Compare IEC 61131-3, Allen-Bradley, Siemens SCL, Mitsubishi, Omron, KEYENCE KV, Schneider Unity, Delta, Instruction List in a runnable learning environment. Each track teaches transferable syntax and logic patterns; none claims to replace the vendor IDE, controller firmware, or hardware test.
Every code sample below comes from the same validated seal-in program used by the cross-dialect test suite. The scope is intentionally explicit: educational interpreters for the listed syntax subsets, not complete vendor emulators.
Vendor-neutral foundation
The international PLC-language standard and the clearest starting point for reusable control logic. The tutor covers ladder-oriented expressions, declarations, function blocks, timers, counters, latches, arithmetic, and common edge patterns.
VAR
START AT %I0.0 : BOOL;
STOP AT %I0.1 : BOOL;
MOTOR AT %Q0.0 : BOOL;
END_VAR
| START OR MOTOR AND /STOP | := MOTOR ;Rockwell Automation · Studio 5000-style learning syntax
Practise the tag-based XIC, XIO, OTE, OTL, OTU, TON, and CTU patterns found throughout North American manufacturing. It teaches transferable Logix-style reasoning; it does not open ACD files or emulate a ControlLogix processor.
TAG START I:0/0 BOOL
TAG STOP I:0/1 BOOL
TAG MOTOR O:0/0 BOOL
XIC START OR XIC MOTOR AND XIO STOP OTE MOTORSiemens · STEP 7 and TIA Portal-style learning syntax
Learn Siemens-oriented boolean, latch, timer, counter, and data patterns with familiar A, AN, O, S, R, and assignment forms. This is a curriculum interpreter, not PLCSIM or an S7 firmware emulator.
VAR
START AT %I0.0 : BOOL;
STOP AT %I0.1 : BOOL;
MOTOR AT %Q0.0 : BOOL;
END_VAR
A START
O MOTOR
AN STOP
= MOTORMitsubishi Electric · GX Works / MELSEC-style mnemonics
Use LD, ANI, OR, OUT, SET, and RST with Mitsubishi-oriented device and symbolic naming. The supported subset is designed for core logic practice, not native GX Works project files or complete instruction coverage.
VAR
START AT %I0.0 : BOOL;
STOP AT %I0.1 : BOOL;
MOTOR AT %Q0.0 : BOOL;
END_VAR
LD START
OR MOTOR
ANI STOP
OUT MOTOROmron · CX-Programmer / Sysmac-style learning syntax
Practise the boolean, latch, timer, and counter forms used in packaging and machine automation. The interpreter focuses on the curriculum subset and transferable logic rather than full CX or Sysmac runtime fidelity.
VAR
START AT %I0.0 : BOOL;
STOP AT %I0.1 : BOOL;
MOTOR AT %Q0.0 : BOOL;
END_VAR
LD START
OR MOTOR
AND NOT STOP
OUT MOTORKEYENCE · KV STUDIO-style mnemonic learning syntax
Practise KV relay and data-memory addressing with R, MR, DM, T, and C devices plus LD, LDB, ANB, OUT, SET, RES, timers, counters, and arithmetic. This tested subset is for learning; it does not open KV STUDIO projects or emulate KV firmware.
VAR
START AT %I0.0 : BOOL;
STOP AT %I0.1 : BOOL;
MOTOR AT %Q0.0 : BOOL;
END_VAR
LD R000
OR R500
ANB R001
OUT R500Schneider Electric · Unity Pro / Control Expert-style learning syntax
A Schneider-oriented track for the IEC-style logic used around Modicon controllers. It covers the runnable curriculum subset and common control patterns; it is not a Unity or Control Expert replacement.
VAR
START AT %I0.0 : BOOL;
STOP AT %I0.1 : BOOL;
MOTOR AT %Q0.0 : BOOL;
END_VAR
LD START
OR MOTOR
ANDN STOP
ST MOTORDelta Electronics · WPLSoft / ISPSoft-style mnemonics
Work with DVP-oriented X, Y, M, T, C, and D devices plus LD, LDI, ANI, OR, OUT, SET, RST, TMR, CNT, and core math operations. Address translation and acceptance tests run in the browser.
LD X0
OR Y0
ANI X1
OUT Y0IEC 61131-3 legacy language · vendor-neutral track
Instruction List was deprecated in IEC 61131-3 third edition, but it remains valuable for installed equipment. This track teaches accumulator-style LD, AND, OR, ST, set/reset, timer, and counter patterns.
VAR
START AT %I0.0 : BOOL;
STOP AT %I0.1 : BOOL;
MOTOR AT %Q0.0 : BOOL;
END_VAR
LD START
OR MOTOR
ANDN STOP
ST MOTORSee the same five programs rendered across all 9 tracks.
Use progressive syntax lessons, immediate feedback, hints, and worked corrections inside each supported learning track.
Move from contacts and coils through timers, counters, latches, and mixed logic.
Study the DVP address model and then run the supported mnemonic subset.
The first 6 core lessons per dialect are free. Pro adds the complete curriculum, worked solutions, and the full supported learning set.
Software evaluation field guide
Direct answer
PLC dialect learning feature becomes useful when it connects source and target platforms, software versions, languages, task model, address or tag model, instruction state, data types, time bases, restart and online workflow with control requirement through source semantics, neutral behavior contract, target syntax, owned state, compiled execution and machine result, then proves one start-stop, timer, counter and edge example expressed and tested in two dialects under normal, boundary, fault and recovery conditions. The objective is a repeatable engineering or learning result, not merely activity inside a page or tool.
This guide is written for learners and teams comparing IEC-style concepts with Allen-Bradley, Siemens, Mitsubishi, Omron and Delta naming and workflows. The intended result is specific: the evaluator can separate transferable control intent from vendor syntax and define the target-specific tests required before migration.
System map / 02
Treat these as connected checkpoints. Each checkpoint has an expected state, an observable state and a boundary to the next part of the system. That structure prevents a software indication from being mistaken for physical proof.
source and target platforms, software versions, languages, task model, address or tag model, instruction state, data types, time bases, restart and online workflow. For cross-vendor PLC dialect comparison and transfer, record the initial condition, actor, requested change, observable result and stopping condition before selecting a tool or implementation.
control requirement through source semantics, neutral behavior contract, target syntax, owned state, compiled execution and machine result. Separate request, internal state, output or service, physical or user-visible result and independent feedback so each boundary can be inspected.
one start-stop, timer, counter and edge example expressed and tested in two dialects. Run more than one cycle from a known state and retain the values, timings or artifacts that demonstrate repeatability.
prescan, first scan, retained memory, skipped calls, time representation, overflow, task period, online edit and power return. Choose minimum, maximum, simultaneous, delayed or restart conditions that reveal assumptions hidden by the happy path.
a naming, parameter, instance, data-type, scan-order, retention, time-base or workflow mismatch. Preserve the first symptom, divide the system at a measurable boundary and change one condition only after predicting the result.
each example rebuilt, compiled and tested in the current official target environment. Restore normal state, remove temporary changes, repeat affected checks and document which claims remain limited to the learning environment.
Procedure / 03
Run the steps in order the first time. Later, the same structure becomes a diagnostic loop: define the expected condition, observe the boundary, interpret the difference and choose one proving action.
Convert source and target platforms, software versions, languages, task model, address or tag model, instruction state, data types, time bases, restart and online workflow into initial conditions, one stimulus and observable pass criteria.
Evidence: Another person can repeat the case without guessing the intended result.
Avoid: Using page completion or an animation as the acceptance criterion.
Document control requirement through source semantics, neutral behavior contract, target syntax, owned state, compiled execution and machine result and name who owns each state or decision.
Evidence: Every request and result has a source, destination and useful inspection point.
Avoid: Using the same value as command, status and independent feedback.
Apply one start-stop, timer, counter and edge example expressed and tested in two dialects from a clean start and record the expected evidence.
Evidence: Repeated runs produce the same bounded result.
Avoid: Changing several parameters before a baseline exists.
Test prescan, first scan, retained memory, skipped calls, time representation, overflow, task period, online edit and power return without changing the acceptance contract.
Evidence: Limits, timing and restart behavior reach defined states.
Avoid: Testing only one ideal sequence.
Introduce or analyse a naming, parameter, instance, data-type, scan-order, retention, time-base or workflow mismatch and locate the first disagreement.
Evidence: The proving action distinguishes the leading hypotheses.
Avoid: Resetting, forcing or replacing before evidence is retained.
Complete each example rebuilt, compiled and tested in the current official target environment and repeat the affected regression cases.
Evidence: An evaluation is complete when the same representative job is tested in each candidate and differences are recorded as evidence rather than inferred from feature labels.
Avoid: Treating an acknowledged message or one successful rerun as handover.
Diagnostic matrix / 04
The table is a reasoning aid, not a parts-replacement chart. Preserve the initial symptom, inspect the named boundary and use the interpretation to choose the next controlled test. Site safety procedures and equipment manuals remain authoritative.
| Observed symptom | Inspect | Interpretation | Next proving action |
|---|---|---|---|
| The expected result is unclear | Requirement, initial state, actor, stimulus, units and pass condition | The evaluator, instructor and technical buyer may be solving different versions of the task. | Rewrite one observable acceptance case before continuing. |
| Internal state changes but the outcome does not | Request, final owner, output or service boundary and independent feedback | A software or interface indication proves intent at one layer, not the complete outcome. | Trace the first boundary after the changing state. |
| Normal case passes but an edge case fails | Limits, timing, simultaneous events, reset and restart assumptions | The implementation contains a hidden assumption exposed by the changed condition. | Add the failed boundary as a permanent regression case. |
| The failure disappears after reset | Original symptom, histories, diagnostics, timestamps and active cause | Reset changed evidence or state without proving the initiating cause. | Reproduce under a controlled condition and preserve pre/post-event data. |
| Simulator and target disagree | Model boundary, software version, task timing, I/O behavior, data types and configuration | A learning model and the intended target do not share one of the recorded assumptions. | Reduce the case and verify against current target documentation. |
| The result cannot be explained | Prediction, observation, proving action, alternative hypotheses and limitations | Activity occurred but the evidence is not yet transferable or reviewable. | Have the learner defend the signal path and repeat a changed case. |
Product evidence / 05
The public product surface exposes runnable examples, capability boundaries, pricing context and test-harness behavior that can be checked before a purchasing decision.
The feature is an independent learning aid, not a firmware emulator, project converter, vendor endorsement or substitute for official software and documentation.
Commissioning notebook / 06
Use these as written briefs rather than click-through instructions. For every case, state the expected condition before acting, retain the first useful observation and explain why the final result proves the requirement. A different program or component choice can still be correct when it produces the same bounded behavior and evidence.
Case 01
predict → observe → prove
Engineering context. source and target platforms, software versions, languages, task model, address or tag model, instruction state, data types, time bases, restart and online workflow. For cross-vendor PLC dialect comparison and transfer, record the initial condition, actor, requested change, observable result and stopping condition before selecting a tool or implementation. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Write the acceptance case” stage of the workflow: convert source and target platforms, software versions, languages, task model, address or tag model, instruction state, data types, time bases, restart and online workflow into initial conditions, one stimulus and observable pass criteria. The acceptance record should show this result: another person can repeat the case without guessing the intended result. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “The expected result is unclear” as one bounded deviation. Inspect requirement, initial state, actor, stimulus, units and pass condition The working interpretation is that the evaluator, instructor and technical buyer may be solving different versions of the task. The next proving action is to rewrite one observable acceptance case before continuing. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is using page completion or an animation as the acceptance criterion. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: What is a PLC programming dialect? A defensible short answer is: It is a platform-specific combination of instruction names, parameters, addressing, data models, task execution and engineering workflow built around common control concepts.
Case 02
predict → observe → prove
Engineering context. control requirement through source semantics, neutral behavior contract, target syntax, owned state, compiled execution and machine result. Separate request, internal state, output or service, physical or user-visible result and independent feedback so each boundary can be inspected. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Build the map” stage of the workflow: document control requirement through source semantics, neutral behavior contract, target syntax, owned state, compiled execution and machine result and name who owns each state or decision. The acceptance record should show this result: every request and result has a source, destination and useful inspection point. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “Internal state changes but the outcome does not” as one bounded deviation. Inspect request, final owner, output or service boundary and independent feedback The working interpretation is that a software or interface indication proves intent at one layer, not the complete outcome. The next proving action is to trace the first boundary after the changing state. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is using the same value as command, status and independent feedback. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: Can PLC code be translated by changing instruction names? A defensible short answer is: Usually not safely. Preserve the intended behavior, identify owned state and retest boundaries such as reset, timing, retention and restart on the target.
Case 03
predict → observe → prove
Engineering context. one start-stop, timer, counter and edge example expressed and tested in two dialects. Run more than one cycle from a known state and retain the values, timings or artifacts that demonstrate repeatability. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Run the baseline” stage of the workflow: apply one start-stop, timer, counter and edge example expressed and tested in two dialects from a clean start and record the expected evidence. The acceptance record should show this result: repeated runs produce the same bounded result. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “Normal case passes but an edge case fails” as one bounded deviation. Inspect limits, timing, simultaneous events, reset and restart assumptions The working interpretation is that the implementation contains a hidden assumption exposed by the changed condition. The next proving action is to add the failed boundary as a permanent regression case. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is changing several parameters before a baseline exists. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: What should I learn first about cross-vendor PLC dialect comparison and transfer? A defensible short answer is: Start with the operating contract and evidence path: source and target platforms, software versions, languages, task model, address or tag model, instruction state, data types, time bases, restart and online workflow, followed by control requirement through source semantics, neutral behavior contract, target syntax, owned state, compiled execution and machine result. Add advanced features only after the baseline is predictable.
Case 04
predict → observe → prove
Engineering context. prescan, first scan, retained memory, skipped calls, time representation, overflow, task period, online edit and power return. Choose minimum, maximum, simultaneous, delayed or restart conditions that reveal assumptions hidden by the happy path. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Challenge assumptions” stage of the workflow: test prescan, first scan, retained memory, skipped calls, time representation, overflow, task period, online edit and power return without changing the acceptance contract. The acceptance record should show this result: limits, timing and restart behavior reach defined states. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “The failure disappears after reset” as one bounded deviation. Inspect original symptom, histories, diagnostics, timestamps and active cause The working interpretation is that reset changed evidence or state without proving the initiating cause. The next proving action is to reproduce under a controlled condition and preserve pre/post-event data. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is testing only one ideal sequence. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: How do I practise cross-vendor PLC dialect comparison and transfer effectively? A defensible short answer is: Use short cases with known initial conditions, a written prediction, one action and an observable result. Then alter a boundary or fault and explain why the evidence changed.
Case 05
predict → observe → prove
Engineering context. a naming, parameter, instance, data-type, scan-order, retention, time-base or workflow mismatch. Preserve the first symptom, divide the system at a measurable boundary and change one condition only after predicting the result. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Isolate one failure” stage of the workflow: introduce or analyse a naming, parameter, instance, data-type, scan-order, retention, time-base or workflow mismatch and locate the first disagreement. The acceptance record should show this result: the proving action distinguishes the leading hypotheses. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “Simulator and target disagree” as one bounded deviation. Inspect model boundary, software version, task timing, I/O behavior, data types and configuration The working interpretation is that a learning model and the intended target do not share one of the recorded assumptions. The next proving action is to reduce the case and verify against current target documentation. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is resetting, forcing or replacing before evidence is retained. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: What counts as proof of competence? A defensible short answer is: A repeatable artifact or system result plus an explanation of the signal path is stronger than time spent, screenshots or a copied answer. Physical competence requires separate supervised evidence.
Case 06
predict → observe → prove
Engineering context. each example rebuilt, compiled and tested in the current official target environment. Restore normal state, remove temporary changes, repeat affected checks and document which claims remain limited to the learning environment. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Close the evidence loop” stage of the workflow: complete each example rebuilt, compiled and tested in the current official target environment and repeat the affected regression cases. The acceptance record should show this result: an evaluation is complete when the same representative job is tested in each candidate and differences are recorded as evidence rather than inferred from feature labels. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “The result cannot be explained” as one bounded deviation. Inspect prediction, observation, proving action, alternative hypotheses and limitations The working interpretation is that activity occurred but the evidence is not yet transferable or reviewable. The next proving action is to have the learner defend the signal path and repeat a changed case. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is treating an acknowledged message or one successful rerun as handover. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: Why test faults and restart behavior? A defensible short answer is: Because a naming, parameter, instance, data-type, scan-order, retention, time-base or workflow mismatch or prescan, first scan, retained memory, skipped calls, time representation, overflow, task period, online edit and power return can expose assumptions that never appear during ideal startup and steady operation.
Answer surface / 07
These concise answers define the operating, training and product boundaries most often missed in broad summaries. The full workflow and diagnostic table above provide the evidence behind them.
It is a platform-specific combination of instruction names, parameters, addressing, data models, task execution and engineering workflow built around common control concepts.
Usually not safely. Preserve the intended behavior, identify owned state and retest boundaries such as reset, timing, retention and restart on the target.
Start with the operating contract and evidence path: source and target platforms, software versions, languages, task model, address or tag model, instruction state, data types, time bases, restart and online workflow, followed by control requirement through source semantics, neutral behavior contract, target syntax, owned state, compiled execution and machine result. Add advanced features only after the baseline is predictable.
Use short cases with known initial conditions, a written prediction, one action and an observable result. Then alter a boundary or fault and explain why the evidence changed.
A repeatable artifact or system result plus an explanation of the signal path is stronger than time spent, screenshots or a copied answer. Physical competence requires separate supervised evidence.
Because a naming, parameter, instance, data-type, scan-order, retention, time-base or workflow mismatch or prescan, first scan, retained memory, skipped calls, time representation, overflow, task period, online edit and power return can expose assumptions that never appear during ideal startup and steady operation.
No. It can build concepts and diagnostic reasoning. Exact firmware, I/O electrical behavior, networking, safety and commissioning require current official tools, documentation and target equipment.
Keep the requirement, initial state, program or configuration, observed values, fault hypothesis, proving action, recovery result and a concise limitations statement.
Continue the signal path / 08
PLC dialect transfer path
Compare address families and mnemonics around the same machine task, then validate the final project in the target vendor tool.