Trajectory learning controller, control system and method with initial state error correction

Abstract

The invention discloses a track learning controller, control system and method with initial state error correction. The controller includes a motion trajectory receiving module configured to receive a motion trajectory output by a wheeled robot and a motion initial state of the wheeled robot. The controller also comprises a tracking error calculation module configured to calculate the difference between the received motion trajectory and motion initial state and a preset expected trajectory stored inside to obtain a tracking error and an initial state error. The controller comprises a compare correction module configured to compare the tracking error with a tracking error threshold; if the tracking error is greater than the tracking error threshold, current control inputs of the wheeled robot are corrected according to the initial state error and the current tracking error, and the modified control inputs are applied to the wheeled robot until the tracking error is smaller than the tracking error threshold; and finally the complete tracking of the preset expected trajectory of the wheeled robot is achieved. The control inputs are the linear velocity and angular velocity of the wheeled robot.

Description

technical field [0001] The invention belongs to the field of robot control, and in particular relates to a trajectory learning controller with initial state error correction, a control system and a method. Background technique [0002] The trajectory tracking feedback control method in the prior art has high requirements on the accuracy of the robot model, requires a lot of prior knowledge, the calculation process is complicated, and it can only realize the gradual tracking of the desired trajectory. [0003] Although the trajectory tracking learning control algorithm can achieve complete tracking of the desired trajectory, the existing methods require the system to meet strict reset conditions, that is, the initial state of the system at each iteration is consistent with the initial state of the expected trajectory. When trying, due to the limitation of repeated positioning accuracy, the initial state of the robot is prone to initial positioning errors. Because the output ...

AI Technical Summary

Problems solved by technology

[0002] The trajectory tracking feedback control method in the prior art has high requirements on the accuracy of the robot model, requires a lot of prior knowledge, and the calculation process is complex and can only realize the gradual tracking of the desired trajectory.

[0003] Although the trajectory tracking learning control algorithm can achieve complete tracking of the desired trajectory, the existing methods require the system to meet strict reset conditions, that is, the initial state of the system at each iteration is consistent with the initial state of the expected trajectory. When trying, due to the limitation of repeated positioning accuracy, the initial state of the robot is prone to initial positioning errors

Because the output trajectory of the robot control system has a continuous dependence on the initial value, the accumulation of initial positioning errors will lead to a large error between the tracking trajectory and the expected trajectory, reducing the tracking accuracy

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