Raktim Bhattacharya AEROSPACE ENGINEERING Robust Real-time Control Systems Reliability through...

Raktim Bhattacharya AEROSPACE ENGINEERING

Robust Real-time Control SystemsReliability through algorithm design, execution and system engineering

Department of Aerospace Engineering

H.R. Bright Building, Rm. 701, Ross Street - TAMU 3141

College Station TX 77843-3141

Raktim BhattacharyaAssistant Professor

[email protected]


• Role of control algorithms is changing

Dynamic OnlineDynamic OnlineStatic OfflineStatic Offline

Paradigm Shift in Design and Implementation of Control Systems From static offline designs to dynamic online systems that adapt in real time

• Change in implementation

• What is driving this?Falling cost of hardware, increasing computational power, increasingly complex control, algorithms and development of new, low cost micro sensors and actuators.

Distributed Multi-ProcessorDistributed Multi-Processor

CentralizedSingle Processor


ModularityModularity

Faster Development TimeFaster Development Time

High ReconfigurabilityHigh Reconfigurability

Easy MaintenanceEasy Maintenance

Fault TolerantFault Tolerant

Complex SoftwareComplex Software

Data CommunicationData Communication

Unbounded Time DelaysUnbounded Time Delays

Real-time Task SchedulingReal-time Task Scheduling

Modification of Control AlgorithmsModification of Control Algorithms

BE

NE

FIT

S

CO

MP

LEX

ITY

Distributed Multi-ProcessorDistributed Multi-Processor



ModularityModularity

Faster Development TimeFaster Development Time

High ReconfigurabilityHigh Reconfigurability

Easy MaintenanceEasy Maintenance

Fault TolerantFault Tolerant

Complex SoftwareComplex Software

Data CommunicationData Communication

Unbounded Time DelaysUnbounded Time Delays

Real-time Task SchedulingReal-time Task Scheduling

Modification of Control AlgorithmsModification of Control Algorithms

BE

NE

FIT

S

CO

MP

LEX

ITY

• Is there a price?Yes! Need sophisticated, reliable software to manage distributed collection of components and tasks.


Reliability of Real-Time Control Systems Verification gap expands exponentially with complexity

Verification gap due to rising complexity in embedded systems. (Source:www.verisity.com)

Complexity in Embedded Systems• Cell phones : ~ 10 million lines of code.• Automobiles : ~ 100 million lines of codes.• Aerospace : ~ 1 billion lines of code.

Verification is Expensive• 90% time is spent on verification and validation

Cost of Failure• 100 times more in the field than in the development stage

Classification of Uncertainty in Real-time Systems• System (model error, sensor noise, etc)• Communication (delays, packet loss, etc)• Computation ( transient CPU overloads)• Product Development (software V&V)

Time

Cap

abili

ty

Not possible to innovate No ability for growth Not possible to innovate No ability for growth

Less reliable productsIncreased failure rate in the fieldHigh cost implicationsResources engaged in fire fighting

Less reliable productsIncreased failure rate in the fieldHigh cost implicationsResources engaged in fire fighting

Cannot react to market changes Competition sensitive Market penetration is difficult

Cannot react to market changes Competition sensitive Market penetration is difficult

Consequence of the Expanding Verification Gap

Solution?Guarantee reliability by design, execution and system engineering.

How? Next slide ….


Uncertainty in System Design application algorithms robust to system uncertainty

Uncertainty DescriptionModel uncertainty, sensor noise, wind gust, etc.

Complexity

Physics.

MitigationDesign controller K to guarantee robust performance.

Methods

Robust Control Design techniques, etc.

V&VBound on input to output norm, etc.

System ComputationCommunication System Engineering

• This is a well researched area.

• Several techniques exist for robustness analysis of linear and nonlinear systems.


Uncertainty in Communication Design application specific transmission controller and routing algorithm to bound communication uncertainty

Uncertainty DescriptionDelays, packet loss, channel noise, multiple transmissions, etc.

ComplexityInformation

MitigationDesign controller K to mitigate communication uncertainty, robust data transmission.

MethodsControl with communication constraints, packet based control, filtering, etc.

V&V Bound on delays, data rate, etc.

Design of Robust Communication Network• Application defines data traffic, data source & topology.

• Synthesize transmission controller and routing algorithm based on communication dynamics.

• Guarantee bounds on delay.

• Preliminary research is based on the work by F. Kelly and G. Vinnicombe, S.Low, J.C. Doyle and F. Paganini.

• Looking at data rate bounds in a dynamic topology as a switched linear system.

Research at aero.tamu.eduResearch at aero.tamu.edu



Design of Robust Communication Network Model data-rate dynamics using fluid based linear models


Fig2: Dynamic Topology – Effective Data Rate is a Hybrid System

t1 t2 t3

G1(t1) G1(t2) G1(t3)

Fig1: Large Scale Network as a Composite of Small Scale Networks

Assumptions• Spatial distribution and connectivity of the mobile agents is described via a graph.

• The graph is assumed to be dynamic in a sense that it adapts to the movement of the agents.

• The agents are constrained to satisfy certain simple dynamics, i.e. they cannot stop on a dime, etc.

• The exact trajectories of the agents are governed by a higher-level algorithm that the agents are implementing; e.g. dynamic sensing algorithm, surveillance, etc.

ApplicationDesign robust communication network for mobile agents engaged in surveillance.

Approach1. Use fluid based linear models to describe the dynamics of data rate for small-scale networks

2. Changing topology results in a switched linear system.

3. Model traffic load as a stochastic process. (Poisson Process, Erlang Formula, etc).

4. Analyse dynamics of node-to-node data rate.

5. Design feedback congestion control algorithm for robustly stable data rate.

6. Work based on research by F. Kelly and G. Vinnicombe, S. Low, J.C. Doyle and F. Paganini.

ObjectiveStabilize node-to-node data rate in the presence of dynamic topology.


Uncertainty DescriptionTransient computational overloads, variation in execution characteristics of code, uncertainty in resource availability, etc.

ComplexityTime

MitigationScheduling of CPU and other resources to guarantee execution deadline.

MethodsDynamics scheduling, imprecise

computation, anytime algorithms, etc.

V&V Bound on runtime, etc.

Uncertainty in ComputationImplement algorithms as anytime algorithms


Anytime Control Algorithms• In real-time systems, the utility of the decisions degrade with the time spent on computation. • The degradation in utility due to cost of time will render traditional models of computation useless real-time systems in uncertain environments.

• Anytime algorithms represent a class of algorithms that can tradeoff quality of solution for computational time.

• For controllers, performance is compromised for computational time during transient overloads. Stability is never compromised.

• Developed preliminary results for linear time invariant controllers.

Decision Quality

Time

Ideal

Anytime

Traditional

Anytime + Time CostTime Cost

Figure : Decision quality with respect to time for Ideal, Traditional & Anytime Procedures (Source: Zilberstein )

Source: Zilberstein



Anytime Control AlgorithmsModel Reduction Approach


Consider Linear Controllers Model ReductionComputational time depends on number of states rejected.

Original Controller

Balanced Realization

Reduced Order Controller

Anytime ImplementationSwitch from higher order to lower order controller during transient CPU overload

C2 :Low Order Controller

C1:High Order Controller

C3 :Transition Controller

Low CPU

High CPU

High CPU

Results• Algorithm is tested on a linear model for longitudinal motion of a B737-100 TSRV (Transport System Research Vehicle).

• Controller objective is to track flight path angle and velocity reference signal.

• Able to accommodate drop in CPU resources by 35%.

• The closed-loop system is robustly stable, compromised tracking performance to save CPU time.


Uncertainty in System EngineeringModel and Platform Based Design Methodology

Uncertainty DescriptionMismatch between requirements & implementation, verification gap, sub-component interactions, hardware-software interactions, etc.

ComplexitySoftware testing.

MitigationRegression testing, hardware in the loop testing,code coverage analysis, etc.

MethodsModel and platform based design of embedded software.

V&V Validation of requirements with embedded software, high percentage of code coverage, etc.

Robust Embedded Software Development Process

• Separation of concern between various stages in the design process.

• Use formal models to capture functionality and architecture.

• Conduct early validation at each stage before proceeding.

• Map solutions at one stage to solutions in the following stage


The Shift from Physical Prototyping to Virtual Prototyping and Integration

Image Source: PARADES

The Shift from Physical Prototyping to Virtual Prototyping and Integration

Image Source: PARADES



Model and Platform Based Product DevelopmentEnabler for Engineering Effectiveness and Reliability

1. Separation of concern between various stages in the design process.

2. Use formal models to capture functionality and architecture.

a) Design Flow b) Design Flowwith key articulation

points

Key Articulation

Points

c) Exploration of alternate solutions atkey articulation points

DesignSpace

Exploration

PlatformA family of alternate solutions

d) Mapping of solutions in upper layer

to solutions in lower layer during integration

Key Principles:

ConstraintsSpecifications Mapping



Model and Platform Based Product DevelopmentKey Benefits

SIMULINK Rhapsody

Intel Power PC MOTOROLA myProcessor

API Layer


API Layer

Polis

ConstraintsMemory Processor Speed I/O bandwidthQuantization of Data

SpecificationsAlgorithms Execution OrderExecution RateExecution DeadlinesPriority

Cod

e G

ener

atio

n

Models of mySystem

PTOLEMY

FUNCTION LAYER

ARCHITECTURE LAYER

SIMULINK Rhapsody


API Layer


API Layer

Polis

ConstraintsMemory Processor Speed I/O bandwidthQuantization of Data

SpecificationsAlgorithms Execution OrderExecution RateExecution DeadlinesPriority

Cod

e G

ener

atio

n

Models of mySystem

PTOLEMY

FUNCTION LAYER

ARCHITECTURE LAYER

Mapping of Functionality to Architecture

Examples:

Key Benefits:

Capability BenefitsEarly Validation Reduced turn backs, higher reliability

Platform Flexibility Lower cost & obsolescence insensitivity

Reuse Faster development time

Analysis Quantification of quality & efficiency

Early ResponseCapability

Separation of Architecture from Functionality

FunctionDefine what needs to be done

ArchitectureDefine how it is done

MA

PP

ING

MA

PP

ING

SpecificationsSpecifications ConstraintsConstraints





MA

PP

ING

MA

PP

ING

SpecificationsSpecifications ConstraintsConstraintsSpecificationsSpecifications ConstraintsConstraintsConstraintsConstraints





MA

PP

ING

MA

PP

ING

SpecificationsSpecifications ConstraintsConstraints





MA

PP

ING

MA

PP

ING

SpecificationsSpecifications ConstraintsConstraintsSpecificationsSpecifications ConstraintsConstraintsConstraintsConstraints





New Paradigm in Embedded System Design Process MBPD and the Design “V”

ManualTest vectors

REQ

IntegrationwithAPI

FUNCARCH

Models

Modeling

API Platform

ANSI CCode

ModelRefinement

ANSI CLanguage

Code Generator(RTW)

TargetedModels

SYS

Platform Abstraction


Component Validation(desktop)

System Validation(Physical Prototype)

Auto generated Test vectors


ManualTest vectors

REQ

IntegrationwithAPI

FUNCARCH

Models

Modeling

API Platform

ANSI CCode

ModelRefinement

ANSI CLanguage

Code Generator(RTW)

TargetedModels

SYS



Component Validation(desktop)

System Validation(Physical Prototype)

Auto generated Test vectors




Tools for Software and Hardware ModelingSoftware modeling tools are more matured than hardware modeling tools.

TOOLS

MATLAB, Simulink, Stateflow

ASCET SD SCADE Rhapsody (UML)

FUNCTIONAL DESIGNMore matured

Hardware Platform Abstraction, Selection & AnalysisResearch Level

Univ. of Michigan, Princeton, Univ. Minnesota

UC BerkeleyRT-BuilderRT-Builder

Tools for functional design are more matured than tools for hardware abstraction and analysis.

TOOLS

TOOLSTOOLS






UC BerkeleyRT-BuilderRT-BuilderRT-BuilderRT-Builder


TOOLS

Tools Supporting PBD

TOOLS






UC BerkeleyRT-BuilderRT-Builder


TOOLS

TOOLSTOOLS






UC BerkeleyRT-BuilderRT-BuilderRT-BuilderRT-Builder


TOOLS

Tools Supporting PBD



Technology MaturityWho is using it?

PA

RA

DE

SP

AR

AD

ES

AutomotiveAcademia Aerospace Semiconductor Industrial Equipment

UC Berkeley

PA

RA

DE

SP

AR

AD

ES


UC Berkeley

PA

RA

DE

SP

AR

AD

ES

PA

RA

DE

SP

AR

AD

ES


UC Berkeley

PA

RA

DE

SP

AR

AD

ES

PA

RA

DE

SP

AR

AD

ES


UC Berkeley

Model and Platform Based Design framework has been successfully applied to a diverse group of industries and has potential to become a standard for embedded systems development.



Other Research ActivitiesGuidance Algorithms for Entry Descent Landing

• Apply receding horizon control methodology to achieve better guidance performance (70% improvement).


Other Research ActivitiesReal-time Trajectory Generation Toolbox in MATLAB

Trajectory Space ApproximationB-Splines are used to transform infinite dimensional problem to finite dimensional problem.

Problem FormulationTrajectory generation problem is cast as an optimal control problem of the following form:

Dynamics:

Constraint:

Cost:

Solution ProcessTranscribe optimal control problem to nonlinear programming problem.

Test bedBlimps from Draganfly, vision based positioning, 3 fan actuation, RF controlled.


Questions ?

Raktim Bhattacharya AEROSPACE ENGINEERING Robust Real-time Control Systems Reliability through...

Documents

Transcript of Raktim Bhattacharya AEROSPACE ENGINEERING Robust Real-time Control Systems Reliability through...