A time-optimal trajectory planning controller and method combined with iterative learning
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A technology of trajectory planning and iterative learning, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve the problems of model factory mismatch, time-optimized trajectory is not the optimal solution, etc., to improve errors, improve The effect of tracking performance
What is AI technical title?
AI technical title is built by PatSnap AI team. It summarizes the technical point description of the patent document.
A technology of trajectory planning and iterative learning, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve the problems of model factory mismatch, time-optimized trajectory is not the optimal solution, etc., to improve errors, improve The effect of tracking performance
CN110221538BInactive 📅 Publication Date: 2021-10-01SOUTH CHINA UNIV OF TECH +1
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[0095] The object of the present invention will be further described in detail through specific examples below, and the examples cannot be repeated here one by one, but the implementation of the present invention is not limited to the following examples.
[0096] In this embodiment, a time-optimal trajectory planning controller combined with iterative learning, the trajectory planning controller includes a path discrete module, a trajectory planning module, an iterative learning module, and a storage module;
[0097] The path discretization module is used to discretize the task path of the robot;
[0098] The trajectory planning module is used to obtain the time-optimal trajectory. Specifically, according to the constraint conditions, the joint space dynamic model of each discrete point is established, and the joint space dynamic model is transformed into the path space dynamic model and a nonlinear optimization model is constructed. Solve the optimal trajectory of the robot t...
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Abstract
The invention discloses a time-optimal trajectory planning controller and method combined with iterative learning. The controller includes a path discrete module, a trajectory planning module, an iterative learning module, and a storage module; the path discrete module is used to discrete the task path The trajectory planning module obtains the time-optimal trajectory; the iterative learning module is used to compensate the dynamic model error; the storage module stores the iterative data of each iteration. The method includes the following steps: S1, inputting the continuous task path into the path discretization module to discretize the path; S2, inputting the discretized result of the path into the trajectory planning module to obtain the time-optimal trajectory; S3, obtaining feedback on the running trajectory Moment; S4, input the calculation result and the iterative data in the storage module into the iterative learning module, and output the iterative learning compensation item; S5, update the joint space dynamic model; S6, repeat steps S2-step S5 until the dynamic model no longer renew.
Description
technical field [0001] The invention relates to a robot controller and method, in particular to a time-optimal trajectory planning controller and method combined with iterative learning. Background technique [0002] When using robots for handling, assembly, machining, etc., in order to improve the working efficiency of the robot, the robot should always work at the maximum allowable speed. In order to reduce the time for the robot to perform tasks, the robot should move under critical conditions. The trajectory planning method is called the time-optimal trajectory planning method. [0003] When using the dynamic model for optimal trajectory planning, because the dynamic model is not completely accurate, this leads to the inevitable model factory mismatch problem, resulting in the time-optimal trajectory obtained through dynamic constraints is not the optimal solution . Contents of the invention [0004] The purpose of the present invention is to provide a time-optimal t...
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