Simulink Control Design
Linearize models and design control systems
Model-Based PID Tuning
Use the PID Tuner app to automatically linearize Simulink models and compute gains of PID Controller blocks with a single click. You can interactively refine controller performance by adjusting bandwidth (speed of response) and phase margin(robustness) to meet design requirements.
Estimation of Plant Dynamics from Simulation Data
For Simulink models that do not linearize due to discontinuities such as pulse width modulation (PWM), use the PID Tuner app to create a linear plant model from simulation input-output data using system identification (requires System Identification Toolbox™). Alternatively, automatically tune PID controller gains based on an estimated frequency response of your plant model.
Real-Time PID Autotuning
Use the Closed-Loop PID Autotuner block to automatically tune PID gains in real-time, based on plant frequency responses estimated from hardware experiments. Generate C code to implement the tuning algorithm in embedded software. Conduct real-time experiments on plant hardware and automatically compute PID controller gains without Simulink in the loop(requires Simulink Coder™ ).
Model an arbitrary control structure in your Simulink model by using Gain, Transfer Function, State-Space, PID controller, and other tunable blocks. Graphically tune discrete or continuous loops by using root locus plots, Bode diagrams, and Nichols charts. Update Simulink model with tuned gains and verify your design using simulation.
Interactively tune controllers with multiple SISO loops and specify loop openings without modifying your Simulink model. You can visualize loop interactions and coupling effects while tuning parameters to optimize overall performance.
SISO and MIMO Loops
Automatically tune arbitrary SISO and MIMO control structures using the Control System Tuner app or command-line functions. You can tune decentralized control architectures with simple tunable elements such as gains, PID controllers, or low-order filters. You can also jointly tune several loops in a multiloop control system in Simulink.
Time and Frequency Domain Objectives
Specify and visualize tuning requirements such as reference tracking goals, sensitivity goals, disturbance rejection, closed-loop pole locations, and stability margins. Automatically tune controller parameters to satisfy these must-have requirements (design constraints) and to best meet the remaining requirements (objectives).
Tuning Against a Set of Plant Models
Linearize Simulink models across different operating points, parameter variations, and failure conditions to create a set of linear plant models. Then, tune the control system to meet performance objectives for all those plant models.
Linearize continuous, discrete, and multirate Simulink models. Use the Linear Analysis Tool or command line functions to specify loop openings and linearization inputs and outputs. You can linearize the whole model, a portion of the model, or a single block or subsystem. Visualize the results in a step-response plot or Bode diagram and compute open-loop and closed-loop responses.
Linearization Across Operating Points and Parameter Variations
Extract and analyze multiple linearizations for a model; vary parameter values, operating points, I/O sets; implement linear parameter varying (LPV) models.
Identify and fix common linearization issues using Linearization Advisor. You can find blocks on the linearization path and isolate blocks with specified linearization behavior, such as blocks that have linearized to zero.
Offline Frequency Response Estimation
Use the Linear Analysis Tool or command-line functions to estimate frequency response of a system modeled in Simulink without modifying the model. You can:
- Construct the excitation signals, such as sine sweeps or chirp signals.
- Run the simulations; collect the data; and calculate and plot the model’s frequency response.
- Examine frequency-domain characteristics and validate linearization of Simulink model.
Online Frequency Response Estimation
Measure the frequency response of your system in operation. You can deploy an embedded estimation algorithm as a standalone application for real-time estimation of a physical plant.
Compute linear parametric models by using System Identification Toolbox with computed frequency response of a Simulink model.
Extremum Seeking Control
Automatically adapt control system parameters to maximize an objective function using model-free real-time optimization with the Extremum Seeking Controller block. Use extremum seeking control for adaptive cruise control, maximum power point tracking (MPPT) for solar arrays, anti-lock braking systems (ABS), and other applications.
Modify control actions to satisfy constraints and action bounds using the Constraint Enforcement block. Apply constraint enforcement to control systems implemented with model predictive control, reinforcement learning, and PID control for automated driving, robotics, and other applications.
Batch Linearization and Trimming
Automatically trim and linearize Simulink models at multiple operating conditions.
Gain Surface Tuning
Model gain scheduled control systems using Simulink blocks such as Varying PID Controller, Varying Transfer Function, Varying Notch Filter and Varying Lowpass Filter. Automatically tune gain surface coefficients to meet performance requirements throughout the system’s operating envelope and achieve smooth transitions between operating points. You can specify requirements that vary with operating condition and validate tuning results over the full operating range of the design.
Calculate operating points from user-defined specifications. You can define custom constraints and objectives for trimming. You can also take operating point snapshots at specific times or events during simulation.
Use the Steady State Manager app to interactively compute operating points from state, input, and output specifications. Validate operating points against specifications andinteractively obtain operating points from simulation snapshots.
Initialize the model with computed operating points to start simulation from a steady-state condition or from a simulation snapshot. You can start the simulation at the beginning of the scenario that needs to be tested.