A generalized predictive controller design method in a network control system
A network control system and generalized prediction technology, which is applied in the field of generalized predictive controller design, can solve the problems of being unable to compensate for the influence of noise, without noise information, etc., and achieve the effect of simple structure, low cost and strong portability
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Embodiment 1
[0063] Embodiment 1: the controlled autoregressive integral sliding average model of the controlled object is:
[0064] y(t)-2y(t-1)+1.1y(t-2)=Δu(t-1)+2Δu(t-2)+x(t)+0.5x(t-1)
[0065] The objective function established in the generalized predictive control algorithm is:
[0066] the y r (t+j)=αy r (t+j-1)+(1-α)ω-x(t)-0.5x(t-1)
[0067] In this embodiment, when the noise parameter C is a 1×n-dimensional matrix type, and the first item c 1 is the maximum value, then it is determined that the noise received by the controlled object is colored noise.
[0068] According to the objective function, it can be obtained from figure 2 Obtain the tracking situation for the following situation schematic diagram of the network control system.
Embodiment 2
[0069] Embodiment 2: the controlled autoregressive integral sliding average model of the controlled object is:
[0070] y(t)-2y(t-1)+1.1y(t-2)=Δu(t-1)+2Δu(t-2)+x(t)+1.5x(t-1)
[0071] The objective function established in the generalized predictive control algorithm is:
[0072] the y r (t+j)=αy r (t+j-1)+(1-α)ω-1.5x(t)-1.5x(t-1)
[0073] In this embodiment, when the noise parameter C is a 1×n-dimensional matrix type, and the first item c 1 If is not the maximum value, it is determined that the noise received by the controlled object is colored noise.
[0074] According to the objective function, it can be obtained from image 3 Obtain the tracking situation for the following situation schematic diagram of the network control system.
Embodiment 3
[0075] Embodiment 3: the controlled autoregressive integral sliding average model of the controlled object is:
[0076] y(t)-2y(t-1)+1.1y(t-2)=Δu(t-1)+2Δu(t-2)+x(t)
[0077] The objective function in the generalized predictive control algorithm is: y r (t+j)=αy r (t+j-1)+(1-α)ω-x(t)
[0078] In this embodiment, when the noise parameter C is a 1×1-dimensional matrix type, it is determined that the noise received by the controlled object is white noise.
[0079] According to the objective function, it can be obtained from Figure 4 Obtain the tracking situation for the following situation schematic diagram of the network control system.
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