Murat Arcak, John Wen Electrical, Computer, and

Systems Engineering

Design, Optimization, and Control for Multiscale Systems

Prabhat Hajela, Achille Messac Mechanical, Aerospace, and

Nuclear Engineering

Roger Ghanem Civil Engineering

Johns Hopkins University

Attributes of Multiscale System Design

• Complex dynamics (large # of DOF, nonlinear) with multiple descriptions for different system behaviors and properties

• Intensive computation requirement for high fidelity simulation

• Identification/calibration requirement for model parameters

• Multiple design objectives and constraints

• Static and dynamically adjustable design parameters

Example

• Integrated control/structure design for electronic manufacturing:

objective: rapid motion with minimal vibration

model: FEM structural model

static design parameters: head inertia/geometry, sensor/actuator type and location, motion profile

dynamically adjustable parameters: actuator output

constraints: torque, velocity, acceleration, temperature, and cost

Current practice/limitation: FEM guided mechanical design, heuristic sensor/actuator selection and placement, control design based on empirical model

Example

• Nanocomposite:

objective: produce materials with specified mechanical, electrical, optical properties

model: multibody model with many polymer chains interacting with nanospheres and one another.

static design parameters: binding material on nanosphere

dynamically adjustable parameters: temperature, pressure, mixing rate

constraints: types of material, actuator limitation

Current practice/limitation: trial and error recipe, intensive model computation (decoupled from design)

MSERC Approach

Dynamical Process

Modeling Identification

Optimization Control

A design methodology integrating modeling, identification, optimization and control

Model Reduction/Identification

• Key technology in large scale system simulation and design, e.g., electromagnetics, structural systems, VLSI circuits, fluid dynamics etc.

• Motivation: wider and faster exploration of design space, lower order on-line estimator and controller, model validation/calibration

• Approximation of high order analytical model by a lower order model or fitting input/output data to parameterized model: an interpolation problem. key issues: parameterization, distance metric, error bound, property-preserving (gain, dissipativity, energy conservation), measurement noise.

quantitative trade-off between model order, error bound, computation time not well developed, especially for nonlinear dynamical systems

Modeling Engine

modeling engine maintains, updates, and provides physics-based and data-driven models based on computation efficiency, accuracy, resolution, parameterization requirements.

analytical models

modeling engine

physical system

simulation

design optimization

process optimization

real-time control

on-line diagnosticsprobing to reduce

uncertaintyphysical data

Multi-Disciplinary Optimization (MDO)

• Multiscale system design involves distinct but coupled subsystems with large number of design parameters, constraints, and performance metrics – multidisciplinary formulation with multiple objectives, constraints, models.

• In addition to system design and process optimization, optimization is also needed for model reduction and identification, and real-time controller and estimator design

• Key issues: surrogate model for efficient search, uncertainty modeling and management, imprecise problem formulation, machine learning

Active research area: optimization in the presence of uncertainty – in underlying models, in performance objectives, in system constraints.

Optimization Engine

Robust, simulation-based exploration of design space, batch and on-line optimization and diagnosis, based on models and error bounds provided by the modeling engine.

modeling engine

optimization engine

physical system

simulation

design optimization real-time

control

process optimization

on-line diagnostics

processing parameters measurement data

model predictive control

optimal estimator

simulation based design exploration

learning based

incorporation of control objectives

On-line Estimation and Control

• Multiscale systems are complex nonlinear dynamical systems with multiple inputs/outputs. Usual approach: linearization about operating point and treat linearization error as uncertainty -- most control design tools are for linear systems (robust control).

• Nonlinear estimation and control: exploit system structure rather than canceling or ignoring it.

• Broader consideration: system design including control objectives, actuator/sensor selection/placement

low order models needed for real-time implementation

trade-off between achievable performance and model uncertainty

Dynamic Control and Estimation

Robust control and estimation algorithms that apply nonlinear model identification and reduction and incorporates model error estimates.

modeling engine

control & estimation

physical system

optimization engine

real-time control

on-line diagnosticsactuator sensor

nonlinear model

identification & reduction

optimization with closed loop objectives

Research Goals

• Developing on-demand model generation based on physical data, analytical models with tunable parameterization, error metric, error bound, size/order, communication overhead, and active probing to reduce model uncertainty

• Establishing integrated design methodology based on simulation driven multidisciplinary optimization, using gradient and evolutionary methods, taking into account imprecise problem formulation, model uncertainty, error management, computation cost, system dynamics, noise.

• Identifying fundamental limits on performance and robustness of multiscale systems based on static and dynamic optimization.

Linkage to Other Technology Components in MSERC

• optimization tools applied to model reduction and identification

• data-driven model can be used to augment physics-based model

• fast simulation speeds up parameter space sampling in design iteration

• error estimate useful in optimization and control

develop common integrated tools and tailor them to specific applications

physics based modeling provides parameterized model and computation tool