mechatronics (mechanical system control): it’s the …bramlambrecht.com/tmp/me230/01 -...
TRANSCRIPT
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Copyright (c) 2007, D. M. Auslander 1
Mechatronics (Mechanical System Control): It’s The Software!
David M. AuslanderMechanical Engineering
University of California at Berkeley
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Preamble
1. Don’t take the name “Mechatronics” too literally2. The biggest value-added in mechatronics is in
software3. Mechanical system control:
Then: Striving for complexityNow: More complexity than we can handle!
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Mechanical Systems
A long history of complexity in mechanical devicesModulation of power for delivery to a targetBroadly applied to physical systems
Motion, thermal, fluid, chemicalThesis: Software has made this into a new game!
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A Little History
Computation in control of early machinesDelivery of power – steam enginesComplex pattern generation – Jacquard loomBrushed DC motor
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Fairbottom Bobs
Newcommenengine(~1760)http://www.ashton-under-lyne.com/bobs.htmAshton under LynePumped water from coal pitsPhoto ~1880
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Newcomen Engine Control
www.technology.niagarac.on.ca/courses/tech238g/newcomen.htmAtmospheric steam engineUsed water spray to condense steam in cylinderControl of valve based on walking beam position1712, invented first usable steam engine
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The Watt Governor
http://www.vintagesaws.com/library/steam/steam.htmlFor cotton mill1856100 HP, 30 RPMNote flyball (Watt) governorSmithville, Texas, USA
Courtesy of Vintage Saws
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Closeup of Governor
Note link that connects flyballto steam valveMajor limitation of all classically controlled mechanical systems
Courtesy of Vintage Saws
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Jacquard Loom
http://www.digidome.nl/history.htmPunch card driven1804Used to weave very complex patterns in silk
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Silk Woven on a Jacquard LoomSilver and gold threads used for the pattern
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Still In Use …
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Classical Mechatronics – Brush vs. Brushless Motors
Brush motor –classic mechanical systemRotor – coilsStator – permanent magnetsCommutatorcomputes
Amplifier
+
-
Magnets
N S
Coils/Commutator Brushes
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Complexity Limitation in Classical Mechanical Systems
No separation of sensing, computation, and powerExample: flyball governer
Power to operate steam valve comes from flyballMust have low-impedance path from main shaft all the way to the steam valveCommon physical medium (mechanical)
Example: Brush motor, commutator
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Enter Mechatronics
Yaskawa Electric coined the term around 1970 to describe brushless motor technology
Trademark, then releasedAdding electronics made a completely different class of systemInconceivable in prior era
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Brushless Motor
Brushless –invert rotor and statorMeasurement of rotor position needed to properly excite stator coils
Multi-channelAmplifier
Computation
Stator(coils)
Wires
Wires
Rotor (magnet)
Positionsensor
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Modern Mechatronics
Add economic, compact computation“The synergetic integration of mechanical engineering with electronics and intelligent computer control in the design and manufacturing of industrial products and processes.” (IEEE/ASME Transactions On Mechatronics, 1996)Or, “The application of complex decision-making to the control of physical systems”
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What’s Unique?
The shorter definition focuses more strongly on the uniqueness of software-driven mechanical systemsThey can have control complexity beyond the wildest dreams of pre-computer engineersControl – interpreted broadly, not just feedback control
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Enabling Technologies - I
AmplificationVacuum tube, Lee De Forrest, 1906Flapper nozzle valve, pneumatic and hydraulic
Enabled isolation of measurement, computation and actuation
Impedance mismatch vs. impedance matchOptimization of medium
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Enabling Technologies - IIThe Emergence of Software
Process control was initial applicationCould afford very expensive, large hardwareImproved productivity, reliabilityMicroprocessor invention dramatically lowered cost-of-entryNow – cheapest way to control power modulation
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Real Time Software
Software is data reproducibleSuccessive program operation with same input data produces same output
This is a defining property of computation and software – no error propagation!
Essence of digital systems: no complexity limitHowever, it is not generally time reproducibleExample – timed loop with histogram
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Same Program, Run Twice
Time (sec) # less than2.0E-6 04.0E-6 26401028.0E-6 169721.6E-5 463.2E-5 3486.4E-5 2411.28E-4 1972.56E-4 915.12E-4 123
Time (sec) # less than2.0E-6 04.0E-6 26342228.0E-6 169681.6E-5 653.2E-5 3626.4E-5 2301.28E-4 2142.56E-4 1005.12E-4 160
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Real Time for Mechatronic Control
In brief, specifications (tolerances) for time reproducibilityEach module of control software will have its own specs“Hard” (deadline) real time is not usually requiredMuch of the activity is asynchronous preventing deterministic scheduling
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Design Principle
If any mechanical components are present to convey information consider replacing them with software (first choice) or electronicsExamples
Brushes brushless motorCarburetor fuel injectionKinematic linkages, cams motion profilesAir dampers variable speed motors
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Design Context: The Unit Machine
Domain of applicabilityEstablish appropriate design methodology“The elements of a unit machine exchange physical power with each other or exchange material with little or no buffering”Unit machine too big can’t handle complexityUnit machine too small can’t optimize
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Example: Semiconductor Mfg
Courtesy of Berkeley Process Control, Inc
Redefinition of the “unit machine”improved throughput by 3.5 times for a system similar to this!
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Control Software for a Unit Machine
Must have rapid access to all internal informationSensors, actuators, states, commands, etc.Rapid: fast enough to use in control loopsCan be used to optimize operation
Information between unit machines usually simple commands, limited scope, slow
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Unit Machine Examples
Wafer handling robotCommercially defined as unit machineOften too simple
Denver airport baggage handlingToo complex to be treated as unit machineDefeated by complexity (my opinion!)
Dynamic definition of unit machine in biologyHuman gait change from walking to running
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Industrial Experience: Complexity is the Problem
Control (read software) is the largest cause of failure in complex manufacturing machinesFailure to understand the consequences of complexity lead to unreliable operation, poor performanceMechanical engineers – don’t understand softwareSoftware engineers – don’t understand machines
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Design Language
A means of describing and documenting solutions; map easily to softwareMore abstract than code
Most code is unreadable – even by the person that created it!
Communication vehicle among all stakeholdersEngineering, manufacturing, marketing, maintenance, etc.
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Tasks and State Machines
Tasks – Simultaneously executing modules“A task is a well-defined responsibility” (American Heritage Dictionary)Suitable for complexity levels associated with unit machinesHierarchical organizationLowest level maps to mechanical system hardwareHigher levels are goal oriented (next slide …)
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Software
HardwareActuator InterfaceActuator Interface Sensor Interface
Actuator Sensor
Target Object
Actuator SignalProcessing
Sensor SignalProcessing
Feedback Control
Supervisory Control
To other targets
Goal Seeking
To other subsystems
External Communication Operator Interface
Control Software
Outside World
OperatorFacility Network Internet
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Finite State Machines
Internal task structure is finite state machineStates consist of three sections
Entry – executed only after a transitionAction – execute alwaysTransition test
Tasks and associated state machines provide a design model that is widely accessible as well as translatable into functioning software
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Implementation Languages
Desired properties in implementation languagesPortabilityClean syntaxEfficient footprint and operationWell documented
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Software Portability
Primary factor in productivity/economicsDevelopment stagesProduction upgradesProcessor generation time: 18 months or lessMechanical generation time: 5 – 20 years
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Computational Implementation
Clean separation between design and software implementationFocus on mapping design to software
Easily connect sections of software with design elements
To the extent possible, weaken the dependence on language, OS, environment, hardware specifics
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Cooperative Multitasking
Most portable form of multitaskingRequires one major stylistic restriction:
All code must be non-blockingState machine fits this model very well
“Universal” real time model –If computer is fast enough, can meet all specsOften true
Otherwise, interrupts, priorities, etc. involve resource shiftingSee plenary talk by Michael Pont on this subject
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Low/Medium Level Languages
Object-oriented approach“Implementing two motors for position control within the program was a rather straightforward approach …” (Berkeley student)C, C++ and Java Java easier to learn, cleaner syntax, much better portability, but has performance problemsC still the most widely used
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High Level Languages
Programming productivity – lines/day, regardless of languageTherefore, use language requiring fewer linesCode generator (as in Matlab/Simulink) or embed into processor (e.g., Labview)More practical as processors get fasterStill used more for development than production
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Simulation
Crucial step in design process but often skippedConceptual and execution errors much more easily found than in real environmentTakes initial effort to set up simulation
Engineers don’t want to spend the time!Dilemma: How to avoid rewriting control code for simulation?Major portability challenge
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Simulation Environments
C, C++, JavaLimited mathematical and plotting support
Matlab/Simulink and other mathematical environments
DLL for control code or use code generatorCh
C for control code; C and limited C++ for simulation with rich math and graphics – easy to integrate C
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Implementation Environments
From lab to production, PC to microcontrollerReal time
General purpose OS (Windows, etc.) ~1 second (can be used faster for demo, debug, but not reliable)Real Time OS (RTOS - QNX, VxWorks, etc) sub-millisecond for regular tasks, microsecond for interruptsLabview-RT – sub millisecond, shell must be LabviewBare processor – microsecond
Cooperative multitasking improves portability
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Multiprocessor and Networking
Networking becoming ubiquitous even in control systemsMany network “standards”Ethernet gaining ground, but no winner yetThree network levels (at least!)
Sensor and actuatorControl processorFactory (sometimes a cell level also)
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Networking and Control Software
Portability again the keyTreat tasks as indivisible network componentsAbstract intertask communication
That is, custom application layer for intertaskcommunication
Allows for isolation of actual network protocols
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Future Directions
Moore’s law still holds, but direction has changed
Multi-core processors rather than more powerfulOnly likely to impact high-end systems in near future
FPGA (field programmable gate array)New processor frontierFully parallelUsually viewed as circuit element but complexity has increased so now looks like processor with software
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Lessons Learned
Managing complexity is the challengeModularity is the primary tool
Unit machine for hardware designTasks, state machines for software design
Too much modularity limits the amount of global optimization that can be doneToo little leads to unpredictable behavior and costTherefore, err on the side of too much modularity