Methods and apparatus for model predictive control of aircraft gas turbine engines

Claim 3

A method according to claim 1 , wherein the optimizer/controller performs following steps in a loop: (A) estimating the current engine state and applicable constraints; (B) for a given control/simulation time period, determining a control action by optimizing an objective function based on the operation model while respecting the applicable constraints; (C) executing the control action determined in step (B).

Claim 4

A method according to claim 1 further comprising defining an objective J in accordance with:  J = ∑ i = 1 p   ( Y1 i - Y1ref i ) 2 + γ * ∑ i = 1 p   ( Y2 i - Y2ref i ) 2 +  ρ 1 * ∑ i = 1 p   Δ   U   1 i 2 + ρ 2  ∑ i = 1 p  Δ   U   2 i 2 +  δ 1  ∑ i = 1 p  (  ( Out   1 min - Out   1 i ) ) 2 + δ 2  ∑ i = 1 p  (  ( Out   2 i - Out   2 max ) ) 2 + … .

Claim 5

A method in accordance with claim 3 further comprising computing a gradient of J.

Claim 6

A method in accordance with claim 4 further comprising taking steps in a negative gradient direction until J is minimized.

Claim 7

A method in accordance with claim 4 further comprising taking steps in a negative gradient direction until the number of steps equals a predetermined number.

Claim 8

A method in accordance with claim 4 further comprising taking steps in a negative gradient direction until an elapsed time exceeds a predetermined time interval.

Claim 11

A system according to claim 8 , wherein said optimizer/controller is configured to perform the following steps in a loop: (A) estimating the current engine state and applicable constraints; (B) for a given control/simulation time period, determining a control action by optimizing an objective function based on the operation model while respecting the applicable constraints; (C) executing the control action determined in step (B).

Claim 12

A system in accordance with claim 8 wherein said optimizer/controller is configured to compute the gradient of J wherein:  J = ∑ i = 1 p   ( Y1 i - Y1ref i ) 2 + γ * ∑ i = 1 p   ( Y2 i - Y2ref i ) 2 +  ρ 1 * ∑ i = 1 p   Δ   U   1 i 2 + ρ 2  ∑ i = 1 p  Δ   U   2 i 2 +  δ 1  ∑ i = 1 p  (  ( Out   1 min - Out   1 i ) ) 2 + δ 2  ∑ i = 1 p  (  ( Out   2 i - Out   2 max ) ) 2 + … .

Claim 13

A system in accordance with claim 10 wherein said optimizer/controller is configured to traverse the gradient in a negative direction until a number of steps equals a predetermined number.

Claim 14

A system in accordance with claim 10 wherein said optimizer/controller is configured to traverse the gradient in a negative direction until J is minimized.

Claim 15

A system in accordance with claim 10 wherein said optimizer/controller is configured to traverse the gradient in a negative direction until an elapsed time exceeds a predetermined time interval.

Claim 16

A non-linear model-based control method for controlling propulsion in a aircraft gas turbine engine, the method comprising: a) obtaining information about the current state of the engine using an Extended Kalman Filter; b) updating model data information about the engine in an model-based control system to reflect the current state of the engine; c) comparing the information about the current state of the engine with the model data information about the engine in the model; d) determining the optimal corrective action to take given the current state of the engine, the objective function, and the constraints of the engine; e) outputting a control command to implement the optimal corrective action; and f) repeating steps a)-e) as necessary to ensure the performance of the engine is optimized at all times.

Claim 17

A non-linear model-based control method for controlling propulsion in a aircraft gas turbine engine, the method comprising: a) obtaining information about the current state of the engine using an Extended Kalman Filter; b) updating model data information about the engine in an model-based control system to reflect the current state of the engine; c) comparing the information about the current state of the engine with the model data information about the engine in the model; d) determining the optimal corrective action to take given the current state of the engine, the objective function, and the constraints of the engine; e) outputting a control command to implement the optimal corrective action; and f) repeating steps a)-e) as necessary to ensure the performance of the engine is optimized at all times.

Claim 18

A method in accordance with claim 14 wherein said obtaining current information comprises defining an objective J in accordance with:  J = ∑ i = 1 p   ( Y1 i - Y1ref i ) 2 + γ * ∑ i = 1 p   ( Y2 i - Y2ref i ) 2 +  ρ 1 * ∑ i = 1 p   Δ   U   1 i 2 + ρ 2  ∑ i = 1 p  Δ   U   2 i 2 +  δ 1  ∑ i = 1 p  (  ( Out   1 min - Out   1 i ) ) 2 + δ 2  ∑ i = 1 p  (  ( Out   2 i - Out   2 max ) ) 2 + … .

Claim 19

A method in accordance with claim 15 further comprising computing a gradient of J.

Claim 20

A method in accordance with claim 16 further comprising taking steps in a negative gradient direction until J is minimized.

Claim 21

A method in accordance with claim 16 further comprising taking steps in a negative gradient direction until the number of steps equals a predetermined number.

Claim 22

A method in accordance with claim 16 further comprising taking steps in a negative gradient direction until an elapsed time exceeds a predetermined time interval.

Claim 23

A method in accordance with claim 16 further comprising taking steps in a negative gradient direction until a first to occur of: J is minimized; the number of steps equals a predetermined number; and an elapsed time exceeds a predetermined time interval.