A system with 2 states , 1 input , and 1 output:

Count the number of inputs and outputs:

The order of the system:

A system with 2 inputs and 1 output:

The response to a unit step applied to each input channel while holding the other at zero:

A system with 1 input and 2 outputs:

A pure gain system:

Connect the gains in series with the original system:

Specify operating values for the states:

Obtain the StateSpaceModel using linearization about the operating values:

Explicitly specify the output and independent variables:

The nonlinear representation of a StateSpaceModel with specified states:

The nonlinear state-space representation of a TransferFunctionModel with default states:

The nonlinear representation of an AffineStateSpaceModel, preserving states:

The nonlinear state-space representation of a set of ODEs:

Set new state, input, and output variables:

Use state , input , and output :

Use Normal to get the model's arguments:

States and inputs deleted using SystemsModelDelete are set to their operating values:

So are the states and inputs not extracted by SystemsModelExtract:

StateSpaceTransform computes the operating values of the new states if the old were specified:

By default, the simulation functions assume the operating values as the initial values:

StateResponse:

OutputResponse:

StateSpaceModel linearizes about all operating values:

AffineStateSpaceModel only linearizes about operating values of input variables:

NonlinearStateSpaceModel preserves operating values specified along with ODEs:

FullInformationOutputRegulator linearizes about the operating values to compute the gains :

Compute the gains from the linearized subsystem:

Label the outputs and states of a system:

Use NonlinearStateSpaceModel to specify the equations for a two-tank system and simulate it:

Use Bernoulli's law and mass balance to derive the resulting differential equations:

Use steady-state operating values :

Define the corresponding nonlinear system with input and output :

With a higher steady-state value for flow rate, the level in the tanks settles at a higher level:

Control the level in the second tank below using a PID controller:

Obtain a PID controller using the linearized model and PIDTune:

Specify a piecewise reference value for the liquid level in the second tank:

The simulations show how the closed-loop system follows the reference:

Find the equations of motion of an aircraft's longitudinal dynamics , where the states are and the inputs are : »

Here , , and ℳ are taken to be polynomials in α and δ_e:

The aerodynamic and model parameters:

Use , where is the flight path angle and the pitch angle:

Find the steady states for steady , level flight at a specific speed :

Define state variables and initial values:

The nonlinear state-space model of the aircraft with states and inputs :

The 2×2 transfer function representation of the linearized system:

The system is unstable due to the poles in the right half-plane:

It is also nonminimum phase due to the zeros in the right half-plane:

The BodePlot shows that the velocity can be affected more than the flight path angle :

The six-degree-of-freedom equations of motion of an aircraft. The rotation matrix for a rotation about an axis : »

The rotation matrices for the Euler angles , , and :

The matrix transformation of a vector from the body-fixed to the earth-fixed axes:

The inverse transformation from the earth-fixed to the body-fixed axes:

Verify they are inverses:

A figure showing the earth-fixed axes and body-fixed axes :

The Euler angle rates in terms of the angular velocity components , , and :

The kinematic equations for the Euler angles:

The kinematic equations for the flight path variables , , and :

The inertia matrix, assuming that the plane is a plane of symmetry:

The angular momentum can be computed as :

A figure showing the forces and moments acting on the aircraft:

The state equations of the angular velocity can be obtained by solving the moment equation , where is the vector of aerodynamic moments:

The dynamics of the rotational motion:

The forces acting on the aircraft are the aerodynamic forces , its weight, and the thrust :

The dynamics of the translational motion can be obtained from Newton's law :

The complete equations of motion for the aircraft:

The longitudinal dynamics are the dynamics for states , , , and :

The lateral dynamics are the dynamics for the states , , , and :

Design an extended Kalman filter to track the motion of a wheeled robot:

The model of the robot:

All the measurements are assumed to be noisy, with covariance :

The gain of the filter is based on the covariance of the states:

The right-hand side of the differential equation for the estimated states with noisy measurements :

The right-hand side of the differential equation for the covariance matrix :

Assemble the estimator using NonlinearStateSpaceModel:

Simulate the robot from initial position and a set of inputs:

The noisy measurements:

Simulate the response of the filter using the inputs to the system and the noisy measurements:

Compare the actual, noisy, and filtered values of the variable :

Variable :

Variable :