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Non-linear PID adaptive control method based on tracking differentiator

A tracking differentiator, adaptive control technology, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve problems such as large amount of calculation

Active Publication Date: 2015-10-21
HUNAN UNIV OF TECH
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Problems solved by technology

This method dilutes the limitations of the unmodeled dynamic global bounded conditions, but the required conditions are limited to the unmodeled dynamics of linear growth, and uses the method of fuzzy neural network inference system and one-to-one mapping to estimate the unmodeled Modeling dynamics, which is computationally intensive

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  • Non-linear PID adaptive control method based on tracking differentiator

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example

[0133] In order to verify the effectiveness of the control method of the present invention, two nonlinear objects given in the prior art are taken as examples for comparative analysis. In the simulation experiment, the relevant parameters of the two tracking differentiators are set as follows:

[0134] Take h=0.1, fast factor r=5, filter factor h 0 =5h, then there are:

[0135] TD 1 : f h = f h a n ( v ...

Embodiment 1

[0137] Embodiment 1. If the uncertain high-order nonlinear term v[X(k)] is bounded, use the discrete zero dynamic unstable nonlinear system as shown below to carry out the simulation experiment:

[0138] y ( k + 1 ) = 2.6 y ( k ) - 1.2 y ( k - 1 ) + u ( k ) ...

Embodiment 2

[0140] Embodiment 2. When the uncertain high-order nonlinear term v [X (k)] satisfies the linear growth condition, in order to verify the effectiveness of the control method of the present invention, consider the following discrete zero dynamic unstable nonlinear system as:

[0141] y(k+1)=2.6y(k)-1.2y(k-1)+u(k)+1.2u(k-1)+ (25)

[0142] 0.5y(k)sin[u(k)+u(k-1)+y(k)+y(k-1)]

[0143] The simulation results using the control method of the present invention are shown in Figure 5, and Figure 6 shows the control performance of the prior art using the base ANFIS and the multi-model switching system. It can be seen from the simulation results that, compared with the control method in the prior art, the control method of the present invention can accurately track the reference signal, not only does not have overshoot and oscillation, but also significantly reduces the input amplitude.

[0144] The non-linear PID adaptive control method based on the tracking differentiator provided by t...

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Abstract

The invention discloses a non-linear PID adaptive control method based on a tracking differentiator. The method comprises the following steps of setting up the tracking differentiator TD; establishing a non-linear PID (NLPID) control law; and setting up an adaptive learning algorithm of an NLPID weight coefficient. The non-linear PID adaptive control method based on the tracking differentiator has great stability and convergence. Furthermore, the method which can accurately track reference signals achieves no overstrike and no oscillation and has strong robustness stability. Furthermore, the method has the characteristics of simple controller structure, clear theory model, no dependence on a controlled object model and low computational complexity. Therefore, the method is especially suitably used for real-time control on unknown objects or objects which are hard to model.

Description

technical field [0001] The invention specifically relates to a nonlinear PID adaptive control method based on a tracking differentiator. Background technique [0002] Aiming at the control problem of nonlinear system uncertainty, a kind of asymptotically stable nonlinear control is realized by using an adaptive control method combining neural network and multi-model; a generalized predictive adaptive control method is proposed to realize zero dynamic Unstable nonlinear control methods. The common feature of the above methods is that such nonlinear systems are expressed as the unmodeled dynamics of higher-order nonlinear terms and a linear system, and it is assumed that the unmodeled dynamics are known to be globally bounded; although the unmodeled dynamics do not require Globally bounded, but the unmodeled dynamic rate of change needs to be known to be globally bounded, a multivariable nonlinear adaptive control method based on multi-model and neural network is proposed; fo...

Claims

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Application Information

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IPC IPC(8): G05B13/02
Inventor 于惠钧柳云山
Owner HUNAN UNIV OF TECH
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