Abstract
In order to solve the problems of repetitive and non-repetitive interference in the workflow of Automated Guided Vehicle (AGV), Iterative Learning Control (ILC) combined with linear extended state observer (LESO) is utilized to improve the control accuracy of AGV drive motor. Considering the working conditions of AGV, the load characteristics of the drive motor are analyzed with which the mathematical model of motor system is established. Then the third-order extended state space equations of the system approximate model is obtained, in which LESO is designed to estimate the system states and the total disturbance. For the repeatability of AGV workflow, ILC is designed to improve the control accuracy. As the goods mass transported each time is not same, the LESO is utilized to estimate the non-repetitive load disturbance in real time and compensate the disturbance of the system to improve the position precision. The convergence of the combined algorithm is also verified. Simulation and experimental results show that the proposed iterative learning control strategy based on LESO can reduce the positioning error in AGV workflow and improve the system performance.
Highlights
In order to shorten the processing cycle and reduce production costs, it is necessary to develop intelligent logistics equipment vigorously [1]
The operation of automated guided vehicle (AGV) is full of a lot of repetitive information but it is not used, and there are some restrictions on the use of iterative learning control, So an Iterative Learning Control (ILC) strategy based on linear extended state observer (LESO) is designed for this situation
The inner loop model of AGV drive servo system is used as the controlled object, and the outer loop position controller adopts iterative learning control combined with active disturbance rejection technology to improve the accuracy of the AGV’s repeated operation and enhance the anti-interference against non-repetitive disturbances ability
Summary
In order to shorten the processing cycle and reduce production costs, it is necessary to develop intelligent logistics equipment vigorously [1]. In reference [7], ILC can’t achieve ideal control effect in incomplete repetitive scenarios. To solve this problem, ILC and disturbance observer (DOB) are combined to compensate non-repetitive disturbances in repetitive tasks. In order to reduce the noise and other disturbances in the feedback channel, a control strategy combining iterative learning with FIR filter is proposed in reference [10], which improves the anti-interference ability of the system. The operation of AGV is full of a lot of repetitive information but it is not used, and there are some restrictions on the use of iterative learning control, So an ILC strategy based on LESO is designed for this situation. Simulations and experiments show that this control strategy can effectively compensate for the repetitive and non-repetitive interference in AGV drive servo system
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