Motor and Power Control Design with Simulink

Engineers developing motor control, battery management, and power conversion systems reduce their efforts by using MATLAB®, Simulink®, and Model-Based Design (3:17) . These engineers develop their software algorithms before implementing them in hardware by:

  • Validating control algorithms through desktop and real-time simulation of the system dynamics
  • Optimizing system behavior using model libraries of energy sources and loads, power semiconductors, and a variety of circuit elements
  • Eliminating design problems found using simulation before moving to implementation
  • Testing and verifying designs with MATLAB and Simulink test harnesses
  • Generating HDL or C code from models for prototyping and implementation
  • Reusing models to speed up design iterations and next-generation projects

Learn more about motor and power control design with Simulink:


Motor Control

Motor control algorithms regulate speed, torque, and other performance characteristics. These algorithms help with energy efficiency, precision control, and system protection. You can use simulation to evaluate control algorithms in order to determine the suitability of motor controller designs. This reduces the time and cost of algorithm development before you commit to expensive hardware testing.

A workflow for developing motor control algorithms using Model-Based Design involves:

  • Building accurate system models from libraries of motors, power electronics, sensors, and loads
  • Developing algorithms to control the output of power electronic components that regulate voltage and frequency
  • Simulating both ideal and nonlinear power electronics models in system-level models to evaluate components, test system variations, and verify power control algorithms
  • Generating ANSI, ISO, or processor-optimized C code and HDL for real-time testing and implementation
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Power Electronics Control

For efficient power conversion and control, you need to control the action of IGBTs, power MOSFETs, and other solid-state electronics. As the number of consumer, commercial, and industrial products employing power electronics increases, it becomes more important to understand the interaction of digital control algorithms, power electronics, and the balance of the electrical system early during development, before hardware testing begins. Using simulation, you can develop power electronic control systems in less time, and you can design control algorithms and verify that your overall system achieves the specified efficiency and performance.

A workflow for developing power electronics control algorithms using Model-Based Design involves:

  • Modeling power electronics, circuit elements, and electrical sources and loads
  • Developing algorithms to control the power electronics in converters and inverters
  • Simulating both ideal and nonlinear power electronics models in system-level models to evaluate components, test system variations, and verify power control algorithms
  • Generating ANSI, ISO, or processor-optimized C code and HDL for real-time testing and implementation

Battery Management Systems

Battery management systems are an essential component of electric vehicle, grid storage and power backup, consumer goods, portable medical device, and aerospace systems. They depend on embedded control systems that regulate charge/discharge scheduling, estimate state-of-charge, set safety cut-off limits, and implement cell balancing. A proven approach for developing these systems accurately includes simulating the control laws within the electrical system. However, one challenge is in creating a battery model that balances accuracy and simulation speed.

A workflow for developing battery management control algorithms using Model-Based Design involves:

  • Modeling battery cells as multi-RC equivalent circuits and estimating circuit parameters with experimental data
  • Building accurate electrical system models, often from libraries of motors, drive electronics, sensors, and loads
  • Generating ANSI, ISO, or processor-optimized C code and HDL for real-time testing and implementation
  • Verifying and testing control algorithms using simulation and prototyping hardware
  • Model and simulate digital and hybrid beamforming techniques for phased antenna arrays

Streamline Battery Management System Development


MPPT and PV Solar Inverters

Solar inverters contain control algorithms for maximum power tracking, grid voltage and frequency synchronization, and anti-islanding protection. You need these algorithms in order to ensure optimal power delivery under changing solar irradiance. You can use simulation to evaluate control algorithms to determine the suitability of the inverter design. This reduces the time and cost of algorithm development before you commit to expensive hardware testing. A workflow for developing solar inverter control algorithms using Model-Based Design involves:

  • Modeling solar irradiance, the photovoltaic (PV) array, power electronics, and electrical loads
  • Developing algorithms to control the inverter power electronics and power regulation
  • Simulating power inverter and battery charging algorithms and various MPPT strategies, such as perturb and observe, incremental conductance, and constant voltage techniques
  • Generating ANSI, ISO, or processor-optimized C code and HDL for real-time testing and implementation