention is used to implement the above-mentioned control method. By observing the UAV through an observer to obtain an estimated value, and outputting a control signal to the UAV flight system through a backstepping controller, the UAV can achieve precise control of the UAV position and sway reduction control of the suspended load.

This application relates to the field of servo system control technology, and provides a green disturbance rejection control method and device for a CMG frame servo system. The method establishes a mathematical model of the CMG frame servo system under multi-source disturbances based on a high-order full-drive method, and models the multi-source disturbances as external sources. Based on the external source model and the system mathematical model, a fine disturbance separation estimator is determined. The fine disturbance separation estimator is used to determine the equivalent estimate of the multi-source disturbances. Based on this equivalent estimate, a state observer is determined, and then the state observer is used to determine the estimate of the higher-order state variables. Finally, a backstepping controller based on a barrier Lyapunov function is established using the estimates of the multi-source disturbances and the higher-order state variables. This backstepping controller controls the CMG frame servo system, providing more adjustable degrees of freedom for control performance optimization, enhancing the fine disturbance rejection capability of the CMG frame servo system under multiple constraints, and exhibiting simple design and low energy consumption.