Adaptive iterative learning control method based on non-strict repetition

An adaptive iterative and learning control technology, applied in the field of iterative learning control systems, can solve problems such as limited research, achieve rapid convergence, reduce the range of jitter, and solve the effects of poor anti-interference ability

Inactive Publication Date: 2018-09-14
JIANGSU INST OF ECONOMIC & TRADE TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although many people have studied control methods based on adaptive and iterative learning, the research on adaptive iterative learning control methods for multiple non-strictly repetitive problems in the controlled system is very limited.

Method used

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  • Adaptive iterative learning control method based on non-strict repetition
  • Adaptive iterative learning control method based on non-strict repetition
  • Adaptive iterative learning control method based on non-strict repetition

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Experimental program
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Effect test

Embodiment 1

[0049] Embodiment 1: In order to solve the problems in the traditional iterative learning control method, when the controlled system is not strictly repeated, the initial state of the system is not strictly positioned, and the tracking trajectory is not strictly repeated, the tracking accuracy is low, the number of iterations is large, and the tracking error vibration range is large By establishing a variety of non-strict repetition constraint models, introducing state-space matrix vectors, and combining the adaptive energy function mechanism, the non-strict repetition is learned, and an adaptive iterative learning control method based on non-strict repetition is invented. The setting function that makes the trajectory automatically track the change has high trajectory tracking accuracy, and the control algorithm has strong robustness at the same time.

[0050] The self-adaptive iterative learning control method based on non-strict repetition of the present invention is general...

Embodiment 2

[0125] Example 2: A more general non-strictly repeatable nonlinear system is as follows:

[0126] x k (t+1)=θ k (t)ξ(x k (t),t)+b(t)u k (t)+d(t)

[0127] Among them, the known function ξ(x k (t),t)=0.1sin(x k (t)); unknown time-varying control gain b(t)=1+sin(0.5t); external disturbance d(t) changes randomly on the interval [0,0.1]; unknown parameter variable θ k (t) The repetition law of the iterative domain can be checked.

[0128] θ k The iterative changes of (t) are as follows:

[0129] θ k (t)=-2cos0.4θ k-1 (t)-θ k-2 (t)

[0130] Among them, the unknown initial state functions are θ -1 (t) = 1.4sin(0.02πt) and θ 0 (t)=0.4cos(0.02πt).

[0131] The initial state of the system x k (0) is a bounded random signal, which changes randomly in the interval (0,1].

[0132] The reference trajectory of system state tracking iterative changes is shown in the following formula:

[0133]

[0134] Among them, r k ∈(0,1], change randomly on the interval (0,1].

[01...

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Abstract

The invention provides an adaptive iterative learning control method based on non-strict repetition which solves the problem of convergence of system trajectory tracking error occurring when multiplenon-strict repetition problems exist in a controlled system. A projection algorithm is used for design of unknown parameter vector estimators, convergence conditions are given and rapid convergence oftrajectory tracking error is realized. Based on the state-space reconstruction technology, the non-strict repetition rule is embedded into the adaptive iterative learning control method, so that thejitter range of tracking error is effectively reduced and the problem of poor anti-interference capability of the conventional projection algorithm is solved.

Description

technical field [0001] The invention relates to an iterative learning control system and method, in particular to an adaptive iterative learning control method based on non-strict repetition. Background technique [0002] The iterative learning control method was first applied to industrial manipulators. It is a control method aimed at repeatable real objects, so it is widely used for its practicability and precise tracking ability. In the traditional iterative learning control method, there is a strong requirement for the strict repeatability of the iterative domain of the controlled system. There are many reasons for this. The most important factor is that when the controlled system cannot be modeled at all, only through Iterative learning can control the system to achieve the tracking goal. At this time, the better the strict repeatability of the iterative domain of the system, the better the control precision of the system. Unfortunately, in practical applications, the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 刘保彬周伟
Owner JIANGSU INST OF ECONOMIC & TRADE TECH
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