Control Systems

Control system engineers use MATLAB and Simulink at all stages of development – from plant modeling to designing and tuning control algorithms and supervisory logic, all the way to deployment with automatic code generation and system verification, validation, and test.

A multi-domain block diagram environment for modeling plant dynamics, designing control algorithms, and running closed-loop simulations.

Plant modeling using system identification or physical modeling tools.

Prebuilt functions and interactive tools for analyzing overshoot, rise time, phase margin, gain margin, and other performance and stability characteristics in time and frequency domains.

Root locus, Bode diagrams, LQR, LQG, robust control, model predictive control, and other design and analysis techniques.

Automatic tuning of PID, gain-scheduled, and arbitrary SISO and MIMO control systems.

Modeling, design, and simulation of supervisory logic for performing scheduling, mode switching, and fault detection, isolation, and recovery (FDIR) 

Model and Simulate Plant Dynamics

Use MATLAB and Simulink to build accurate plant models. Describe the complex dynamics of your plant using a variety of supported modeling approaches, and use the most appropriate approach for each component in your plant to create the system-level plant model.

Estimate plant dynamics from input-output data using system identification when you do not know the detailed structure of the model.  Alternatively, create complex multidomain plant models without having to derive the underlying first-principles equations using physical modeling tools. Use blocks that represent mechanical, electrical, magnetic, hydraulic, pneumatic, and thermal components to map the component topography and physical connections of your system. 

Design and Tune Feedback Compensators

Analyze and develop closed-loop compensators, and assess key performance parameters, such as overshoot, rise time, and stability margins. Trim and linearize nonlinear Simulink models. You can also model and analyze the effects of uncertainty on the performance and stability of your models.

Take advantage of Bode plots, root locus, and other linear control design techniques and automatically tune PID controllers in a simulation model or on test hardware. Prebuilt tools let you automatically tune decentralized multivariable controllers and leverage advanced control strategies, such as model predictive control and robust control. Use optimization methods to compute controller gains to meet rise-time and overshoot constraints.

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