MPC course materials

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Model Predictive Control Dr.Ir. Ton van den
Boom Prof.Dr.Ir. Ton Backx
October 27, 2003
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Model Predictive Control

• Fourth lecture
• Introduction on Model Predictive Control and
  Industrial relevance of Model Predictive Control
• Models and model characteristics

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Model Predictive Control

• Model Predictive Control technology has its roots
  in industry. The model predictive control concept
  development started at the end of the sixties
• Dynamic Matrix Control (Shell, Charlie Cutler et
  al, 1980)
• IDCOM (Jacques Richalet, 1978)
• Quadratic Dynamic Matrix Control (Shell, Mike
  Morshedi et al, 1984)
• Shell Multivariable Optimizing Control (Shell,
  1985)
• IDCOM-M (Setpoint, 1986)
• Setpoint Multivariable Control Architecture
  (Setpoint, 1994)
• Robust Multivariable Process Control Technology
  (Honeywell, 1995)
• Ipcos Novel Control Architecture (IPCOS, 1999)

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control

• Model Predictive Control (MPC) technology is most
  widely applied in Oil Refining and Petrochemical
  industry applications today
• The application objectives are
• Maximization of throughput
• Operation within permitted operating constraints
• Pushing for best economic operating conditions
• Interface between steady state, first principle
  model based optimization and primary process
  control

• Introduction on Model Predictive Control
• Models and model characteristics

5
Model Predictive Control

• Most of todays industrial applications of MPC
  are primarily focussing on quasi steady state
  behavior of processes
• Compensation of low frequency components of
  disturbances only due to low bandwidth
• Low bandwidth controller tuning for robustness
  reasons
• Mostly a single, linear, non-parametric dynamic
  model to describe process behavior for the
  complete operating range

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control

• Use of the MPC technology in most Chemical and
  other Processing Industry applications requires
  more attention for control system performance
• 6-sigma quality of specified product parameters
  and critical process conditions
• predictable and reproducible transition between
  different operating conditions
• market situation based process optimization and
  transitions between product grades/product types
• time critical operation as one step in a supply
  chain

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control

• Poor capital productivity -i.e. the money
  generated with the invested capital- is a major
  problem in most of the chemical processing, pulp
  paper, glass manufacturing, steel production
  and several other processing industries
• Global competition resulting in strangling
  pressure on prices and thus margins
• World-wide saturation of markets leading to price
  pressure and need for innovation
• Tightening legislation on ecosphere load and
  resource consumption resulting in increasing
  complexity of processes and corresponding growing
  difficulties with process operation

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control

• Todays way of supply driven operation of
  production is one of the causes of the poor
  performance of the processing industries
• Focus on increase of scale
• Focus on reproducibility
• Minimization of number of product types
• Fixed grade slates, fixed recipe driven
  changeovers
• ? Comparable situation to the Automotive and
  Consumer Electronics Industries in the seventies
• Part of the answer is demand driven production
• This requires completely integrated high
  performance technologies for process unit control
  and plant optimization

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control

• A constrained market situation asks for a demand
  or market driven mode of process operation
• Increase flexibility in processing of a broad
  range of feedstock materials
• Produce products that have market demand
• Take price advantage of a scarce market
• Minimize capital blocked in stored products and
  intermediates
• Increase capital turnaround by shortening
  production-to-product delivery cycles
• Each of the above effects directly contributes to
  the required increase of the capital productivity

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control

• Capital productivity of a company is determined
  by the following major factors
• The profit made on each (quantity) of product
  being sold
• The volume of product sold
• The amount of money required for realizing this
  volume of production
• Time appears to be a crucial and very sensitive
  factor in actual realized capital productivity
  The shorter the time interval between buy of
  feedstock material and delivery of final product
  the more frequent (even a small) margin can be
  realized

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control
Reduction of the order to delivery cycle
significantly increases capital productivity
mimp improved margin m margin per
cycle d margin improvement s speedup factor of
order to delivery cycle

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control

• Realization of demand driven operation of
  production processes requires new technologies
  that enable
• Flexible operation of plants over broad operating
  ranges at minimum costs
• dynamic optimization
• Tight production at pre-specified Cpk values to
  achieve quality constraints
• high performance (model based) control systems
  that enable significant reduction of variance of
  critical process/product variables
• Overall optimization of economic performance
• integration of optimization of operation with
  (model based) control
• Extensive (re-)use of available a-priori
  knowledge to minimize total application costs and
  to enable economic feasibility

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control
Market driven objectives
Classical Steady-state model based
optimizationQuasi-steady state MPClocal
validity of MPC
NewTechnologyDynamic model based trajectory
optimizationHigh Performance MPCTrajectory
tracking MPC
Plant-Wide Model Based Optimizer
Plant-Wide Model
Optimal Process Conditions
Model Predictive Control
Model Predictive Control
Unit Model
Model Predictive Control
Unit Model
Unit Model
Optimal Reference Signals

• Introduction on Model Predictive Control
• Models and model characteristics

DCS
DCS
DCS
Primary Control Signals
Process
Process
Process
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Model Predictive Control
Model Predictive Control system

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control
Prioritised Optimisation
Fulfill operational requirements
Satisfy MV constraints Satisfy Priority 1 zones
and targets, if not possible balance between all
priority 1 requirements Satisfy priority i zones
and targets, if not possible balance between all
priority i requirements Bring CVs as close as
still possible to optimal values Bring MVs as
close as still possible to optimal values
Fulfill quality requirements

• Introduction on Model Predictive Control
• Models and model characteristics

Fulfill economic performance conditions
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Model Predictive Control

• High performance operation of processes requires
  a tight integration of optimization and control

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control

• Models are the heart of a model predictive
  control system models are used for
• Prediction of future process output behavior on
  the basis of known past input signals, known past
  disturbances and expected future disturbances
• Calculation of the best future process
  manipulations on the basis of a given criterion
  function and specifications for the controlled
  variables

• Introduction on Model Predictive Control
• Models and model characteristics

Achievable performance and robustness of the
model predictive control system are governed by
model accuracy
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Model Predictive Control

• Essentially two type of modeling techniques are
  applied today for modeling the dynamic behavior
  of processes
• First principles based modeling
• modeling of known main process mechanisms
  covering a broad operating envelope on the basis
  of first principles (mass, energy and momentum
  balances)
• empirical estimates of physical properties,
  reaction kinetics, reaction complex, ...
• Empirical modeling
• modeling of observed process behavior in response
  to test signals and/or operator/disturbance
  invoked process excitations
• test signal content determines model validity and
  coverage of operating envelope

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control
First Principle Modeling

• Lack of detailed knowledge of all process
  mechanisms and their parameter values that
  contribute to relevant process input/output
  behavior makes it difficult to develop purely
  first principles based models for high
  performance MPC
• No methodology resulting in necessary accuracy of
  the model for high performance control
• Lack of structured model design methods to
  analyze and assure inclusion of all relevant
  mechanisms
• Lack of methods that enable choosing required
  model granularity and that enable appropriate
  model reduction
• Lack of methods for validation of first principle
  models in a structured way

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control
Empirical Modeling

• Empirical process models only reflect behavior
  observed during testing. The models only have
  limited validity and only cover a limited part of
  the entire operating envelope of the process
• Limitations in describing wide ranges in process
  dynamics (slowest vs. fastest time constants)
• Limitations in covering large gain ranges over
  various directions in input and output spaces
• Limited capability in accurately describing
  non-linear process dynamics for a broad range of
  input trajectories covering the full allowed
  input space
• Interpolation and especially extrapolation beyond
  observed trajectories is risky and mostly results
  in large modeling errors

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control

• In industrial practice processes are showing
  three types of dynamic characteristics with much
  potential for performance improvement, if they
  can be exploited
• Broad frequency range covered by various process
  mechanisms and accessible for process operation
  and disturbance compensation
• Large differences in gain for various
  input/output directions in multivariable
  processes
• Non-linear process behavior both for steady state
  as well as for transfer dynamics

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control
Directionality of process transfer

• Introduction on Model Predictive Control
• Models and model characteristics

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Model Predictive Control
Controller Robustness
Controller performance

• Introduction on Model Predictive Control
• Models and model characteristics

Model inaccuracy
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Model Predictive Control

• Model accuracy, controller performance and
  controller robustness need to be selected in line
  with the imposed performance specifications over
  the entire relevant operating range
• steady state gain errors of the model generally
  result in slow responses towards targeted steady
  state
• high frequency errors of the model may result in
  instabilities of the controller, if performance
  is pushed
• tuning of the control system to cope with model
  inaccuracies results in stable but sluggish
  control with poor disturbance rejection

• Introduction on Model Predictive Control
• Models and model characteristics

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Relation between past and future
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Dynamic trajectory optimization
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