RoboNation Resources
- Model-Based Design
- Vehicle and Environmental Modeling
- Control Design and Simulation
- System Testing and Validation
- Rapid Prototyping and Hardware-in-the Loop Testing
Control Design and Simulation
Introduction
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Once an accurate plant model has been created, closed-loop embedded controller development and open-loop supervisory control strategies can be quickly developed in a single environment using MathWorks tools.
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Simulink Control Design lets you systematically design single and multiloop control systems. You can:
- Automatically tune PID controllers
- Apply linear control design techniques using interactive, graphical techniques such as root locus, Bode, and Nichols charts
- Leverage advanced control design techniques such as MIMO robust control, model predictive control, and fuzzy logic
- Assess key performance parameters, such as overshoot, rise time, and stability
- Trim, linearize, and compute the frequency response of nonlinear Simulink models
- Model and analyze the effects of uncertainty on the performance and stability of your models
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Simulink Design Optimization lets you optimize system performance by automatically tuning design parameters in your Simulink model. You can:
- Optimize controller gains to meet rise time, overshoot, settling time, and steady state error constraints
- Jointly optimize physical plant and algorithmic parameters to maximize overall system performance
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Stateflow provides a graphical environment to model, design and simulate supervisory logic such as task scheduling, energy management, mode selection and fault management. Supervisory control system logic operation can be easily visualized in Stateflow using the debug animation feature.
Control Design Tools
- MATLAB
- Simulink
- Simulink Control Design: Compute PID gains, linearize models, and design control systems
- Simulink Design Optimization: Estimate and optimize Simulink model parameters
- Stateflow: Design and simulate state machines and control logic
- Control System Toolbox: Design and analyze control systems
- Model Predictive Control Toolbox: Design and simulate model predictive controllers
- Robust Control Toolbox: Design robust controllers for plants with uncertain parameters and unmodeled dynamics
- Fuzzy Logic Toolbox: Design and simulate fuzzy logic systems
Tutorials
Videos and Webinars
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Simulink Control Design
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Simulink Design Optimization
