49results about How to "Improve trajectory tracking performance" patented technology

The invention provides a self-adaptation trajectory tracking control method for an autonomous underwater vehicle horizontal plane. By means of a high-gain state observer method, the speed and the angular speed of an AUV are estimated, and high-precision approximation function compensation model parameter uncertain items and external disturbance items of a radial basis function (RBF) neural networkis adopted are used for converting an AUV trajectory tracking problem into a tracking problem under polar coordinate system through coordinate transformation. During the specific solution, firstly, the expectation input of a kinematic model is designed, then, expectation input of the kinematic model is designed, finally, the RBF neural network is used for estimating uncertain items in expectationinput, the neural network weight updating rules are designed, and finally the AUV tracks the expected trajectory.

The invention relates to a fish trajectory tracking method and system based on an artificial neural network. The method comprises the following steps of S1, acquiring a video or an image of a target fish; S2, obtaining a fish body image with a label as an initial training sample set, inputting the initial training sample set into a Unet convolutional neural network, and carrying out iterative training through a deep learning algorithm to obtain a fish body recognition neural network model; S3, recognizing a fish body in the acquired video or image of the target fish by utilizing a fish body recognition neural network model, segmenting a fish body form, and calculating the position of a preset point in the fish body; and S4, obtaining the motion trail of the target fish by using a target tracking algorithm, and analyzing the motion characteristics of the target fish. The method can accurately perform identification and trajectory tracking on the fish body, and thereby dependence of an experiment on an illumination condition is reduced, influence of mirror reflection and water surface fluctuation of the fish tank are overcome, high calculation efficiency is realized, the method can be applied to a long-time experiment video, and identification and trajectory tracking effects are improved.

The invention discloses a neural network adaptive trajectory tracking control method for a permanent magnet synchronous linear motor and belongs to the field of linear motor motion control technologies. The method utilizes the nonlinear mapping capability of a neural network controller to establish model estimation and compensation links of the permanent magnet synchronous linear motor, so that good trajectory tracking performance of the motor is realized. The control method comprises two parts including a neural network controller based on a radial basis function (RBF) and a robust controller with feedback gain used for carrying out model estimation and compensation and restraining the influence of a model error respectively. According to the method, the nonlinear approximation characteristic of the neural network is utilized to establish the nonlinear compensation link of the model of the permanent magnet synchronous linear motor, so that the influence of nonlinear factors, such as dead zone and thrust ripple, in a motor operation process are effectively overcome, the method has stronger anti-interference capability and good stability, and the good trajectory tracking performance can be realized.

The invention discloses a robot trajectory tracking method, a robot trajectory tracking system and a storage medium which integrate the trajectory optimization and control, and better ensure the feasibility of trajectory tracking of a robot. When the robot passes through a dense obstacle, the weight in the target function is correspondingly adjusted according to the shortest distance between the current position of the differential robot and the obstacle by adopting the self-adaptive weight, so that the safety and the running speed can be considered when the robot performs trajectory tracking,and the trajectory tracking performance of an obstacle area is improved. According to the method, the obstacle avoidance constraint is put into the target function and is converted into the soft constraint, the optimal control input is found in the mode of sampling in the allowable control space, the non-convex optimization problem is solved, and meanwhile, the obstacle avoidance performance of the mobile robot is improved in the mode of dynamically adjusting the weight.

The invention provides a tailstock type unmanned aerial vehicle trajectory tracking fault-tolerant control method based on a self-adaptive controller. According to the method, adaptive controllers aredesigned for the nonlinear dynamics and kinematics model of a tailstock type unmanned aerial vehicle, wherein the controllers comprise a position adaptive controller and an angle adaptive controller,so that expected trajectory tracking performance can be achieved; in order to ensure the reliability of flight, an actuating mechanism damage fault is added into the model of the tailstock type unmanned aerial vehicle, and fault-tolerant control is carried out to realize trajectory tracking in a fault state. Compared with other control methods, the method can just use one set of parameters to perform control, is convenient to operate and can effectively reduce the influence of external interference and uncertainty on results and improve the stability of the tailstock type unmanned aerial vehicle, thereby achieving the expected trajectory tracking performance.

The invention discloses an anti-interference synchronous sliding mode control method for hybrid type automobile electrophoresis coating conveying mechanism. The method comprises the following steps: firstly, for the hybrid type automobile electrophoresis coating conveying mechanism, using the Lagrangian method to create a kinetic model containing the total disturbance term and performing motion trajectory planning to the mechanism; then, using the detection result from a decoder to calculate and obtain the tracking errors of the active joints and defining a coupling error; after that, based on the coupling error, designing a cross coupling synchronous sliding mode controller; based on the kinetic model, designing a non-linear disturbance observer; further, in combination with the cross coupling synchronous sliding mode controller and the non-linear disturbance observer, developing an anti-interference synchronous sliding mode controller; and finally, through the software programming, realizing the anti-interference synchronous sliding mode control to the conveying mechanism. According to the invention, not only the synchronous performance of the conveying system is increased; but also the robustness and the anti-interference capability of the system are improved. In addition, the problems with the jittering in sliding mode control and the saturation of the execution device of the control system can be inhibited.

The invention discloses a loaded quadrotor unmanned aerial vehicle speed control method with completely unknown model parameters. The method comprises the steps: 1) establishing a kinetic model of a loaded quadrotor unmanned aerial vehicle; (2) setting a speed tracking error ev = v-v * and ew = w-w * of the unmanned aerial vehicle; (3) preprocessing an unknown gain matrix B by using matrix decomposition; and (4) designing controllers of a translation subsystem and a rotation subsystem of the loaded quadrotor unmanned aerial vehicle under the condition that the model parameters are completelyunknown. According to the invention, actual application conditions of the loaded unmanned aerial vehicle, such as completely unknown model parameters, time-varying unknown gain matrixes, actuator saturation and actuator faults, are considered; the design problem of the controller under the condition that the gain matrix is unknown after loading is solved; the proposed controller not only can ensure that all internal signals in a closed-loop system are bounded and continuous, but also can ensure that final errors converge to a small tight set, and can ensure that state constraint conditions arenot violated in the process of obtaining good trajectory tracking performance.

The invention provides a sectional pressure set value optimization method of a hydraulic propelling system of a heading machine. The method comprises the steps of dividing the hydraulic cylinder of the hydraulic propelling system of the heading machine into N sections, calculating the resultant force and the resultant moment provided by the hydraulic cylinder in each section by use of a linear quadratic form regulator, and obtaining the optimized resultant force provided by the hydraulic cylinder in each section by virtue of quadratic programming, thereby obtaining the hydraulic cylinder pressure set value in each section. According to the sectional pressure set value optimization method of the hydraulic propelling system of the heading machine, the hydraulic cylinder of the hydraulic propelling system of the heading machine is divided into N sections, the pressure set value of the hydraulic cylinder in each section is the same and the control on the hydraulic propelling system is simplified; the resultant force of each section is optimized by virtue of quadratic programming so as to realize excellent tracking capability of main trajectory of the heading machine; the linear quadratic form regulator is combined with a conventional controller to further improve the trajectory tracking capability.

The invention discloses a speed control method for a loaded quadrotor unmanned aerial vehicle with known model parameters. The method comprises the following steps: 1) establishing a kinetic model ofthe loaded quadrotor unmanned aerial vehicle; 2) setting a speed tracking error ev = v-v* and ew = w-w* of the unmanned aerial vehicle, 3) additionally arranging a sensor on the unmanned aerial vehicle to measure swing angles alpha and beta in the flight process, and calculating a time-varying gain matrix B; and 4) designing a controller of a translation subsystem and a rotation subsystem of the loaded quadrotor unmanned aerial vehicle with known model parameters. According to the method, flight speed control of the loaded unmanned aerial vehicle under the actual conditions of a time-varying gain matrix, actuator saturation, actuator faults and the like is considered; the problem in controller design under time varying of a gain matrix after loading is solved, the proposed controller not only can ensure that all internal signals in a closed-loop system are bounded and continuous, but also can ensure that final errors converge in a small compact set, and it can be ensured that state constraint conditions are not violated in the process of obtaining good trajectory tracking performance.

The invention relates to an accelerated speed optimal space robot online track planning method based on control period self-adaptive clock synchronization. The method aims at solving the problem that a robot is not stable in the motion process due to asynchronization of clocks of an upper computer and a lower computer in space robot control and the intense change of the accelerated speed of the track of a joint layer. The method includes the steps that firstly, mathematic models of the joint track curve position, the speed and the accelerated speed of the space robot with the optimal accelerated speed within a track segment planning period are established and a joint track equation is obtained; secondly, according to the continuity condition of joint track interpolation, parameters in the joint track equation in the first step are obtained, and a joint space track is continuously planned; thirdly, on the basis that the joint space track is continuously planned, synchronous control is conducted. The method is applied to the robot control field.

The invention discloses a robot self-adaption fuzzy control method and system. An approximate error is in a time variant mode, the time-variant approximate error is introduced into an improved fuzzy logic system, compensation of the time-variant error is achieved, and the system online calculation amount is decreased; in addition, an auxiliary system is added and used for compensating an unknown input dead zone of a robot system, the input dead zone serves as an unknown model instead of a specific model, the auxiliary system can process different dead zone situations in a self adaptive mode, and the trajectory tracking performance of the robot system is advantageously improved.

A trailer with better trace tracking performance is composed of chassis, front and back wheels and their axles, a pull-rod for linking the trailer to tractor, steering gear mechanism and vertical axle. Said front wheel axle is linked to chassis via vertical axle and can rotate around the vertical axle under the drive of pull-rod can rotate around the vertical axle under the drive of pull-rod and steering gear mechanism. It features that the pull angle of pull-rod and the steering angle of front wheel axle must meet a predefined relation.

The invention relates to a robot teleoperation track planning method based on control behavior detection, and belongs to the technical field of robot and human interaction. The method comprises the steps: firstly, establishing a human-computer interaction model of a teleoperation robot, detecting and estimating human operation behaviors by a sliding mode control and neural network technology based on theoretical analysis and parameter confirmation, applying the obtained operation behaviors to expected track tracking and updating, and controlling the teleoperation robot to move according to a formed new reference track, and thus in the process of man-machine interaction of the teleoperation robot, enabling an operator to perceive expected impedance characteristics through interaction behaviors. According to the method, human operation behaviors of a master end are detected, evaluated and reconstructed, influences of uncertainty and disturbance on the human operation behaviors are drawn by behaviors of operators and are actively compatible, the influences are regarded as active contribution, the expected track of a slave end is designed and updated, the effect of improving the consistency of the master behaviors and the slave behaviors is achieved, and the track tracking effect is improved.

The invention provides a double-steering-wheel AGV path tracking method, relates to the technical field of automatic control, and aims to effectively improve the trajectory tracking effect of a double-steering-wheel AGV and ensure real-time deviation correction of an AGV body so as to control the AGV body to accurately travel along a target path. The method comprises the following steps of: S1, establishing a kinematic model of a double-steering-wheel AGV; S2, according to odometer information of the AGV body and the real-time vehicle body attitude data, calculating an driving track of the AGV; S3, calculating the attitude deviation of the AGV at the current moment according to the calculated driving track of the AGV; S4, according to the calculated attitude deviation of the AGV, obtaininga target control quantity of the vehicle body in the next period; and S5, calculating the adjustment amount of the double steering wheels through the target control amount, calculated in the step S4,of the next period in combination with the kinematics model of the double-steering-wheel AGV, thus achieving deviation rectification. The technical scheme provided by the invention is suitable for the driving process of the double-steering-wheel AGV.

The invention provides a data-based sewage treatment system self-learning trajectory tracking method, which realizes a self-learning optimal tracking control algorithm of a non-affine nonlinear systemby using a DHP structure, and is applied to sewage treatment process control in rainstorm weather. According to the method, a numerical method is adopted to solve stability control corresponding to an expected trajectory, and then a data-driven self-learning method for solving the optimal control law of the non-affine system based on iterative DHP is established. The method is applied to concentration control of dissolved oxygen and nitrate nitrogen so as to achieve a good trajectory tracking effect of a sewage treatment system.

The invention discloses an estimation control method applied to trajectory control of automatic driving, and belongs to the technical field of basic intelligent driving. Firstly, a domain controller receives signals input by a virtual brain controller and a whole vehicle CAN network in real time, then transverse deviation dy and course angle deviation dphi in the signals are filtered and substituted into a dynamic error prediction and estimation model, and new transverse deviation dy, course angle deviation dphi, transverse deviation change rate and course angle deviation change rate are obtained through calculation; the lateral deviation dy, the lateral deviation change rate, the steering angle deviation dphi and the steering angle deviation change rate of future Hp points are estimated according to an estimation model of the deviation dynamic variation; and finally, a steering angle is calculated and input to a steering angle control system in a CAN message form, and a steering control output request needs to be limited by the physical limit of stable operation of the active steering system. According to the invention, the safety and comfort of automatic driving are improved, and the adaptive scene of automatic driving is expanded.

The invention provides a quadruped robot single-leg trajectory tracking control method and system. The quadruped robot single-leg trajectory tracking control method comprises the steps of obtaining joint parameters of a quadruped robot; establishing a single-leg dynamic model according to the joint parameters; correcting the single-leg dynamical model based on disturbance and a neural network, constructing a single-leg dynamical model under the condition of considering disturbance, and assuming a reference trajectory; obtaining an optimal parameter of the reference trajectory by using a value function approximation method based on neural network learning, and obtaining an optimal control rate; and achieving single-leg trajectory tracking control of the quadruped robot based on the optimal control rate. The method solves the problem of how to compensate the influence of nonlinearity and errors, improves the performance of a nonlinear control system of the quadruped robot, guarantees that the trajectory tracking error of the quadruped robot is gradually converged to zero, and achieves the high-precision trajectory tracking control of the quadruped robot.

The invention discloses an intelligent trajectory tracking control method for a mobile robot, and the method is characterized by estimating a weight value of a given mobile robot model through the convergence of a least square method; updating the weight of a controller by introducing a neural network and a strategy iteration method in reinforcement learning, solving an optimal value function. The method comprises the following steps: 1) establishing a mobile robot nonlinear system; 2) initializing a state variable of the system, and giving an initial strategy; 3) updating the weight by using a reinforcement learning strategy iteration method, and solving an optimal strategy; 4) introducing a neural network algorithm and calculating a weight by using the convergence of least squares; and 5) stopping strategy updating. According to the method, a relatively good trajectory tracking effect is ensured under the condition that internal dynamic knowledge of a power system is not needed.

The invention discloses a visual tracking quadruped robot and a feature information extraction algorithm of a tracking line of the visual tracking quadruped robot. The visual tracking quadruped robot comprises an eight-degree-of-freedom quadruped walking robot body, a camera module, an upper computer module, a lower computer module and a steering engine. The four-legged walking robot body adopts a full-elbow type four-legged robot series leg structure; the steering engine serves as an executing mechanism of leg joints of the four-footed walking robot body, the camera module is arranged at the head of the four-footed walking robot body and connected with the upper computer module through a usb, the upper computer module is used for processing video frames collected by the camera module in real time, and the upper computer module is connected with the lower computer module through a serial port. The lower computer module receives serial port communication of the upper computer module to execute gaits, the upper computer module improves the recognition capability of black tracking lines through a processing method of cutting an area of interest. According to the invention, the trajectory tracking effect of the four-footed walking robot body is improved through a bionic gait tracking strategy of the lower computer module.

The invention relates to the technical field of robot control. The invention discloses a self-adaptive iterative learning control method. The method comprises the following steps: a, solving a robot position error; b, solving a robot speed error; c, inputting the expected position of a robot, the expected speed of the robot, the actual position after k times of iteration and the actual speed afterk times of iteration into a parameter adaptive control module; d, respectively performing proportional operation and differential operation on the position error and the speed error, and then inputting the position error and the speed error into a variable gain feedback control module; e, adding the output of the parameter adaptive control module and the output of the variable gain feedback control module to obtain a control moment; f, taking the control moment as a control moment for controlling the (k + 1)-th iteration of the robot, wherein k is the number of iterations, and k is equal to 1, 2,.... The invention also discloses an adaptive iterative learning control system. According to the method, the convergence rate of the algorithm is considered while the parameter uncertainty of theindustrial robot is solved, and the position and speed tracking precision of the industrial robot can be effectively ensured.

The invention discloses an H-infinity feedback and iterative learning coordination control method based on error analysis. Two errors overline(e<r>(t)) and overline(e<nr>(t)) are defined, and frequency spectrums of repeatable interference and unrepeatable interference are estimated by analyzing signals of the two errors in actual operation so as to design a filter Q(s) and a feedback controller K(s) to enable coordination work of iterative learning control and H-infinity feedback control. Influences of the repeatable interference and the unrepeatable interference can be reduced by the aid of coordination work of iterative learning control and H-infinity feedback control, so that a control system has excellent astringency to carry out more accurate trajectory tracking.

The invention relates to an autonomous overtaking method and system. The method includes the steps of: defining a state lattice space; determining trajectory parameters in different overtaking stages according to the state lattice space; constructing a vehicle motion model according to the state lattice space and the trajectory parameters in different overtaking stages; generating a vehicle prediction track according to the vehicle motion model; and performing overtaking trajectory tracking according to the vehicle prediction trajectory, and determining an overtaking decision based on a semi-Markov decision process. Safe and efficient autonomous overtaking of the vehicle is achieved, and the problem that trajectory tracking is difficult in the overtaking process in the prior art is solved.

The invention provides a control method and control device for an airship. The control method comprises the steps of acquiring route information of the airship and state information of the airship, determining speed and tangential acceleration of the airship based on the route information and the state information, and controlling the airship based on the speed and the tangential acceleration. Thecontrol method and control device for the airship acquire the route information of the airship and the state information of the airship, then determine the speed and the tangential acceleration of the airship base on the route information and the state information and finally control the airship based on the speed and the tangential acceleration, thereby ensuring the stable flight of the airshipand improving the trajectory tracking ability of the airship at the same time.

This application is a robot self-adaptive fuzzy control method and system, which regards the approximation error as time-varying, introduces the time-varying approximation error into the improved fuzzy logic system, realizes the compensation of the time-varying error and reduces the system online operation In addition, the present invention also adds the design of the auxiliary system, which is used to compensate the unknown input dead zone of the robot system, regards the input dead zone as an unknown model rather than a specific model, and can adaptively handle different dead zone situations, It is beneficial to improve the trajectory tracking performance of the robot system.

An intelligent trajectory tracking control method for a mobile robot. For a given mobile robot model, the convergence of the least squares method is used to estimate the weights; and the weights of the controller are updated by introducing the method of neural network and strategy iteration in reinforcement learning, Solving the optimal value function, the method includes the following steps: 1) establishing a mobile robot nonlinear system; 2) initializing the state variables of the system, and giving an initial strategy; 3) using the method of reinforcement learning strategy iteration to update the weights , to find the optimal policy; 4) Introduce the neural network algorithm and use the convergence of least squares to calculate the weights; 5) Stop the policy update. The present invention ensures a better trajectory tracking effect without requiring dynamic knowledge inside the power system.

The invention relates to a nonlinear aircraft robust control method considering actuator saturation, and belongs to the field of flight control. The method comprises the following steps: decomposing an aircraft model into a linear main system considering all interferences and uncertainty and a nonlinear auxiliary system considering actuator saturation; designing an observer to estimate the state of the auxiliary system and the output of the main system; designing the controllers for the main system and the auxiliary system respectively, and after the controllers of the main system and the auxiliary system are designed, obtaining the controller of the original system by integrating the controllers of the main system and the auxiliary system. According to the method, the influence of nonlinear information, uncertainty and interference of the system is fully considered, and a good trajectory tracking effect can be obtained; the influence of actuator saturation on system control performance is fully considered, and the classical linear control method can be still adopted in the main system through compensation of the auxiliary system. The method is simple, effective and high in flexibility and reliability.

The invention discloses an anti-interference synchronous sliding mode control method for hybrid type automobile electrophoresis coating conveying mechanism. The method comprises the following steps: firstly, for the hybrid type automobile electrophoresis coating conveying mechanism, using the Lagrangian method to create a kinetic model containing the total disturbance term and performing motion trajectory planning to the mechanism; then, using the detection result from a decoder to calculate and obtain the tracking errors of the active joints and defining a coupling error; after that, based on the coupling error, designing a cross coupling synchronous sliding mode controller; based on the kinetic model, designing a non-linear disturbance observer; further, in combination with the cross coupling synchronous sliding mode controller and the non-linear disturbance observer, developing an anti-interference synchronous sliding mode controller; and finally, through the software programming, realizing the anti-interference synchronous sliding mode control to the conveying mechanism. According to the invention, not only the synchronous performance of the conveying system is increased; but also the robustness and the anti-interference capability of the system are improved. In addition, the problems with the jittering in sliding mode control and the saturation of the execution device of the control system can be inhibited.

The invention relates to the technical field of navigation instruction analysis, and discloses a navigation instruction analysis method of a single-steering-wheel type transfer robot. The method comprises the steps of S1, receiving a signal, and receiving a signal instruction of departure of a navigation system; S2, predicting the position of a forklift; S3, setting a state variable, wherein the set state variable is represented by x; S4, establishing an observation equation, and and establishing a state observation equation of the single-steering-wheel forklift after the state variable is set; and S5, analyzing the state, calculating by using a Kalman filtering algorithm during state analysis, wherein the state analysis includes state prediction, position correction and noise estimation.According to the method, the navigation instruction is accurately analyzed into the execution speed and angle of the steering wheel, the track tracking performance of automatic driving of the forkliftis greatly improved, the safety and reliability of automatic driving of the forklift are guaranteed, and the operation efficiency of the forklift is improved.

The invention discloses a neural network adaptive trajectory tracking control method for a permanent magnet synchronous linear motor and belongs to the field of linear motor motion control technologies. The method utilizes the nonlinear mapping capability of a neural network controller to establish model estimation and compensation links of the permanent magnet synchronous linear motor, so that good trajectory tracking performance of the motor is realized. The control method comprises two parts including a neural network controller based on a radial basis function (RBF) and a robust controller with feedback gain used for carrying out model estimation and compensation and restraining the influence of a model error respectively. According to the method, the nonlinear approximation characteristic of the neural network is utilized to establish the nonlinear compensation link of the model of the permanent magnet synchronous linear motor, so that the influence of nonlinear factors, such as dead zone and thrust ripple, in a motor operation process are effectively overcome, the method has stronger anti-interference capability and good stability, and the good trajectory tracking performance can be realized.