Introduction to Statespace Models with LTPDA

What is a ssm object?

The ssm class is a class to use “state space models” for simulation, identification and modeling in the LTPDA toolbox. A state space model is a mathematical object constituted of

Input and output variables (lines in u and y) may be named and assigned a unit. However for the state x there is more of a choice since if free of choice via a basis change. The most usual choice is that x contents the same units as y and it derivatives, or the same units as u and its integrals. In general the size of the state space is equal to the number of poles in the system.

In the LTPDA toolbox, lines in u,y,x are grouped in blocks (input blocks, state blocks, output blocks) to ease the understanding of a large system. This means the matrices A, B, C, and D are also split into blocks of the corresponding size. The diagonal blocks of A may represent the dynamics of a LTP subsystem (the controller, the propulsion …) or the coupling between two systems (from the propulsion to the equations of motion)... In addition the dynamic equation also contains an inertia matrix M so that the parametric equation is made simple. The equation becomes M x' = Ax + Bu. If not user set M is supposed to be the identity.

Below we build a standard system to show the contents of the object.

sys = ssm(plist('built-in', 'HARMONIC_OSC_1D', 'symbolic params', 'all'))
M:   running buildParamPlist
M:   running buildParamPlist
M:   running update_struct
M:   running validate
M:   running validate
M:   running display
------ ssm/1 -------
      amats: {  [2 x2 ]  }  [1x1]
      bmats: {  [2 x1 ]    []    }  [1x2]
      cmats: {  [1 x2 ]  }  [1x1]
      dmats: { [0] [1] }  [1x2]
   timestep: 0
     inputs:  [1x2 ssmblock]
         1 : COMMAND | force [kg m s^(-2)]
         2 : NOISE | readout [m]
     states:  [1x1 ssmblock]
         1 : HARMONIC_OSC_1D | x [m], xdot [m s^(-1)]
    outputs:  [1x1 ssmblock]
         1 : HARMONIC_OSC_1D | position [m]
  numparams: (empty-plist)
     params: (M=1, K=1, VBETA=1)
    Ninputs: 2
 inputsizes: [1 1]
   Noutputs: 1
outputsizes: 1
    Nstates: 1
 statesizes: 2
 Nnumparams: 0
    Nparams: 3
isnumerical: false
       hist: ssm.hist 
   procinfo: []
   plotinfo: []
       name: HARMONIC_OSC_1D
description: Harmonic oscillator
       UUID: 25bf3710-6057-45a7-b817-bea4dff74021

The fields “amats”, “mmats”, “bmats”, “cmats”, “dmats” contain the matrices of the differential and observation equation of a mass spring system with dampening.

The systems inputs/states/outputs are listed by blocks. There are two input blocks, which correspond to the commanded signal (COMMAND) and the noise (NOISE). There is one state block and one output block. Each input has its name displayed followed with individual input variable names and units. Note that the empty ssm object does not have an input block, a state block and an output block of size 0, it has no such blocks, and the size fields are empty.

The “params” field is the 1x1 default plist with the symbolic parameters. In the case when the system is fully numerical, parameters are deleted from the field “params” as they get substituted. Viceversa, when the model is not fully numeric (as in this case), the “params” plist is filled with the names of the symbolic parameters.

Inputs, states and output sizes are summed up in the fields N* and *sizes.

Useful informations about the structure and the content of the ssm built-in models can be obtained by accessing their dynamics documentation. In order to access these information, just remember that all ssm model files are called ssm_model_<model_name> and then type

help ssm_model_HARMONIC_OSC_1D
  HARMONIC_OSC_1D A statespace model of the HARMONIC OSCILLATOR 1D
  DESCRIPTION: A statespace model of the HARMONIC OSCILLATOR 1D
            HARMONIC_OSC_1D = ssm(plist('built-in','HARMONIC_OSC_1D'))
            - HARMONIC_OSC_1D is an SSM object
    Model Information

The 'Model Information' link is very useful since it will open a help browser html page like the following:

Built-in ssm model info

In this way you can know the structure of the model, in terms of the building blocks, the input and output ports and so on.

©LTP Team