Timing analysis and Design Optimization for Real-Time Engine...

Timing analysis and Design Optimization for Real-Time

Engine ControlsAlessandro Biondi

Scuola Superiore Sant’Anna, Pisa, Italy

2A. Biondi – IWES 2016

THIS TALK

1 Engine Control Applications

2 Our Contribution

3 Ongoing Work

A LOOK INTO ENGINE CONTROL

APPLICATIONSTiming, macro-functional aspects

and challenges

1


INTRODUCTION

Engine control is a very interesting andchallenging CPS problem

• Software plays a key role

• Design constraints (due tolimited computational power)

• Timing significantly influencessystem performance

• Both time- and event-drivenbehavior


ENGINE CONTROL APPLICATIONS

• Engine control applications include

• Periodic Tasks, with fixed periods (1-500 ms)

• Angular Tasks, linked to the rotation of the crankshaft

(t)

t

t

/2

3/2

2

Task activations

activate task

10-120 ms


ENGINE CONTROL APPLICATIONS

CPU

ECU

Set of Periodic Tasks

Set of Engine-triggered Tasks


1 2

C(1)

C(2)

C(3)

1

2

3

To prevent overload at high rates, different control implementations are used

speed time

timeexec time

ADAPTIVE BEHAVIOUR

speed


ADAPTIVE BEHAVIOUR

fuel injections

controller controller controller controller

~C

Engine-triggered Task

To prevent overload at high rates, different control implementations are used


A FAMILY OF CONTROLLERS

C C

C C

C C

C

C C

C

C C

#1

#2

#3

#N

~


TIMING IS IMPORTANT

Timing properties of the controllers “rules theroost”

• Scheduling plays a key role

• Engine performance depends (in differentand complex ways) on timing parameters

• Accurate timing analysis is useful to drivedesign choices


EXAMPLE OF IMPACT

EngineplantTPUCPU

Latch

Angular

Trigger

Injector

• TPU uses data produced from the CPU

(injection angle, quantity of fuel, CR pressure…)

If deadlines are missed (on the CPU), theTPU uses old data for the next injection

FUEL INJECTION


EXAMPLE OF IMPACT

• Deadline misses can be penalizing if the conditions

of the engine changed (too much) from previous

cycles.

• The use of old data can produce errors in the

injection angle.

Scheduling errors

FUEL INJECTION

Inaccurate injection

Decreasing performance


THE MULTICORE DIMENSION

• Introduce further complications

E.g., syncronization, task placement,…

• If we are not able to understand timingaspects of such applications it is hard toattack real-world problems

Engine control systems are (inevitably) switching to multicore platforms

OUR CONTRIBUTIONMajor achievements during the last ~3 years of research

2


TIMING ANALYSIS

Periodic computational activities

Periodic Real-Time Tasks – Studied since 70’s

Engine-triggered computational activities

Adaptive Variable-Rate (AVR) Tasks –Studied only in the last years

Davis et al. RTAS14Buttazzo et al. DATE14 Guo and Baruah ICCPS15Biondi et al. ICCPS15


TIMING ANALYSIS WITH AVR TASKS

• Exact response-time analysis under fixed-priority scheduling

• Exact schedulability analysis under dynamic-priority (EDF) scheduling

• Linear-time approximation scheme for thedynamic-priority (EDF) schedulability analysis

State-of-the-art analysis techniques failed with engine-

control tasks

Novel timing analysis techniques

for the AVR task model

Major Achievements


DYNAMIC-PRIORITY SCHEDULING

Significant benefitsobserved (in theory), much higher than those well-known for periodic tasks

Priority

Assignment

Law

Dynamic-priority

Scheduler

Engine Speed

Processor load

No

de

adlin

e m

iss

Dynamic-priority

Fixed-priority


RTOS SUPPORT

OSEK-like support for dynamic-priorityscheduling of engine control applications

• Requires minimal changes to existing applications

• Integrated with OSEK configuration language (OIL)

• Low run-time overhead (+1.5 𝝁𝒔 over fixed-priority)

• Low additional footprint (<500 bytes)on STM32 @ 160Mhz


MULTICORE REVOLUTIONC

urr

en

tly

ma

pp

ed

in 1

2 t

as

ks

Mapped onto

the tasksCalling 100s of DD

functions

Ap

pro

x

20

0 r

un

na

ble

s

With

internal

depend

-encies

Using the

devices of

the micro

2 Cores

Mapped onto

the tasks

Ma

pp

ed

?

Allocated and scheduled on 2 cores

+ INTER-CORE SYNCHRONIZATION

? ? ?

Ap

pro

x

20

0 r

un

na

ble

s

Calling 100s of DD

functionsSharing

the

devices of

the micro

With

internal

depend

-encies

Scheduled by FP

on 1 core

1 Core


MULTICORE REVOLUTION

CC++

Static code analysis

AUTOSAR model + extensions(for detailed timing info and improved HW model)

Execution trace data Optimization

algorithms(wrt time only)

2 Cores

OUR APPROACH


DESIGN ISSUES

C C

C C

C C

C

C C

C

C C

#1

#2

#3

#N

~

Engine control involves multiple complex multi-

criteria design optimization problems

(power, fuel efficiency, noise, emissions,…)

An Example:


DESIGN ISSUESSWITCHING SPEEDS

𝜔

WCET(𝜔) Most complex

implementation

impl. #2

impl. #3

impl. #4

𝜔𝑚𝑖𝑛 𝜔1 𝜔2 𝜔3 𝜔𝑚𝑎𝑥

simplified control implementations giving lower performance

Which is the best speed to switch control

implementation?

TODAY’S APPROACH:ITERATIONS BETWEEN

TEST-BENCH AND TUNING

“Something” more systematic supported by a

model and an analysis would be very useful…


OUR APPROACH

Formulated as an optimization problem

Find the switching speeds that maximize the engine performance

such that no deadline misses occur

𝜔1

𝜔2

𝜔2 > 𝜔1

no deadline miss

Non-convex optimization


CONTRIBUTION

Algorithm to bound the design space

Effective heuristics, empirically very close

to the optimum (>95%)

Ad-hoc branch-and-bound algorithm

Analytical performance model based on

real-world data𝑝 𝜔 = 𝑘1𝑒

−𝑘2𝜔

ONGOING WORK

3


TIMING ANALYSIS: OPEN PROBLEMS

• The timing analysis for the AVR model is stillnot able to capture some real-world designsolutions, such that

• Different angular periods

• Angular phases

• Hysteresis in mode-change

• Multiframe behavior

• Fine-grained Synchronization

• Multi-priority tasks

currently solved only under approximation schemes


CPS ANALYSIS: THE (REAL) PROBLEM

Enginemodel

Control laws

SimulinkScheduler interface

External SchedulingSimulator

Sensors

Actuation

Control

Unit

Simulink architecture

Deadline misses can be tolerated

“…but not that many”… and “what matters is performance”

Work by Paolo Pazzaglia


MULTICORE: OPEN PROBLEMS

SingleCore MultiCore

• Scalability issues of optimization algorithmsfor task placement

• Platform-aware placement (device sharing)

• Support for bus and memory contention

• Support for adaptive variable-rate tasks

• …

Thank you!Alessandro Biondi

[email protected]

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