30 results about "Backstepping controller" patented technology

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 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 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 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 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.

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 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 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.

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.

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 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.

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.

The application discloses a kind of based on manned submersible's submarine pipeline leak source searching method and system, method includes: the system model of establishing including submarine oil spill diffusion model, submersible upper layer control model and oil concentration sensor model;Based on the estimation framework of bayes, fusion real-time measurement information, adopt particle filter iterative update the posterior probability distribution of leak source state;Based on posterior distribution, adopt exploration and development dual control strategy to construct objective function, solve optimal control quantity and expected target position;According to target position, based on the dynamics model of submersible, design based on interference observer's backstepping controller drive submersible accurate tracking.The application realizes the autonomous positioning of leak source under complex submarine environment, with strong adaptability, high search efficiency, the advantages of good anti-interference ability, suitable for emergency search task of sudden leakage.

This disclosure provides a method and system for safe control of unmanned vehicles based on derivative-aware reinforcement learning, relating to the field of vehicle safety control technology. The method includes: establishing a three-degree-of-freedom disturbed vehicle horizontal plane dynamic model and a DoS attack model based on the dynamic characteristics of the unmanned vehicle, and describing the vehicle horizontal plane dynamic model as a disturbed second-order nonlinear model; designing a finite-time switching state observer and a safe backstepping controller based on the disturbed second-order nonlinear model; inputting the parameter errors and external disturbances observed by the switching state observer to the safe backstepping controller; integrating a derivative-aware Actor-Critic reinforcement learning enhancement channel and a steady-state priority scheduler on the basis of safe robust backstepping control; enhancing the online optimization of transient tracking performance through reinforcement learning during the intervals of the DoS attack model; and reducing the reinforcement learning authority in the steady-state region through the steady-state priority scheduler to protect the steady-state error accuracy of the safe main control. This disclosure protects the steady-state error accuracy of the safe main control.

The invention provides a sensorless robust prediction-backstepping double-loop control method and device for a permanent magnet synchronous motor, and solves the problems that in an existing sensorless control system of the permanent magnet synchronous motor, a PI control structure is difficult to give consideration to fast dynamic response and robust stability, and a complex nonlinear algorithm is poor in real-time performance and insufficient in realizability. According to the method, a sliding-mode observer based on a Sigmoid function is constructed to estimate induced electromotive force, and a phase-locked loop is combined to extract a smooth rotor speed and position estimation value; a robust prediction controller based on a proportion-integral type error trend index is adopted in a rotating speed outer ring, an integral type backstepping controller is adopted in a current inner ring, and the motor is driven to operate through SVPWM modulation. According to the invention, through prediction-backstepping double-loop cooperative control, the dynamic response speed, anti-disturbance capability and stability of the system are substantially enhanced, the structure is simple, and engineering realization is easy.

The application discloses a heave compensation winch interference compensation control method. The method comprises the following steps: based on the state space model of a heave compensation winch system comprising a heave compensation winch and a pump station, a backstepping controller is established by a backstepping control method; an online parameter estimation module of the hydraulic motor of the heave compensation winch is established, the initial identification parameters of the hydraulic motor and the dynamic regression vector of the hydraulic cylinder are inputted for processing, and the identification parameters of the hydraulic motor are obtained in real time; the identification parameters and the operation parameters of the heave compensation winch are inputted into the backstepping controller, the valve core displacement control instruction of the proportional servo valve of the pump station is outputted after processing, and then the interference compensation control of the heave compensation winch is realized by controlling the pump station. The method can still maintain the high-precision tracking and robust stability control of the heave compensation winch system under the condition of model uncertainty and strong disturbance, and provides support for underwater high-precision operation requirements.

The present application belongs to the technical field of motor nonlinear control, and particularly relates to a nonlinear control method for a motor system. The method first constructs an improved adaptive neural network backstepping controller, in the process of which a composite observer is constructed, the composite observer including a state observer and a disturbance observer to respectively achieve estimation of system states and external disturbances, and then the improved adaptive neural network backstepping controller constructed is used to control the motor system. The present application combines the composite observer with the adaptive neural network finite-time command filter control scheme, can estimate the unmeasurable states and disturbances in the system, and thus better controls the system, which is more practical in actual systems with many restrictions, so that the method of the present application can be more generally applicable in actual engineering.

The invention discloses a six-rotor unmanned aerial vehicle error tracking control method based on optimal specified performance, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the following steps: carrying out the modeling of the physical characteristics of a follower unmanned aerial vehicle, constructing a position dynamics model of an unmanned aerial vehicle follower, and converting the position dynamics model into a state model; introducing specified performance control based on the state model to ensure that the formation tracking error of each follower is strictly constrained in a preset performance boundary, and enabling the actual tracking error to track a preset and expected error trajectory; an optimized backstepping controller fusing fuzzy self-adaption and command filtering is designed to control a follower unmanned aerial vehicle in a six-rotor unmanned aerial vehicle system, so that a follower can accurately track the trajectory of a leader unmanned aerial vehicle, and it is ensured that the dynamic and steady-state processes of tracking errors strictly meet preset performance constraints. According to the method, error convergence and expected trajectory decoupling are realized, and the transient performance and time controllability of the formation system can be remarkably improved.

The invention discloses a submarine pipeline leakage source searching method and system based on a manned submersible. The method comprises the steps that a system model comprising a submarine oil spill diffusion model, a submersible upper layer control model and an oil contamination concentration sensor model is established; based on a Bayesian estimation framework, fusing real-time measurement information, and iteratively updating posterior probability distribution of a leakage source state by adopting particle filtering; based on posterior distribution, adopting an exploration and development dual control strategy to construct a target function, and solving an optimal control quantity and an expected target position; and designing a backstepping controller based on the disturbance observer to drive the submersible to perform accurate tracking according to the target position and based on the submersible dynamic model. The method achieves the self-positioning of the leakage source in a complex seabed environment, has the advantages of being high in adaptability, high in searching efficiency and good in anti-interference capability, and is suitable for a sudden leakage emergency searching task.

The application relates to the technical field of unmanned vehicle control, and specifically provides a propeller-driven unmanned vehicle motion control method under input saturation and speed constraint, clearly defines a trajectory tracking control target of the propeller-driven unmanned vehicle, analyzes an actuator physical constraint, an input saturation constraint and a speed state constraint of the propeller-driven unmanned vehicle, determines an input saturation constraint boundary and a speed state constraint boundary; based on a backstepping method design framework, according to the input saturation constraint boundary and the speed state constraint boundary, an auxiliary system method is adopted to process the input saturation constraint and the speed state constraint, a neural network adaptive method is adopted to approximate and compensate system uncertainty, and an adaptive neural network backstepping controller is constructed; the adaptive neural network backstepping controller is used for motion control of the propeller-driven unmanned vehicle; the adaptive neural network backstepping controller can process unknown dynamic and external disturbance of the unmanned vehicle and other uncertainty problems, and further provides stability of the system.

The application discloses a multi-unmanned ship longitudinal formation control method based on a nonlinear extended state observer, which comprises the following steps: establishing a kinematics model, a dynamics model and a wind disturbance model, and analyzing the boundedness of the wind disturbance; designing a time corresponding distance increment rule, generating a reference signal, constructing a longitudinal scheduling mixer and a double closed-loop control framework; establishing a kinematics error system, and deducing an error dynamics equation; designing a backstepping controller with a geometric compensation term, and outputting an expected speed; establishing an extended state system, and introducing equivalent external disturbances as extended states; designing a nonlinear extended state observer, outputting disturbance estimation values as disturbance compensation signals, and deducing a nonlinear error system; designing an integral sliding mode surface with a continuous nonlinear function; establishing a speed error system, and designing an integral sliding mode controller with a saturation function and disturbance feedforward compensation. The application designs a robust control and collaborative compensation mechanism for the working condition of narrow channel and superimposed wind disturbance, so as to guarantee the safe and reliable operation of the multi-ship longitudinal formation.

The application discloses a kind of error tracking control methods of six-rotor unmanned aerial vehicle based on optimal specified performance, it is related to unmanned aerial vehicle control technical field, including the following steps: the physical characteristics of follower unmanned aerial vehicle are modeled, the position dynamics model of unmanned aerial vehicle follower is constructed, and it is converted into state model;Based on the state model, introduce specified performance control, to ensure that the formation tracking error of each follower is strictly constrained within the preset performance boundary, and make actual tracking error can track a pre-set, expected error trajectory;Design fusion fuzzy adaptive and command filter optimization backstepping controller, control follower unmanned aerial vehicle in six-rotor unmanned aerial vehicle system, so that follower can accurately track the trajectory of leader unmanned aerial vehicle, and ensure that its tracking error dynamic and steady-state process strictly satisfy preset performance constraints.The application realizes the convergence of error and the decoupling of expected trajectory, can significantly improve the transient performance and time controllability of formation system.

The invention discloses an unmanned aerial vehicle formation cooperative control method and device based on a consistency theory and a backstepping design method, and relates to the field of unmanned aerial vehicle formation cooperative control. The problems that role differences between unmanned aerial vehicles in formation are not considered in an existing unmanned aerial vehicle formation method, a communication topology model of a directed graph or an undirected graph is singly used, and two-way sharing of states cannot be achieved between slaves if the directed graph is singly used are solved. According to the scheme, the method comprises the steps that a complete four-rotor unmanned aerial vehicle kinetic model is constructed and simplified, and a simplified model of the four-rotor unmanned aerial vehicle is obtained; a backstepping controller of the unmanned aerial vehicle is obtained by combining a backstepping design method; a position observer which is in the unmanned aerial vehicle formation and is input into a slave machine is obtained by combining a designed unmanned aerial vehicle formation cooperative control algorithm, a backstepping controller of the unmanned aerial vehicle and an obtained combined consistency theory of the communication topology model of the unmanned aerial vehicle formation; and cooperative control of the unmanned aerial vehicle formation is completed. The method is also suitable for the field of complete model analysis of the four-rotor unmanned aerial vehicle.

Aiming at the problem that the operation performance of a vortex spring energy storage system is weakened due to time delay phenomena such as sampling and communication, the invention designs a time-delay-considered permanent magnet synchronous motor backstepping control method for vortex spring energy storage. Establishing a permanent magnet synchronous motor mathematical model considering current delay under the dq0 coordinate system; on the basis, a Lyapunov function containing time delay is designed by adopting a Lyapunov-Krasovskii theory, and a time-delay backstepping controller is deduced by combining a backstepping control method, so that the influence of time delay on the system is eliminated, and the closed-loop stability of the system is ensured. Simulation and experiment results show that the influence of system control response deviation and fluctuation is increased along with the increase of time delay; compared with a conventional backstepping controller, the speed and the current of the permanent magnet synchronous motor under the backstepping controller considering the time delay can track respective reference values more accurately in the case of constant time delay and variable time delay, meanwhile, the response speed is higher, the fluctuation is smaller, and the influence of the time delay on the operation performance of the system is greatly improved.

The application discloses a fuzzy adaptive backstepping control method of a micro-electro-mechanical resonator, and steps include: fractional order micro-electro-mechanical resonator modeling; design of an inaccurate target trajectory fuzzy adaptive optimal backstepping controller with a disturbance compensation mechanism; stability analysis. In order to reduce the damage of matching disturbance to the control effect, the application proposes a disturbance compensation mechanism based on fractional order disturbance observation. Secondly, in order to approximate unknown functions in the fractional order MEMS resonator, an interval type-3 fuzzy logic system (IT3FLS) with an adaptive law is constructed. Then, the Fourier series and the IT3FLS are introduced to reconstruct the inaccurate target trajectory, and the fractional order hyperbolic tangent tracking differentiator is introduced to deal with the "complexity explosion" related to the traditional backstepping control. Finally, the optimal control input is embedded into the technical framework of the backstepping controller, so that the tracking accuracy of the controller is improved and the cost function is minimized.

The application discloses a learning-based composite layered anti-interference control system and method suitable for a large windward area unmanned aerial vehicle, and is used for realizing robust wind resistance control of the large windward area unmanned aerial vehicle. In view of the flight characteristics that the windward area is large and sensitive to external wind field changes in the vertical take-off and landing stage, an air speed estimation method without additional sensors is provided, modeling of external disturbance force is realized in combination with a Gaussian process regression algorithm, and compensation for the disturbance force is realized by using a model predictive control based on a quaternion, so that the influence of the external change wind field on the dynamics of the large windward area unmanned aerial vehicle is reduced, and the trajectory tracking precision is significantly improved. A backstepping controller based on SO(3) is designed in the attitude control loop, and a nonlinear disturbance observer is integrated, so that active compensation for the external disturbance torque and robust tracking control of the attitude are realized. Based on the above technical features, a complete dynamics control link capable of estimating and feeding back compensation disturbance in real time is further constructed.

To improve the stability and disturbance rejection performance of a single-inverter dual-parallel motor drive system, this invention proposes an adaptive speed control method for a single-inverter dual-parallel permanent magnet synchronous motor. First, an equivalent mathematical model of the system is established through non-singular coordinate transformation, revealing the coupling relationship between the inverter output voltage and the system control objective. Based on this, a basic control framework for the dual-parallel motor drive system is constructed by combining the design concept of a backstepping controller. Based on the deterministic equivalence principle, an adaptive average speed controller is designed, significantly enhancing the system's robustness to parameter mismatch and load disturbances. An adaptive differential speed controller is designed using the gradient search method, and through online gradient correction of the controller gain, the risk of instability caused by system singularities is effectively avoided.

The invention relates to the technical field of agent control, in particular to a fractional order multi-agent distributed optimization method under communication constraints, and the method comprises the steps: distributed optimization; the track is smooth; performance constraint; backstepping control; generating a signal; and parameters are self-adaptive. Local gradient information and neighbor state differences are fused through a distributed optimization algorithm, a high-order continuous piecewise polynomial is constructed through Hermite-Birkhoff interpolation, transient and steady behaviors of tracking errors are constrained within a preset range through a time-varying performance boundary function, system nonlinearity and fractional order dynamics are compensated through a fractional order backstepping controller, and the tracking precision is improved. The parallel adaptive law is based on real-time states, errors and control signals, unknown parameters are dynamically corrected, the reciprocal of gain and the estimated value of the upper bound of interference are controlled, and the problems that due to integer order modeling limitation, lack of an optimization mechanism and communication uncertainty, the system description precision is insufficient, the control target is single, and engineering implementation is difficult are effectively solved.