Choose the right control approach
Assess Active Disturbance Rejection Control (ADRC), Linear-Quadratic Regulator (LQR), H∞, sliding mode, and other suitable methods against a meaningful baseline.
ADRC · LQR · H∞ · Sliding mode · C++ · MATLAB/Simulink · Python
Control the response.
Even as conditions change.
Control development for vehicles, robots, machines, and mechanisms, with practical implementation and a clear calibration method.
Robust & Optimal Controllers
What the work
can include.
A strong controller must perform beyond a nominal simulation. TwinDroid compares control strategies across disturbances, changing loads, actuator limits, and the operating envelope that matters to your product.
Implement for your workflow
Develop controllers in C++, MATLAB/Simulink, or Python, with attention to interfaces, computational demand, and maintainability.
Test robustness and constraints
Evaluate disturbances, parameter uncertainty, sensor behavior, saturation, and changing operating conditions through agreed scenarios.
Make calibration repeatable
Deliver a documented tuning and calibration method, parameter guidance, and practical evaluation steps your team can reuse.
YOUR HANDOVER
Useful deliverables.
- Controller implementation in the agreed environment
- Baseline comparisons and robustness assessments
- Calibration workflow and parameter guidance
- Documentation and technical handover
A GOOD STARTING POINT
Inputs to discuss.
- Plant model or measurements
- Performance objectives and operating envelope
- Actuator, sensor, and compute constraints
- Existing controller or comparison baseline
HOW WE ASSESS THE RESULT
The controller is assessed using agreed tracking, stability, disturbance-rejection, and implementation criteria. The method is selected for the problem, rather than for its complexity.
START A CONVERSATION
Let’s scope the right first step.
Tell us about your system, the challenge, and your timeline.