Systems and methods for using nonlinear model predictive control (mpc) for autonomous systems
US-2020089229-A1 · Mar 19, 2020 · US
US12117782B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-12117782-B2 |
| Application number | US-202418430513-A |
| Country | US |
| Kind code | B2 |
| Filing date | Feb 1, 2024 |
| Priority date | Aug 22, 2022 |
| Publication date | Oct 15, 2024 |
| Grant date | Oct 15, 2024 |
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A linear motor motion control method, device, equipment and storage medium. The proposed invention uses an extended state observer constructed in advance based on the model parameters of the linear motor, and performs state estimation based on the total control amount of the linear motor and the actual displacement signal to obtain each observation signal. The phase advance controller is used to improve the estimated lag of the disturbance observation signal, and a model prediction controller built in advance based on the mathematical model of the linear motor is used to perform rolling optimization based on each observation signal to obtain the optimal control amount increment; The total control amount is updated based on the improved disturbance observation signal and the optimal control amount increment, and the corresponding drive signal is output to the drive end of the linear motor based on the updated total control amount.
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We claim: 1. A linear motor motion control method, wherein, it comprises: a displacement planning signal and an actual displacement signal of a linear motor are obtained; an extended state observer constructed in advance based on model parameters of the linear motor is used to perform state estimation based on a total control amount of the linear motor and the actual displacement signal; and a corresponding displacement observation signal, a speed observation signal and a disturbance observation signal are obtained; a pre-constructed phase advance controller is used to improve an estimated lag of the disturbance observation signal and obtain an improved disturbance observation signal; a model prediction controller constructed in advance based on a mathematical model of the linear motor is used to perform rolling optimization based on the displacement planning signal, the displacement observation signal and the speed observation signal to obtain an optimal control amount increment; the total control amount is updated based on the improved disturbance observation signal and the optimal control amount increment, and a corresponding drive signal is output to a drive end of the linear motor based on an updated total control amount; this achieves a motion control of the linear motor; a prediction model used by the model prediction controller is: Y p ( k )= P x ΔX ( k )+ Iy ( k )+ P u ΔU ( k ) in the formula, k is a current time value, Y p (k) is a predicted displacement output value sequence (Y p (k)=[y(k+1|k) y(k+2|k) . . . y(k+p|k)] T p×1 ), the superscript T indicates transposition, y(k+i|k) is a displacement output value in a future time k+i predicted at a current time k(i=1, 2, . . . , p), P x is a system matrix (P x =[CAΣ x=1 2 CA x . . . Σ x=1 p CA x ] T p×2 ), I is an unit matrix (I=[ 1 1 . . . 1 ] T p×1 ), P u is a control matrix ( P u = [ CB 0 0 ⋯ 0 ∑ x = 1 2 CA x - 1 B CB 0 ⋯ 0 ⋮ ⋮ ⋮ ⋱ ⋮ ∑ x = 1 m CA x - 1 B ∑ x = 1 m - 1 CA x - 1 B ⋯ ⋯ CB ⋮ ⋮ ⋮ ⋱ ⋮ ∑ x = 1 p CA x - 1 B
Linear motors · CPC title
using a predictor · CPC title
in which a parameter or coefficient is automatically adjusted to optimise the performance · CPC title
Observer control, e.g. using Luenberger observers or Kalman filters · CPC title
Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control · CPC title
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