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 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.

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 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 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.