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A data-driven adaptive optimization control method and medium for random disturbance system

A random perturbation, data-driven technology, applied in adaptive control, general control system, control/regulation system, etc., can solve problems such as control system performance deterioration, avoid the burden of repeatedly reading and updating control signals, reduce computation amount of effect

Active Publication Date: 2022-06-24
北京理工大学重庆创新中心 +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, in the presence of measurement noise, this approach will undoubtedly degrade the performance of the control system

Method used

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  • A data-driven adaptive optimization control method and medium for random disturbance system
  • A data-driven adaptive optimization control method and medium for random disturbance system
  • A data-driven adaptive optimization control method and medium for random disturbance system

Examples

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Effect test

Embodiment 1

[0122] The invention provides a data-driven adaptive optimization control method of a random disturbance system, which includes a problem description part, a design part of a data-driven optimal state observer, and a different-strategy data-driven ADP control part of the random disturbance system;

[0123] For the problem description section:

[0124] Given a stochastic disturbance system, and obtain the output equation associated with it; solve the optimal linear control, and minimize the cost function through the designed stochastic optimal control strategy;

[0125] For the design part of the data-driven optimal state observer:

[0126] Design data-driven optimal state observers for completely unmeasured system states; obtain state design systems through random disturbance systems, output equations and observers;

[0127] The optimal control strategy of the observed state design system;

[0128] Borrowing the idea of ​​data-driven ADP, a data-driven algorithm is designed ...

Embodiment 2

[0222] Corresponding to the first embodiment, the present invention provides a computer-readable storage medium on which a computer program is stored, and the computer program is executed by a processor to execute the above-described method.

[0223] The present invention may employ a computer program product embodied on one or more storage media (including disk storage, CD-ROM, optical storage) having computer program code embodied therein.

[0224] The present invention is described with reference to methods according to embodiments of the present invention. It will be understood that each process in the flowchart can be implemented by computer program instructions. These computer programs may also be stored in a computer readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory result in an article of manufacture comprising the instruct...

Embodiment 3

[0226] Corresponding to the first embodiment, the present invention provides an application example of the learning mechanism simulation of the central nervous system.

[0227] This embodiment verifies the effect of the above method by simulating an arm motion control experiment of the central nervous system (CNS) under the interference of an external force field. The human object moves the manipulator tip forward to the target position by arm motion in the horizontal plane, such as figure 1 shown. There are two torque motors in the base of the manipulator, which can generate the required force field and apply the corresponding interference force to the arm through the mechanical arm and the handle at the end. used here figure 2 The data-driven Adaptive optimal control (AOC) method shown simulates the learning mechanism of the CNS in this example.

[0228] 1) Simulation settings

[0229] The dynamic characteristics of the system can be described as:

[0230]

[0231] ...

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Abstract

The invention discloses a data-driven adaptive optimization control method and medium for a random disturbance system. The method includes a problem description part, a design part of a data-driven optimal state observer, and a different-strategy data-driven ADP control part of a random disturbance system; for The above three parts, the present invention has been described in detail. The invention implements different-strategy data-driven ADP control of a random disturbance system by designing a data-driven optimal state observer. For the first time, the data-driven ADP method is used for the system whose state is completely unpredictable; the model-free LQG control is extended to the continuous time system; the non-matching noise outside the control signal channel is considered in the ADP design, and the independent state and control signal are not dependent Noise: A new type of different strategy data-driven ADP control method and medium for random disturbance systems is proposed, which avoids the burden of repeatedly reading and updating control signals, and significantly reduces the amount of calculation.

Description

technical field [0001] The invention relates to a random noise disturbance system, in particular to a model-free random optimal control. Random noise disturbance systems are used in many fields such as industrial and agricultural production, power system, chemical technology, machinery manufacturing, transportation, aerospace, artificial intelligence and so on. Background technique [0002] Uncertainty in real systems can come from noise in signals such as input and state. Therefore, the optimal control problem of stochastic noise disturbance system has been paid much attention. In traditional literature, this kind of problem usually adopts H 2 or H ∞ The mainstream implementation method is to adjust the disturbance input with a certain deterministic model, and then design the state feedback control. But in engineering practice, it is often unrealistic to make external disturbances update in the way that people expect. On the other hand, the existing H 2 and H ∞ The r...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 甘明刚马千兆张蒙陈杰窦丽华邓方白永强
Owner 北京理工大学重庆创新中心
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