Use Control System Toolbox™ to design a digital servo controller for a disk drive read/write head.
Design a compensator for a plant model defined by frequency response data (FRD) using Control System Designer.
Configure Control System Designer from the command line and how to create functions to customize the startup of a Control System Designer session.
Tune compensators for a feedback control system using Control System Designer.
Design and analyze a controller for multiple plant models using Control System Designer.
The design of a YAW DAMPER for a 747® aircraft using the classical control design features in Control System Toolbox™.
Perform Kalman filtering. Both a steady state filter and a time varying filter are designed and simulated below.
The design of a non-inverting feedback amplifier circuit using Control System Toolbox™. This design is built around the operational amplifier (op amp), a standard building block of
Estimate states of linear systems using time-varying Kalman filters in Simulink. You use the Kalman Filter block from the Control System Toolbox library to estimate the position and
The comparison of three DC motor control techniques for tracking setpoint commands and reducing sensitivity to load disturbances:
Design a MIMO LQG regulator to control the horizontal and vertical thickness of a steel beam in a hot steel rolling mill.
Design a compensator in an IMC structure for series chemical reactors, using Control System Designer. Model-based control systems are often used to track setpoints and reject load
Design a compensator for a plant with time delays using Control System Designer.
Use LQG synthesis to design a feedback controller for a disk drive read/write head using Control System Designer.
Use the unscented Kalman filter and particle filter algorithms for nonlinear state estimation for the van der Pol oscillator.
Perform nonlinear state estimation in Simulink™ for a system with multiple sensors operating at different sample rates. The Extended Kalman Filter block in Control System Toolbox™ is used
Demonstrates the use of Particle Filter block in Control System Toolbox™. A discrete-time transfer function parameter estimation problem is reformulated and recursively solved as a
This case study illustrates Kalman filter design and simulation. Both steady-state and time-varying Kalman filters are considered.
Use command-line PID tuning options to reduce overshoot in reference tracking or to improve rejection of a disturbance at the plant input. Using the pidtune command, the example
Design a two-degree-of-freedom (2-DOF) PID controller at the command line. The example also compares the 2-DOF controller performance to the performance achieved with a 1-DOF PID
Design a computer hard-disk read/write head position controller using classical control design methods.
Use Simulink Control Design to tune a two-loop autopilot controlling the pitch rate and vertical acceleration of an airframe.
Use slTuner and systune to tune the current and velocity loops in a linear electric actuator with saturation limits.
Use the Control System Tuner app to tune a MIMO, multiloop control system modeled in Simulink.
Gives a tour of available time-domain requirements for control system tuning with systune or looptune.
Uses systune to design and tune a MIMO controller for a Diesel engine. The controller is tuned in discrete time for a single operating condition.
Use systune or looptune to automatically tune control systems modeled in Simulink.
Leverage the Parallel Computing Toolbox™ to accelerate multi-start strategies for tuning fixed-structure control systems.
Use Control System Toolbox™ to tune a digital motion control system.
In this example, you learn how to use Control System Tuner app to design a controller for a nonlinear plant modeled in Simulink. You accomplish the following tasks:
Use slTuner and systune to tune the standard configuration of a longitudinal autopilot. We thank Professor D. Alazard from Institut Superieur de l'Aeronautique et de l'Espace for
Mitigate communication delays in a passive control system.
Uses systune to explore trade-offs between setpoint tracking and disturbance rejection when tuning PID controllers.
Use the Control System Tuner app to tune the current and velocity loops in a linear electric actuator with saturation limits.
Specify loop shapes and stability margins when tuning control systems with systune or looptune.
Use looptune to decouple the two main feedback loops in a distillation column.
Jointly tune the inner and outer loops of a cascade architecture with the systune command.
Tune a high-performance digital controller with bandwidth close to the sampling frequency.
Use slTuner and systune to tune a multiloop controller for a rotorcraft.
Gives a tour of available frequency-domain requirements for control system tuning with systune or looptune.
The systune command can jointly tune the gains of your control system regardless of its architecture and number of feedback loops. This example outlines the systune workflow on a simple
Use the commands for continuous/discrete, discrete/continuous, and discrete/discrete conversions.
Convert a discrete-time system to continuous time using d2c, and compares the results using two different interpolation methods.
Absorbing time delays into frequency response data can cause undesirable phase wrapping at high frequencies.
To reduce the order of a model by pole-zero cancelation at the command line, use minreal .
Convert a time delay in a discrete-time model to factors of 1/_z_.
Create a two-dimensional (2-D) array of transfer functions using for loops. One parameter of the transfer function varies in each dimension of the array.
Compute a reduced-order approximation of a system when the system has unstable or near-unstable poles.
Compute a low-order approximation in two ways and compares the results. When you compute a low-order approximation by the balanced truncation method, you can either:
Convert a compensator from continuous to discrete time using several discretization methods, to identify a method that yields a good match in the frequency domain.
Insert multichannel analysis points in a generalized state-space model of a MIMO control system.
Build a block diagram and insert analysis points at locations of interest using the connect command. You can then use the analysis points to extract various system responses from the model.
Extract SISO control components from a 2-DOF PID controller in each of the feedforward, feedback, and filter configurations. The example compares the closed-loop systems in all
Query model characteristics such as stability, time domain, and number of inputs and outputs. You can use the techniques of this example on any type of dynamic system model.
Query the size of model arrays, including the number of inputs and outputs of the models in the array, and the array dimensions. It also shows how to query characteristics of the models in the
Focusing the energy-contribution calculation on a particular frequency region sometimes yields a good approximation to the dynamics of interest at a lower order than a reduction that
Sample a parametric model of a second-order filter across a grid of parameter values using sampleBlock .
Create a tunable model of the control system in the following illustration.
How choice of model type can affect numerical accuracy when interconnecting models.
Load an existing state-space ( docid:control_ref.f4-390421 ) model.
Upsample a system using both the d2d and upsample commands and compares the results of both to the original system.
At the command-line, use balred to compute a reduced-order approximation of a model.
Improve the frequency-domain accuracy of a system with a time delay that is a fractional multiple of the sample time.
Uses systune to generate smooth gain schedules for a three-loop autopilot.
Design and tune a gain-scheduled controller for a chemical reactor transitioning from low to high conversion rate. For background, see Seborg, D.E. et al., "Process Dynamics and Control",
This is Part 3 of the example series on design and tuning of the flight control system for the HL-20 vehicle. This part shows how to tune a classic SISO architecture for controlling the roll,
This is Part 5 of the example series on design and tuning of the flight control system for the HL-20 vehicle. This part shows how to perform most of the design in MATLAB without interacting with
This is Part 4 of the example series on design and tuning of the flight control system for the HL-20 vehicle. This part shows how to tune a MIMO PI architecture for controlling the roll, pitch,
This is Part 1 of a five-part example series on design and tuning of the flight control system for the HL-20 vehicle. This part deals with trimming and linearization of the airframe.
Proper scaling of state-space models can be critical for accuracy and provides an overview of automatic and manual rescaling tools.
Guidelines for building minimum-order models of LTI system interconnections.
High-multiplicity poles have high numerical sensitivity and can shift by significant amounts when switching model representation.
Compute and plot the response of a state-space ( ss ) model to specified initial state values using initial .
Passive control is often part of the safety requirements in applications such as process control, tele-operation, human-machine interfaces, and system networks. A system is passive if it
In its simplest form, a conic sector is the 2-D region delimited by two lines, y=au and y=bu .
Obtain a step-response plot and step-response data for a discrete-time dynamic system model. Obtaining time-domain responses of discrete-time models is the same as for continuous-time
Obtain numeric values of step response characteristics such as rise time, settling time, and overshoot using stepinfo . You can use similar techniques with lsiminfo to obtain
Obtain step and impulse response data, as well as step and impulse response plots, from a dynamic system model.
Examine the pole and zero locations of dynamic systems both graphically using pzplot and numerically using pole and zero .
Plot the frequency response and obtain frequency response data for a single-input, single-output (SISO) dynamic system model.
Obtain numeric values of several frequency-domain characteristics of a SISO dynamic system model, including the peak gain, dc gain, system bandwidth, and the frequencies at which the
Examine the frequency response of a multi-input, multi-output (MIMO) system in two ways: by computing the frequency response, and by computing the singular values.
Illustrates the properties of a series interconnection of passive systems.
Obtain impulse response data and plots for a multi-input, multi-output (MIMO) model using impulse . You can use the same techniques to obtain other types of time-domain responses of MIMO
Examine the sensitivity of a closed-loop control system to time delays within the system.
Construct a Linear Parameter Varying (LPV) representation of a system that exhibits multi-mode dynamics.
Model interconnections of LTI systems, from simple series and parallel connections to complex block diagrams.
How the Control System Toolbox™ lets you represent, manipulate, and analyze any LTI model with a finite number of delays. The delays can be at the system inputs or outputs, between specific
Create continuous-time linear models using the tf , zpk , ss , and frd commands.
Switch between the transfer function (TF), zero-pole-gain (ZPK), state-space (SS), and frequency response data (FRD) representations of LTI systems.
Examine the effect of stability margins on closed-loop response characteristics of a control system.
Use Control System Toolbox™ to analyze and design control systems with delays.
Analyze the time and frequency responses of common RLC circuits as a function of their physical parameters using Control System Toolbox™ functions.
Illustrates the properties of a parallel interconnection of passive systems.
Illustrates the properties of a feedback interconnection of passive systems.
The limitations of PI control for processes with long dead time and illustrates the benefits of a control strategy called "Smith Predictor."
Design feedback and feedforward compensators to regulate the temperature of a chemical reactor through a heat exchanger.
Design a PI controller with good disturbance rejection performance using the PID Tuner tool. The example also shows how to design an ISA-PID controller for both good disturbance rejection