AcArman
Chilingaryan
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Control Systems, Robotics & Industrial Automation

Arman Chilingaryan

Senior Control Engineer & Applied Scientist

Making complex systems
behave predictably.

I develop control algorithms and simulation tools that help complex machines operate predictably.

View selected work↓

MATLAB / Simulink · LabVIEW RT / FPGA · State estimation

Inverse kinematics · 6 degrees of freedomLIVE / IK
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Z↑WORLD
Stewart platform · inverse kinematics

Choose a pose. Compare the actuator lengths below.

L10.943 m
L20.943 m
L30.943 m
Original educational demonstration

Selected work

Engineering, made tangible.

Documented areas of experience, paired with original browser experiments. Explore the work, then test the principle yourself.

The experience describes engineering work. The illustrations and laboratories are independent educational models.

5 / 5 · projects shown

Inverted pendulumModel preview

Original educational demonstration

Robotics / feedback control

Finding balance in instability.

Educational and research inverted-pendulum platforms: a concrete way to study feedback, modeling and observation.

  • Cart position
  • Feedback
  • Disturbance
In the laboratory

Move the carriage target. See how feedback keeps the pole upright.

Explore experience +

My contribution

Development work on educational and research control platforms within advanced robotics activities.

Methods & scope

Documented robotics work includes multivariable (MIMO) control, linear-quadratic regulation (LQR), Luenberger observers, tracking and system identification. This brief does not assign every method to every platform.

Engineering deliverables

Control algorithms, simulation models and educational or research tools within the documented platform-development scope.

Available evidence

Experience summarized from the owner-supplied CV brief. The browser lab is independently authored with generic parameters; it does not reproduce or validate original hardware.

Stewart platformModel preview
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Original educational demonstration

Robotics / parallel mechanisms

Six actuators. Coordinated motion.

Stewart mechanisms connect spatial motion, multivariable control and the practical challenge of a parallel robot.

  • 6 DOF pose
  • Inverse kinematics
  • Actuator stroke
In the laboratory

Change position and orientation. Inspect all six actuator lengths and stroke limits.

Explore experience +

My contribution

Development of educational and research platforms for Stewart mechanisms.

Methods & scope

Parallel-mechanism modeling and control within the documented robotics work. The linked lab specifically demonstrates pose-to-actuator inverse kinematics.

Engineering deliverables

Modeling, control-development and research-platform tools; original hardware photographs and project-specific measurements are not supplied.

Available evidence

Experience summarized from the owner-supplied CV brief. The browser lab is independently authored with generic parameters; it does not reproduce or validate original hardware.

Gimbal stabilizationModel preview

Original educational demonstration

Stabilization / orientation

Keeping a direction in a moving world.

Precision gyro-stabilized platforms bring orientation, measurement and control together.

  • Nested axes
  • Tracking error
  • Joint limits
In the laboratory

Rotate the base. Compare world-lock and body-follow tracking.

Explore experience +

My contribution

Participation in grant-funded work involving precision gyro-stabilized platforms and related inertial-navigation concepts.

Methods & scope

Related expertise includes quaternion attitude estimation, sensor fusion and real-time signal processing; the lab isolates actuator tracking from estimation.

Engineering deliverables

Engineering algorithms, modeling and integration within collaborative research. No unsupported stabilization-accuracy claim is made.

Available evidence

Experience summarized from the owner-supplied CV brief. The browser lab is independently authored with generic parameters; it does not reproduce or validate original hardware.

Drone flight controlModel preview
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Original educational demonstration

Autonomous systems / flight control

From a flight objective to feedback.

Adaptive autopilot and inertial-navigation research connects several layers of control and estimation.

  • Attitude
  • Flight modes
  • Motor thrust
In the laboratory

Command position, roll or pitch. Follow the airframe, thrust and counterrotating propellers.

Explore experience +

My contribution

Participation in grant-funded work on adaptive autopilots and inertial-navigation concepts.

Methods & scope

Related expertise: MATLAB/Simulink/Simscape, quaternion orientation and extended/error-state Kalman filtering (EKF/ESKF). The browser lab uses ideal state feedback.

Engineering deliverables

Control and simulation engineering within collaborative work. The independent generic quadrotor lab contains no customer flight data.

Available evidence

Experience summarized from the owner-supplied CV brief. The browser lab is independently authored with generic parameters; it does not reproduce or validate original hardware.

Pumpjack & rod-pump explorerModel preview

Original educational demonstration

Industrial automation / oil & gas

Understanding the whole pumping system.

Sucker-rod pumping, physical modeling, dynamometer cards and real-time condition monitoring.

  • Closed linkage
  • Downhole pump
  • Dynamometer cards
In the laboratory

Follow the linkage down to the plunger. Compare valve timing and liquid supply.

Explore experience +

My contribution

Oil-well pump control and physical models of rod-pumping units, including mechanism kinematics/dynamics and real-time monitoring.

Methods & scope

Surface and downhole dynamometer-card calculations with wave-equation methods, deviated-well considerations, leakage calculations and AI-supported condition classification.

Engineering deliverables

Models, control and diagnostic software, monitoring workflows and engineering integration. Pumping-station capabilities are shown as a generic educational example; no specific delivered station is claimed.

Available evidence

Experience summarized from the owner-supplied CV brief. The browser lab is independently authored with generic parameters; it does not reproduce or validate original hardware.

IMU / SIGNALS

InertiaStudio / IMU Analyzer

My own engineering project for inertial measurement unit (IMU) signal analysis and diagnostics. The sensor lab below is an original synthetic example, not a product preview.

CONFIDENTIAL / EXPERIENCE

Rotorcraft control & simulation

Confidential engineering engagement. High-level experience only; customer models, parameters, screenshots and results are not published.

Experience overview

Continue with the fundamentals

Study the controller, the measurements and the process behind the machine.

Expertise

Engineering capabilities. Connected to the task.

I connect models, measurements and feedback across robotics and industrial systems. Each area below shows the engineering question, the work it produces and the methods behind it.

See how I work

The independent browser laboratories illustrate individual engineering principles.

Control & system identification

How should a system track a command and respond to disturbances? I connect a physical model to a feedback strategy.

Engineering output

System models, controller algorithms and scenarios for assessing stability and tracking.

Methods & tools
  • Multivariable control (MIMO), linear-quadratic regulation (LQR), Luenberger observers and system identification are part of the documented robotics experience.
  • The PID laboratory isolates a controller and a simple plant so the effect of tuning can be explored.
Educational example

Robotics & mechatronics

How do coordinated actuators create the required motion? My experience includes educational and research Stewart and inverted-pendulum platforms.

Engineering output

Mechanism models, motion-control algorithms and tools for studying coordinated movement.

Methods & tools
  • Kinematics, trajectory tracking and the relationship between mechanism coordinates and actuator commands.
  • The Stewart laboratory demonstrates pose-to-actuator inverse kinematics with stroke constraints.
Educational example

Flight control & stabilization

How can orientation be maintained while the vehicle or its base moves? Collaborative research connects autopilots, inertial navigation and stabilized platforms.

Engineering output

Control and simulation algorithms, orientation calculations and engineering integration.

Methods & tools
  • Quaternion-based orientation, control cascades and real-time signal processing.
  • Grant-funded work includes adaptive-autopilot and precision gyro-stabilized-platform research. The gimbal example isolates servo tracking.
Educational example

Simulation & state estimation

What can measurements tell us about motion that cannot be observed directly? I build simulation and signal-analysis tools to study the answer.

Engineering output

Simulation models, state-estimation algorithms and repeatable signal comparisons.

Methods & tools
  • MATLAB, Simulink and Simscape; sensor fusion and inertial measurement unit (IMU) analysis.
  • Related expertise includes extended Kalman filtering (EKF) and error-state Kalman filtering (ESKF). The browser example uses a complementary filter.
Educational example

Industrial automation & diagnostics

How do mechanism physics, process measurements and operator information connect? My industrial experience includes rod-pumping models and condition monitoring.

Engineering output

Control and diagnostic software, monitoring tools and engineering interfaces.

Methods & tools
  • Surface and downhole dynamometer-card calculations, wave-equation methods, leakage assessment and vibration analysis.
  • LabVIEW Real-Time/FPGA, MATLAB/Simulink, supervisory control and data acquisition (SCADA), EtherNet/IP and Modbus.
  • The pumping-station laboratory is an independent educational process-control example.
Educational example

Applied AI & engineering assistance

Where can classification and repeatable analysis help interpret engineering data? I use the question and the available evidence to define the scope.

Engineering output

Condition-classification methods and digital models within the documented industrial work.

Methods & tools
  • Applied experience includes AI-supported dynamometer-card and operating-condition classification.
  • Analysis scripts, simulation review, documentation and code review are possible supporting AI workflows, without a claim of completed client AI projects.
Explore the professional experience

Industrial automation

From the well to the operator.

Control engineering connects the mechanism, the process and the information an operator needs. My documented industrial work includes rod-pump models, dynamometer-card calculations and condition monitoring.

Conceptual system overview. Original illustration; no connection to live equipment.

Mechanism → well

A surface pumpjack moves a rod string and downhole plunger pump. Models connect kinematics, loads, operating modes and control. Sucker-rod pumps are distinct from electric submersible pumps.

Pump → process

Generic pumping-station engineering: pressure or level objectives, drive coordination, instrumentation, operator modes and process alarms. The lab controls pressure with a source-level interlock.

Signals → condition

Surface and downhole dynamometer cards, wave-equation calculations, leakage assessment, condition classification and vibration analysis support understanding of equipment state.

Control → operator

Real-time acquisition and controller algorithms connect sensors, motor controllers and variable-frequency drives to supervisory control and data acquisition (SCADA), trends and event history.

Engineering lab

Change an input. Understand the response.

Explore nine original, calculated experiments. Every visualization follows the same state as its plots and measurements.

Robotics
Industrial Lab
Fundamentals
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Experiment / 6 DOF

Six legs. One pose.

Move a platform in six directions and discover how its pose determines six actuator lengths. This is constrained inverse kinematics, not a dynamic stabilization model.

How I work

A defined result at every stage.

Start with the system and its objective. Build a model, design the feedback, assess behavior and prepare evidence for implementation.

  1. Understand the system

    Clarify the machine, its operating modes and the behavior that matters. Agree what can be measured and how success will be assessed.

    Questions & checks
    • What should the system do in each operating mode?
    • Which measurements and interfaces are available?
    • Which observations would demonstrate that the objective is met?
    Stage output

    Problem definition, interfaces, constraints and acceptance criteria.

  2. Build the model

    Choose the physics and level of detail needed to answer the question. Identify parameters and make the assumptions explicit.

    Questions & checks
    • Are coordinates, units and sign conventions consistent?
    • Which parameters are known and which need identification?
    • Where does the model stop representing the real system?
    Stage output

    A system model with parameters, assumptions and a stated range of use.

  3. Design the feedback

    Connect the objective to a control strategy. Separate the plant, state estimation, controller and actuator constraints.

    Questions & checks
    • Which signals are measured, estimated and commanded?
    • How do the control loops interact?
    • Where do actuator saturation, rate limits and mode changes matter?
    Stage output

    A control architecture, algorithms and a set of tunable parameters.

  4. Assess the behavior

    Compare calculated responses in useful scenarios. Include disturbances and constraints, then inspect errors, control effort and numerical behavior.

    Questions & checks
    • Which nominal and disturbed scenarios are representative?
    • What happens at the relevant limits or faults?
    • Are conclusions supported by the response, metrics and numerical checks?
    Stage output

    Simulation comparisons and a record of assumptions, limits and unresolved questions.

  5. Prepare implementation & verification

    Support integration with clear interfaces and repeatable checks. Record the conditions and evidence needed to assess an implementation.

    Questions & checks
    • Can another engineer reproduce the check?
    • Are units, timing, interfaces and configuration recorded?
    • Does each acceptance criterion have a corresponding check and result?
    Stage output

    Integration notes, reproducible test scenarios and verification documentation.

The depth of each stage depends on the system, the available data and the agreed scope.

Start with the engineering question.

Describe the system, the intended behavior and the constraints. That gives us a concrete starting point for discussion.

Discuss an engineering taskExplore the laboratories

About

Control engineering. From models to implementation.

I’m Arman Chilingaryan, a control engineer and applied scientist. I develop models, feedback algorithms and engineering tools across robotics and industrial systems. My work connects physical behavior, measurements and implementation.

Engineering practice

Robotics, flight & stabilization

Development of educational and research platforms for Stewart mechanisms and inverted pendulums. Participation in grant-funded work on adaptive autopilots, inertial-navigation concepts and precision gyro-stabilized platforms.

Explore the project experience

Industrial automation & diagnostics

Oil-well pump control, physical models of sucker-rod pumping units and real-time condition monitoring. Work includes surface and downhole dynamometer-card calculations, wave-equation methods, leakage assessment and condition classification.

Explore industrial engineering

Experience milestones

  1. Scientific & technical collaboration

    The start of collaborative scientific and technical work.

  2. Engineering work with Ovak Technologies

    Control, modeling and engineering integration within the documented experience.

Years mark the start of these activities. They do not establish a current employment status.

How AI fits into my engineering work

Applied experience

Industrial condition classification and digital modeling are part of the documented engineering work.

Supporting workflows

AI assistance can support analysis scripts, simulation review, documentation and code review. These are possible uses, without a claim of completed client AI projects.

Contact

Let’s discuss your engineering task.

For control development, modeling, diagnostics and applied research. Send a short description of the system and the question you want to resolve.

A useful starting point

  • The system and its operating context.
  • The behavior or engineering goal you want to achieve.
  • Available measurements, models and practical constraints.