Control Architecture Design
Plan your CONTROL system from natural language specifications. Define goals, constraints, signals, and architecture requirements before moving into automated controller generation.
Harness Agentic AI and control engineering to deliver an end-to-end solution: synthesize, verify, improve, and deploy reliable modular control systems for robotics, aerospace, embedded systems, and mechatronicsβin hours, not weeks.
Enterprise-Grade Integration
Everything you need to design, verify, and deploy production-grade control systems.
Plan your CONTROL system from natural language specifications. Define goals, constraints, signals, and architecture requirements before moving into automated controller generation.
Automated tuning, deployment, feedback collection, and hyperparameter optimization for MPC, PID, LQR, RL-based, and other control systems using an iterative design pattern.
Deliver a modular architecture that allows the integration of required blocks including disturbance observer, parameter estimator, state observer, adaptive elements, and robust termsβinto the control system according to mission requirements.
Verify your design through Model-in-Loop, Software-in-Loop, and Processor-in-Loop testing with automated reports and validation checkpoints.
Generate optimized C/C++ code and deploy directly to your hardware. Support for Raspberry Pi, Arduino, STM32, and more.
Comprehensive simulation environment with scope analysis, performance metrics, and real-time visualization of control behavior.
A streamlined pipeline from specification to deployed system.
Describe your control system requirements in plain English: "Design an adaptive trajectory tracking controller for this quadrotor with minimized control effort."
AI agents decompose requirements, analyze your system dynamics, and propose an optimal control architecture.
Iteratively tune the parameters of each architectural block by leveraging effective feedback from the closed-loop system response, using AI agents empowered by efficient context engineering.
Run MIL, SIL, and PIL tests automatically. Generate traceability reports and safety documentation.
One-click deployment of optimized C/C++ code to your target platform. Real-time monitoring and logging included.
The next evolution in control system design. Not scripting. Not manual tuning. Intelligent, structured engineering.
| Feature | LabCD | Traditional Approach |
|---|---|---|
| Start from Prompt | β | β |
| AI-Powered Synthesis | β | β |
| Model-Based Design | β | β |
| Automatic Code Generation | β | β |
| Integrated Testing | β | β |
| Safety-Critical Ready | β | β |
| Requires Deep Expertise | β | β |
| Weeks to Deploy | β | β |
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Watch how a control system goes from prompt to deployment in minutes.
YouTube video embed would go here β’ 12 minutes
See what control systems experts are saying about LabCD.
"LabCD reduced our development cycle from 12 weeks to 3 weeks. The AI-powered synthesis and automatic testing saved us countless hours of manual tuning."
Control Systems Lead at Robotics Innovations Inc.
"For our safety-critical robotics application, the traceability and safety-ready documentation was invaluable. We deployed with confidence."
Senior Engineer at Autonomous Systems Corp.
"The natural language interface made it accessible to engineers without deep control theory backgrounds. Yet it remained rigorous for advanced users."
Research Director at Mechatronics Lab
Join engineers designing the next generation of robotics and control systems. Start free, no credit card required.
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