Fractional-order iterative learning control method and system for manipulator with initial state learning

Abstract

The invention discloses a mechanical arm fractional order iterative learning control method and system with an initial state learning function. The mechanical arm fractional order iterative learning control method with the initial state learning function comprises the steps that firstly, a kinetic model of a mechanical arm system is established, and an expected movement track of the mechanical arm system is preset; secondly, the initial state of the state quantity of the mechanical arm system and system input of the mechanical arm system are initialized, and the actual movement track of the mechanical arm system is worked out according to the kinetic model of the mechanical arm system; thirdly, whether the tracking error between the actual movement track and the expected movement track is equal to zero or not is judged through calculation, if yes, the actual movement track overlaps with the expected movement track, and the process is ended, and if not, the next step is executed; and fourthly, the initial state of the state quantity of the mechanical arm system is corrected according to the initial state of the tracking error and a set initial state learning gain, system input of the mechanical arm system is corrected according to the tracking error, a set input learning gain, and a fractional order, the actual movement track of the mechanical arm system is worked out accordingly, and then the third step is executed.

Description

technical field [0001] The invention belongs to the field of trajectory tracking control, and in particular relates to a fractional-order iterative learning control method and system of a mechanical arm with initial state learning. Background technique [0002] With the development of science and technology, robots have been widely used in various fields such as aerospace, medical and military, and even daily life, entertainment and education. The initial purpose of making a robot is to use it as an automation device to serve the manufacturing industry. With the increasing maturity of theory and technology, people have put forward more and more requirements for robots. [0003] The robot system is a typical highly nonlinear and strongly coupled dynamical system, and its high-precision control has always been a research hotspot in the field of industrial automation. At present, the precise control methods of the robot manipulator include: variable structure control, sliding...

AI Technical Summary

Problems solved by technology

[0004] The trajectory tracking learning control method can realize the complete tracking of the desired trajectory. However, 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 , 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

Images