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

A random disturbance, data-driven technology, applied in adaptive control, general control system, control/regulation system, etc., can solve problems such as control system performance deterioration

Active Publication Date: 2020-03-13
北京理工大学重庆创新中心 +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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

Examples

Experimental program
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Embodiment 1

[0121] The invention provides a data-driven self-adaptive optimization control method for a random disturbance system, including a problem description part, a data-driven optimal state observer design part, and a different-strategy data-driven ADP control part for the random disturbance system;

[0122] For the problem description part:

[0123] Given a random perturbation system, and obtain the associated output equation; solve the optimal linear control, and minimize the cost function through the designed stochastic optimal control strategy;

[0124] For the design part of the data-driven optimal state observer:

[0125] For completely unmeasurable system states, design data-driven optimal state observers; obtain state design systems through random perturbation systems, output equations, and observers;

[0126] Design the optimal control strategy of the system based on the observed state;

[0127] Borrow the idea of ​​data-driven ADP, design a data-driven algorithm on the ...

Embodiment 2

[0222] Corresponding to Embodiment 1, the present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program is used by a processor to execute the method described above.

[0223] The invention can take the form of a computer program product embodied on one or more storage media (including magnetic disk storage, CD-ROM, optical storage) having computer program code embodied therein.

[0224] The invention is described with reference to methods according to embodiments of the invention. It should be understood that each procedure in the flowcharts 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 operate in a specific manner such that instructions stored in the computer readable memory produce an article of manufacture comprising instruction means.

Embodiment 3

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

[0227] This embodiment verifies the effect of the above method by simulating the arm movement control experiment of the central nervous system (CNS) under the interference of an external force field. The human object moves the end of the manipulator forward to the target position through the arm movement 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 disturbance 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 instance.

[0228] 1) Simulation settings

[0229] The dynamic characteristics of the system can be describe...

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PUM

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Abstract

The invention discloses a data-driven adaptive optimization control method for a random disturbance system and a medium. The method comprises a problem description part, a design part of a data-drivenoptimal state observer, and an off-policy data-driven ADP control part of a random disturbance system. The three parts are explained in detail in the invention. A data-driven optimal state observer is designed to carry out off-policy data-driven ADP control on a random disturbance system. The data-driven ADP method is used in a system with a completely unmeasurable state for the first time. Model-free LQG control is generalized to a continuous time system. In the ADP design, non-matching noise except a control signal channel and independent noise independent of the state and the control signal are considered. By using the novel off-policy data-driven ADP control method for a random disturbance system and the medium provided by the invention, the burden of repeatedly reading and updating acontrol signal is avoided, and the computation is remarkably reduced.

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 systems, chemical processes, machinery manufacturing, transportation, aerospace, artificial intelligence, etc. Background technique [0002] Uncertainties in real systems may come from noise in signals such as inputs and states. Therefore, the optimal control problem of random noise disturbance system has been paid much attention. In the 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 people expect. On the other hand, the existing H 2 and H ∞ Most of the...

Claims

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Application Information

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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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