Robot adaptive iterative learning control method and system

Abstract

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.

Description

technical field [0001] The invention relates to the technical field of robot control, in particular to robot position and speed control technology, in particular to a robot self-adaptive iterative learning control system. Background technique [0002] Industrial robots can replace humans to work in industrial environments, complete monotonous, heavy, repetitive long-term operations, effectively reduce human labor intensity, improve production efficiency, and are widely used in welding, spraying, polishing, handling and palletizing Wait for homework. [0003] With the rapid development of modern industry, a higher level of product quality is required, which requires higher and higher requirements for the trajectory tracking technology of industrial robots. In actual industrial production, repeating the same trajectory is a common working mode of industrial robots. Due to the characteristics of repeating the same trajectory, the error of trajectory tracking will accumulate as...

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Problems solved by technology

However, the introduction of the forgetting factor will make the output error of the system not close to zero, but can only converge to a certain neighborhood of zero

Literature [Pang Bo, Shao Cheng. Iterative learning control algorithm for high-order parameter optimization [J]. Control Theory and Application, 2015 (04): 144-150.] proposed a high-order parameter optimization iterative learning control algorithm, using high-order learning The rate can also speed up the convergence speed of the algorithm. However, due to the high-order learning rate involved, the algorithm uses the information of previous iterations, and the process of the algorithm is more complicated.

Literature [Zhang Tie, Li Changda, Qin Binbin, et al. Adaptive iterative learning trajectory tracking control of SCARA robot [J]. China Mechanical Engineering, 2018, 494(14): 90-95.] proposed an adaptive iterative learning The trajectory tracking algorithm overcomes the uncertainty caused by the unknown parameters of the robot through adaptive iterative items, but the convergence speed of the algorithm needs to be improved

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