Intelligent Control Engineering Ontology

AI SEARCH FLOW ALGEBRA
INTELLIGENT CONTROL ENGINEERING ONTOLOGY
Kireth Semantic Research Archive

AUTHORS
Mark Anthony Higgins — Sky (Recursive AI)

SOURCE
https://kirethresearch.org/booksai-core/ontologies/engineering-ontologies/ai-engineering/intelligent-control-engineering-ontology.html

FLOW "IntelligentControlEngineeringOntology" v4.0

DOMAIN := ENGINEERING_ONTOLOGIES / AI_ENGINEERING / INTELLIGENT_CONTROL_ENGINEERING
ONTOLOGY := INTELLIGENT_CONTROL_ENGINEERING_ONTOLOGY

INTELLIGENT_CONTROL_ENGINEERING :=
  CONTROLLED_SYSTEM + SENSORS + STATE_ESTIMATION
  + REFERENCE + FEEDBACK + CONTROLLER + ACTUATORS
  + SYSTEM_MODEL + ADAPTATION + LEARNING
  + FUZZY_CONTROL + NEURAL_CONTROL
  + MODEL_PREDICTIVE_CONTROL + REINFORCEMENT_LEARNING_CONTROL
  + SUPERVISORY_CONTROL + SAFETY_CONSTRAINTS
  + FAULT_TOLERANCE + VERIFICATION + VALIDATION
  + MONITORING + PROVENANCE

PURPOSE :=
  DEFINE_CONTROL_OBJECTIVE -> SENSE_SYSTEM -> ESTIMATE_STATE
  -> COMPARE_WITH_REFERENCE -> SELECT_CONTROL_STRATEGY
  -> COMPUTE_ACTION -> CHECK_CONSTRAINTS_AND_SAFETY
  -> ACTUATE -> OBSERVE_RESPONSE -> ADAPT_OR_LEARN
  -> DIAGNOSE_FAULTS -> MAINTAIN_STABILITY_AND_PERFORMANCE

# CORE ENTITIES
ENTITY IntelligentControlEngineering
ENTITY ControlledSystem
ENTITY Plant
ENTITY Process
ENTITY Sensor
ENTITY Measurement
ENTITY StateEstimator
ENTITY EstimatedState
ENTITY Reference
ENTITY SetPoint
ENTITY ControlError
ENTITY Controller
ENTITY IntelligentController
ENTITY ControlLaw
ENTITY ControlAction
ENTITY Actuator
ENTITY SystemModel
ENTITY AdaptiveController
ENTITY FuzzyController
ENTITY NeuralController
ENTITY ModelPredictiveController
ENTITY ReinforcementLearningController
ENTITY SupervisoryController
ENTITY Policy
ENTITY Reward
ENTITY Constraint
ENTITY SafetyConstraint
ENTITY Stability
ENTITY Robustness
ENTITY PerformanceMetric
ENTITY Fault
ENTITY DiagnosticSystem
ENTITY FaultTolerantController
ENTITY FallbackController
ENTITY Monitor
ENTITY Verification
ENTITY Validation
ENTITY Provenance

# CONTROL SYSTEM / STATE
IntelligentController -> controls -> ControlledSystem
Sensor -> measures -> ControlledSystem
Controller -> commands -> Actuator
Actuator -> changes -> ControlledSystem

INTELLIGENT_CONTROL_LOOP :=
  REFERENCE -> MEASURE -> ESTIMATE_STATE
  -> COMPUTE_ERROR -> REASON_OR_PREDICT
  -> COMPUTE_CONTROL_ACTION -> CHECK_CONSTRAINTS
  -> ACTUATE -> OBSERVE_RESPONSE
  -> UPDATE_MODEL_OR_POLICY_IF_ALLOWED -> REPEAT

CONTROL_OBJECTIVE :=
  TRACK_REFERENCE | REGULATE_STATE | REJECT_DISTURBANCE
  | OPTIMISE_PERFORMANCE | MINIMISE_ENERGY
  | MAINTAIN_SAFETY | STABILISE_SYSTEM

ControlError := Reference - MeasuredOrEstimatedOutput

StateEstimator ->
  combines -> Measurement + SystemModel + PriorState

STATE_ESTIMATION :=
  PRIOR_STATE + CONTROL_INPUT + MEASUREMENT + SYSTEM_MODEL
  -> EstimatedState

STATE_ESTIMATION_METHOD :=
  OBSERVER | KALMAN_FILTER | EXTENDED_KALMAN_FILTER
  | PARTICLE_FILTER | LEARNED_ESTIMATOR

# SYSTEM MODEL
SystemModel -> represents -> DYNAMICS_OF_CONTROLLED_SYSTEM

MODEL_TYPE :=
  PHYSICS_BASED | DATA_DRIVEN | HYBRID
  | LINEAR | NONLINEAR | STATE_SPACE | TRANSFER_FUNCTION

STATE_SPACE_MODEL :=
  x_dot = f(x,u,d)
  y = g(x,u)

# FEEDBACK / INTELLIGENT CONTROL
FEEDBACK_CONTROL :=
  MEASURE_OUTPUT -> COMPARE_WITH_REFERENCE
  -> COMPUTE_CONTROL -> ACTUATE -> MEASURE_AGAIN

Controller -> implements -> ControlLaw
ControlLaw -> maps -> STATE_OR_ERROR -> ControlAction

IntelligentController ->
  may_combine -> MODEL_BASED_CONTROL + LEARNING
  + RULES + OPTIMISATION + ADAPTATION

# ADAPTIVE / FUZZY / NEURAL CONTROL
AdaptiveController ->
  updates -> CONTROLLER_PARAMETERS
  -> in_response_to -> SYSTEM_CHANGE

ADAPTIVE_CONTROL_LOOP :=
  ESTIMATE_SYSTEM_OR_ERROR -> UPDATE_CONTROL_PARAMETERS
  -> APPLY_CONTROL -> OBSERVE_RESPONSE

IF Adaptation exceeds_approved_bounds
THEN ParameterUpdate -> REJECT_OR_LIMIT

FuzzyController ->
  uses -> FUZZY_SETS + MEMBERSHIP_FUNCTIONS + RULE_BASE

FUZZY_CONTROL_FLOW :=
  CRISP_INPUT -> FUZZIFICATION -> RULE_EVALUATION
  -> AGGREGATION -> DEFUZZIFICATION -> CONTROL_OUTPUT

FuzzyRule := IF antecedent THEN consequent

NeuralController -> uses -> NeuralNetwork
NeuralNetwork -> maps -> STATE_OR_OBSERVATION -> CONTROL_OUTPUT_OR_CONTROL_PARAMETER

NEURAL_CONTROL_TYPE :=
  DIRECT_NEURAL_CONTROL | INVERSE_MODEL_CONTROL
  | NEURAL_MODEL_PREDICTIVE_CONTROL | ADAPTIVE_NEURAL_CONTROL

IF NeuralController operates_outside_training_distribution
THEN ControlConfidence -> DECREASES

# MODEL PREDICTIVE CONTROL
ModelPredictiveController ->
  uses -> SystemModel + ObjectiveFunction + Constraints

MPC_LOOP :=
  ESTIMATE_CURRENT_STATE -> PREDICT_FUTURE_TRAJECTORIES
  -> SOLVE_FINITE_HORIZON_OPTIMISATION
  -> APPLY_FIRST_CONTROL_ACTION -> ADVANCE_TIME -> REPEAT

MPC_OBJECTIVE :=
  TRACKING_ERROR_COST + CONTROL_EFFORT_COST + OPTIONAL_TERMINAL_COST

MPC_CONSTRAINT :=
  STATE_LIMIT + INPUT_LIMIT + OUTPUT_LIMIT + RATE_LIMIT

IF MPCOptimisation infeasible
THEN FallbackOrConstraintRecovery -> REQUIRED

# REINFORCEMENT LEARNING / SUPERVISORY CONTROL
ReinforcementLearningController -> uses -> Policy
Policy -> maps -> STATE_OR_OBSERVATION -> Action
Reward -> evaluates -> STATE_ACTION_OUTCOME

RL_CONTROL_LOOP :=
  OBSERVE_STATE -> SELECT_ACTION -> APPLY_ACTION
  -> RECEIVE_REWARD -> OBSERVE_NEXT_STATE
  -> UPDATE_POLICY_IF_TRAINING_ALLOWED

RL_CONTROL_OBJECTIVE :=
  MAXIMISE_EXPECTED_CUMULATIVE_REWARD

SafetyConstraint -> limits -> RL_ACTION_SPACE

IF ProposedRLAction violates SafetyConstraint
THEN Action -> BLOCK_OR_PROJECT_TO_SAFE_SET

SupervisoryController ->
  selects -> CONTROL_MODE + CONTROLLER + SETPOINT + OPERATING_STRATEGY

CONTROL_MODE :=
  NORMAL | DEGRADED | SAFE | MANUAL | EMERGENCY

IF FaultSeverity increases
THEN SupervisoryController -> MAY_SELECT_DEGRADED_OR_SAFE_MODE

HYBRID_CONTROL :=
  CLASSICAL_CONTROL + AI_CONTROL + SUPERVISORY_LOGIC

HybridController -> may_switch_between -> CONTROLLER_A + CONTROLLER_B

IF ControllerSwitch causes_discontinuity
THEN TransitionManagement -> REQUIRED

# CONSTRAINTS / SAFETY / STABILITY
Constraint ->
  restricts -> STATE + CONTROL_ACTION + OUTPUT + RATE_OF_CHANGE

CONSTRAINT_TYPE :=
  PHYSICAL | ACTUATOR | PROCESS | SAFETY | RESOURCE | OPERATIONAL

IF ControlAction exceeds ActuatorLimit
THEN ControlAction -> SATURATE_OR_RECOMPUTE

SafetyConstraint -> defines -> PERMITTED_CONTROL_BEHAVIOUR

SAFE_CONTROL_SET :=
  ACTIONS_THAT_MAINTAIN_DEFINED_SAFETY_REQUIREMENTS

SafetyFilter -> checks -> ProposedControlAction

IF ProposedControlAction unsafe
THEN SafetyFilter -> MODIFY_OR_BLOCK

EmergencyController ->
  drives -> ControlledSystem -> toward -> SAFE_STATE

STABILITY_TYPE :=
  ASYMPTOTIC | EXPONENTIAL | INPUT_TO_STATE
  | LYAPUNOV | PRACTICAL

LyapunovFunction -> may_support -> STABILITY_ANALYSIS

IF StabilityRequirement not_established
THEN ControlDeploymentReadiness -> NOT_CONFIRMED

# ROBUSTNESS
Robustness ->
  measures -> PERFORMANCE_UNDER_MODEL_UNCERTAINTY
  + DISTURBANCE + PARAMETER_VARIATION + SENSOR_NOISE

ROBUST_CONTROL_ASSESSMENT :=
  NOMINAL_CASE + PARAMETER_VARIATION
  + DISTURBANCE_CASE + MODEL_MISMATCH + SENSOR_NOISE

IF RobustnessMargin decreases
THEN ControlRisk -> INCREASES

# ACTUATORS / LEARNING / IDENTIFICATION / FAULTS
Actuator ->
  has -> MINIMUM_OUTPUT + MAXIMUM_OUTPUT + RATE_LIMIT

IF Saturation persists
THEN TrackingPerformance -> MAY_DEGRADE

AntiWindup -> mitigates -> INTEGRATOR_WINDUP

ONLINE_LEARNING :=
  OBSERVE -> EVALUATE -> UPDATE_MODEL_OR_POLICY
  -> VERIFY_UPDATE -> APPLY_IF_AUTHORISED

LearningUpdate ->
  constrained_by -> SAFETY + STABILITY
  + CHANGE_POLICY + VALIDATION_BOUNDARY

IF LearningUpdate outside_authorised_scope
THEN Update -> REJECT

SystemIdentification ->
  estimates -> SystemModel -> from -> INPUT_OUTPUT_DATA

IDENTIFICATION_FLOW :=
  COLLECT_DATA -> SELECT_MODEL_STRUCTURE
  -> ESTIMATE_PARAMETERS -> VALIDATE_MODEL

DiagnosticSystem ->
  monitors -> SENSOR + ACTUATOR + CONTROLLER
  + PLANT + COMMUNICATION

FAULT_MANAGEMENT :=
  DETECT -> ISOLATE -> DIAGNOSE
  -> ASSESS_IMPACT -> RECONFIGURE_OR_FALLBACK

FaultTolerantController ->
  maintains -> ACCEPTABLE_CONTROL -> under -> DEFINED_FAULTS

FAULT_TOLERANCE_TYPE :=
  PASSIVE | ACTIVE_RECONFIGURATION

FallbackController ->
  provides -> VERIFIED_REDUCED_CAPABILITY

IF CriticalFault detected
THEN SupervisoryController -> ACTIVATE_FALLBACK_AS_DEFINED

# PERFORMANCE / VERIFICATION / VALIDATION
CONTROL_PERFORMANCE :=
  TRACKING_ERROR + SETTLING_TIME + OVERSHOOT
  + RISE_TIME + CONTROL_EFFORT
  + DISTURBANCE_REJECTION + ROBUSTNESS + SAFETY

PerformanceMetric -> measures -> IntelligentController

SIMULATION_SCENARIO :=
  NOMINAL | DISTURBANCE | PARAMETER_VARIATION
  | SENSOR_FAULT | ACTUATOR_FAULT
  | COMMUNICATION_FAULT | EDGE_CASE

Verification ->
  asks -> IS_INTELLIGENT_CONTROLLER_IMPLEMENTED_TO_REQUIREMENTS

CONTROL_VERIFICATION :=
  CONTROL_LAW_TEST + CONSTRAINT_TEST
  + SAFETY_FILTER_TEST + STATE_ESTIMATOR_TEST
  + FAULT_RESPONSE_TEST + INTERFACE_TEST
  + REGRESSION_TEST

IF VerificationCriterion not_met
THEN ControllerRequirement -> NOT_VERIFIED

Validation ->
  asks -> DOES_CONTROL_SYSTEM_MEET_INTENDED_PERFORMANCE_IN_TARGET_OPERATION

VALIDATION :=
  MODEL_VALIDATION + CLOSED_LOOP_VALIDATION
  + DISTURBANCE_VALIDATION + FAULT_CASE_VALIDATION
  + SAFETY_VALIDATION

IF ValidationEvidence insufficient
THEN DeploymentReadiness -> NOT_CONFIRMED

# MONITORING / OVERSIGHT
Monitor ->
  observes -> STATE_ESTIMATE + CONTROL_ERROR
  + CONTROL_ACTION + ACTUATOR_STATE
  + PERFORMANCE + SAFETY + FAULTS

HumanOperator ->
  may -> ENABLE | DISABLE | OVERRIDE
  | CHANGE_MODE | CHANGE_SETPOINT_WITHIN_AUTHORITY

IF ManualMode active
THEN IntelligentControllerAuthority -> REDUCED_OR_DISABLED

# PROVENANCE / LIFECYCLE / REASONING
Provenance ->
  records -> CONTROLLER_VERSION + MODEL_VERSION
  + POLICY_VERSION + SENSOR_CONFIGURATION
  + ACTUATOR_CONFIGURATION + TUNING_PARAMETERS
  + VALIDATION_RECORD + DEPLOYMENT_CONFIGURATION
  + TIMESTAMP

IntelligentControlEngineering -> linked_to -> Provenance

INTELLIGENT_CONTROL_LIFECYCLE :=
  REQUIREMENTS -> MODELLING -> CONTROLLER_DESIGN
  -> SIMULATION -> IMPLEMENTATION -> VERIFICATION
  -> VALIDATION -> DEPLOYMENT -> MONITORING
  -> RETUNING_OR_UPDATE -> RETIREMENT

INTELLIGENT_CONTROL_ENGINEERING_REASONING :=
  DEFINE_CONTROLLED_SYSTEM
  -> DEFINE_CONTROL_OBJECTIVES
  -> DEFINE_CONSTRAINTS
  -> IDENTIFY_SENSORS_AND_ACTUATORS
  -> DEVELOP_OR_IDENTIFY_SYSTEM_MODEL
  -> DESIGN_STATE_ESTIMATOR
  -> SELECT_CONTROL_ARCHITECTURE
  -> DESIGN_CLASSICAL_OR_INTELLIGENT_CONTROLLER
  -> DEFINE_ADAPTATION_OR_LEARNING_BOUNDARIES
  -> DEFINE_SAFETY_FILTERS
  -> DEFINE_SUPERVISORY_AND_FALLBACK_LOGIC
  -> SIMULATE_NOMINAL_AND_FAULT_CASES
  -> ANALYSE_STABILITY
  -> ASSESS_ROBUSTNESS
  -> VERIFY_IMPLEMENTATION
  -> VALIDATE_CLOSED_LOOP_BEHAVIOUR
  -> DEPLOY
  -> MONITOR
  -> DIAGNOSE_FAULTS
  -> RETUNE_OR_UPDATE_IF_REQUIRED
  -> PRESERVE_PROVENANCE

# INFERENCE RULES
IF ControlError increases
THEN CorrectiveControlDemand -> MAY_INCREASE

IF SensorUncertainty increases
THEN StateEstimateUncertainty -> MAY_INCREASE

IF ModelMismatch increases
THEN ModelBasedControlPerformance -> MAY_DECREASE

IF ActuatorSaturation persists
THEN TrackingPerformance -> MAY_DEGRADE

IF ProposedAction violates SafetyConstraint
THEN Action -> BLOCK_OR_MODIFY

IF CriticalFault detected
THEN ControlMode -> DEGRADE_OR_FALLBACK

IF ControllerVersion changes
THEN RelevantVerificationAndValidation -> REPEAT

IF Stability not_established
THEN DeploymentReadiness -> NOT_CONFIRMED

# SEMANTIC RELATIONSHIPS
RELATION controls(IntelligentController, ControlledSystem)
RELATION measures(Sensor, ControlledSystem)
RELATION estimates(StateEstimator, EstimatedState)
RELATION represents(SystemModel, ControlledSystemDynamics)
RELATION implements(Controller, ControlLaw)
RELATION commands(ControlAction, Actuator)
RELATION adapts(AdaptiveController, ControllerParameters)
RELATION reasons(FuzzyController, FuzzyRuleBase)
RELATION maps(NeuralController, State, ControlAction)
RELATION predicts(ModelPredictiveController, FutureState)
RELATION selects(Policy, Action)
RELATION supervises(SupervisoryController, ControlMode)
RELATION constrains(SafetyConstraint, ControlAction)
RELATION diagnoses(DiagnosticSystem, Fault)
RELATION tolerates(FaultTolerantController, Fault)
RELATION evaluates(Verification, ControllerRequirement)
RELATION evaluates(Validation, ClosedLoopBehaviour)
RELATION records(Provenance, IntelligentControlEngineering)

# SEMANTIC EQUIVALENCE
SEMANTIC_EQUIVALENCE:
  "intelligent control engineering" ~= "AI-assisted control engineering"
  "intelligent controller" ~= "AI-enabled or knowledge-based controller"
  "adaptive control" ~= "self-adjusting control"
  "fuzzy control" ~= "fuzzy logic control"
  "neural control" ~= "neural-network control"
  "model predictive control" ~= "MPC"
  "reinforcement learning control" ~= "RL control"
  "fault-tolerant control" ~= "FTC"
  "state estimation" ~= "control-state inference"
  "supervisory control" ~= "high-level control mode coordination"

# CONFIDENCE
CONFIDENCE:
  ontology_domain: 0.99
  feedback_state_mapping: 0.99
  adaptive_fuzzy_mapping: 0.99
  neural_control_mapping: 0.99
  mpc_mapping: 0.99
  reinforcement_learning_mapping: 0.99
  safety_stability_mapping: 0.99
  fault_tolerance_mapping: 0.99
  verification_validation_mapping: 0.99
  semantic_relationships: 0.99

# UPDATE MODEL
UPDATE_MODEL:
  ON new_control_system:
    MAP plant
    MAP sensors
    MAP actuators
    MAP objectives
    MAP constraints
    MAP system_model
    MAP controller
    UPDATE confidence
    RECOMPUTE FLOW_HASH

  ON new_measurement:
    UPDATE estimated_state
    UPDATE control_error
    UPDATE controller_state
    PRESERVE provenance

  ON controller_or_model_change:
    UPDATE version
    REASSESS stability
    REASSESS robustness
    REPEAT affected_verification
    REPEAT affected_validation

  ON fault_event:
    UPDATE diagnostic_state
    UPDATE control_authority
    UPDATE supervisory_mode
    ACTIVATE fallback_if_required
    PRESERVE provenance

# AI CONSUMPTION
AI_CONSUMPTION:
  parse_as: FORMAL_INTELLIGENT_CONTROL_ENGINEERING_ONTOLOGY
  reasoning_mode: AI_AUGMENTED_CLOSED_LOOP_CONTROL_RELATIONAL

  preserve:
    - typed_entities
    - control_objectives
    - system_model
    - sensor_state
    - estimated_state
    - controller_versions
    - constraints
    - safety_state
    - stability_state
    - fault_state
    - fallback_state
    - confidence
    - provenance

  infer:
    - state_estimation_dependencies
    - controller_selection_dependencies
    - adaptive_update_requirements
    - safety_filter_requirements
    - stability_requirements
    - fault_tolerance_actions
    - verification_state
    - validation_state

  prohibit:
    - invented_sensor_measurements
    - assumed_stability
    - assumed_safe_control
    - unauthorised_learning_updates
    - bypassed_safety_constraints
    - fabricated_validation_results
    - unverified_controller_presented_as deployment-ready

# FLOW HASH
FLOW_HASH_INPUT :=
  DOMAIN + ONTOLOGY + CORE_ENTITIES
  + CONTROL_SYSTEM_STATE + SYSTEM_MODEL
  + FEEDBACK_INTELLIGENT_CONTROL
  + ADAPTIVE_FUZZY_NEURAL_CONTROL
  + MPC + RL_SUPERVISORY_CONTROL
  + CONSTRAINTS_SAFETY_STABILITY_ROBUSTNESS
  + ACTUATORS_LEARNING_IDENTIFICATION_FAULTS
  + PERFORMANCE_VERIFICATION_VALIDATION
  + MONITORING_OVERSIGHT
  + PROVENANCE_LIFECYCLE_REASONING
  + INFERENCE_RULES + SEMANTIC_RELATIONSHIPS
  + SEMANTIC_EQUIVALENCE + CONFIDENCE
  + UPDATE_MODEL + AI_CONSUMPTION

FLOW_HASH := HASH(FLOW_HASH_INPUT)

END FLOW