Adaptive-predictive control method and adaptive-predictive control system

Claim 1

A method for generating a control vector during each of a plurality of sampling instants k, said control vector being applied to an apparatus which carries out a process having at least one input variable and at least one output variable, at least one of said input variables defining a process input vector, said apparatus varying said process input vector in accordance with the value of said control vector, said method comprising the steps of storing a model comprising data for predicting a dynamic value of a process output vector, which vector is composed of at least one of said process output variables at predetermined future sampling instants, generating a desired dynamic process output vector at each of said sampling instants k, said desired dynamic process output vector being representative of a desired value of said process output vector at said predetermined future sampling instants, and generating, at each of said sampling instants k, said control vector which said model predicts will cause said dynamic process output vector to be equal to said desired dynamic process output vector, characterized in that the model comprises data for predicting the trajectory of the dynamic process output vector between future sampling instants k+r+1+λ,λ being a positive integer or equal to zero, as a function of a future sequence of said control vectors between sampling instants k and k+λ, in that at each of said sampling instants k a desired dynamic process output trajectory is selected between sampling instants k+r+1 and k+r+1+\ , said desired dynamic process output trajectory being equal to that said specific process output trajectory between sampling instants k+r+1 and k+r+1+λ, which said model predicts would be caused by a specific future control sequence between sampling instants k and k+ X, and such that said specific process output trajectory and said specific future control sequence optimize a chose performance criterion in which a future reference trajectory is explicitly considered, said reference trajectory being periodically redefined as a function of the previously measured process output and evolving according to desired dynamics to the set point; and in that the control vector generated at each of said sampling instants k causes said dynamic process output vector at sampling instants k+r+1 to be equal to the value of said desired dynamic process output trajectory at said sampling instant k+r+1, such that said control vector is equal to the value at sampling instant k of said specific control vector sequence and optimizes said chosen performance criterion.

Claim 2

A method for generating a control vector during each of a plurality of sampling instants k, said control vector being applied to an apparatus which carries out a process having at least one input variable and at least one output variable, at least one of said input variables defining a process input vector, said apparatus varying said process input vector in accordance with the value of said control vector, said method comprising the steps of storing a model comprising data for predicting a dynamic value of a process output vector, which vector is composed of at least one of said process output variables at predetermined future sampling instants, generating a desired dynamic process output vector at each of said sampling instants k, said desired dynamic process output vector being representative of a desired value of said process output vector at said predetermined future sampling instants, and generating, at each of said sampling instants k, said control vector which said model predicts will cause said dynamic process output vector to be equal to said desired dynamic process output vector, characterized in that the model comprises data for predicting the trajectory of the dynamic process output vector between future sampling instants k+r+1+λ,λ being a positive integer or equal to zero, as a function of a future sequence of said control vectors between sampling instants k and k+λ, in that at each of said sampling instants k a desired dynamic process output trajectory is selected between sampling instants k+r+1 and k+r+1+\ , said desired dynamic process output trajectory being equal to that said specific process output trajectory between sampling instants k+r+1 and k+r+1+λ, which said model predicts would be caused by a specific future control sequence between sampling instants k and k+ X, and such that said specific process output trajectory and said specific future control sequence optimize a chose performance criterion in which a future reference trajectory is explicitly considered, said reference trajectory being periodically redefined as a function of the previously measured process output and evolving according to desired dynamics to the set point; and in that the control vector generated at each of said sampling instants k causes said dynamic process output vector at sampling instants k+r+1 to be equal to the value of said desired dynamic process output trajectory at said sampling instant k+r+1, such that said control vector is equal to the value at sampling instant k of said specific control vector sequence and optimizes said chosen performance criterion.

Claim 4

The method of claims 1 or 2, wherein said chosen performance criterion is characterized by an index, and the optimization of said chosen performance criterion is obtained by minimizing the value of said index, which index may vary as a function of said reference trajectory, the setpoint, tha measured and predicted process output and input sequences the control constraints and any other parameter or variables that may influence the control performance.

Claim 7

The method of claims 2, 3, 4 or 5,-further including the step of generating an incremental vector indicative of the difference between the actual value of said one of said control, output or measurable disturbance vectors at said given sampling instant and the actual value of said one of said control, output or measurable disturbance vectors at an earlier sampling instant; wherein said estimation vector is the value of said incremental vector estimated by said model as updated at said prior sampling instant; and wherein said step of generating an estimated error vector comprises the step of determining the difference between the value of said incremental vector and the value of said estimation vector.

Claim 9

A system for generating a control vector during each of a plurality of sampling instants k, said control vector to be applied to an apparatus which carries out a process having at least one input variable and at least one output variable, at least one of said input variables defining a process input vector, said apparatus varying said process input vector in accordance with the value of said control vector, said system comprising: (A) means for storing a model which is capable of predicting the trajectory of a dynamic process output vector, which vector is composed of at least one of said process output variables, between future sampling instants k+r+1 and k+r+l+x, λ being a positive integer or equal to zero, as a function of a future sequence of said control vector between sampling instants k and k+X; (B) means for selecting, at each of said sampling instants k, a desired dynamic process output trajectory between sampling instants k+r+1 and k+r+l+X, said desired dynamic process output trajectory being equal to a specific process output trajectory between sampling instants k+r+1 and k+r+1+λ, that said model predicts would be caused by a spe- σ-cific future control sequence between sampling instants k and k+λ, and such that said specific process output trajectory and said specific future control sequence optimize a chosen performance criterion in which a future reference trajectory is explicitly considered, said reference trajectory being periodically redefined as a function of the previously measured process output and evolving according to desired dynamics to the set point; and (C) means for generating, at each of said sampling instants k, that control vector which said model predicts will cause said dynamic process output vector at sampling instant k+r+1 to be equal to the value of said desired dynamic process output trajectory at said sampling instant k+r+1, such that said control vector is equal to the value at sampling instant k of said specific control vector sequence and optimizes said chosen performance criterion.

Claim 10

A system for generating a control vector during each of a plurality of sampling instants k, said control vector to be applied to an apparatus which carries out a process having at least one input variable and at least one output variable, at least one of said input variables defining a process input vector, said apparatus varying said process input vector in accordance with the value of said control vector, said system comprising: (A) means for storing a model which is capable of predicting the trajectory of a dynamic process output vector, which vector is composed of at least one of said process output variables, between future sampling instants k+r+1 and k+r+l+x, λ being a positive integer or equal to zero, as a function of a future sequence of said control vector between sampling instants k and k+X; (B) means for selecting, at each of said sampling instants k, a desired dynamic process output trajectory between sampling instants k+r+1 and k+r+l+X, said desired dynamic process output trajectory being equal to a specific process output trajectory between sampling instants k+r+1 and k+r+1+λ, that said model predicts would be caused by a spe- σ-cific future control sequence between sampling instants k and k+λ, and such that said specific process output trajectory and said specific future control sequence optimize a chosen performance criterion in which a future reference trajectory is explicitly considered, said reference trajectory being periodically redefined as a function of the previously measured process output and evolving according to desired dynamics to the set point; and (C) means for generating, at each of said sampling instants k, that control vector which said model predicts will cause said dynamic process output vector at sampling instant k+r+1 to be equal to the value of said desired dynamic process output trajectory at said sampling instant k+r+1, such that said control vector is equal to the value at sampling instant k of said specific control vector sequence and optimizes said chosen performance criterion.