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The models can be built from a plist constructor describing each field. It is possible to give incomplete information on the model and let the constructor auto-complete some fields.
It is still rather lengthy to build a ssm object, and it is advisable to write built-in models to limit the time spent on modeling.
The most retrained set of inputs is
sys = ssm(plist('amats', amats, 'bmats', bmats, 'cmats', cmats, 'dmats', dmats, 'timestep', timestep, 'name', name, 'statenames', statenames, 'inputnames', inputnames, 'outputnames', outputnames)))
Example:
name = 'sys'; statenames = {'ss1' 'ss2' 'ss3'}; inputnames = {'input1' 'input2' 'input3'}; outputnames = {'output1' 'output2' 'output3'}; timestep = 0; amats = cell(3, 3); bmats = cell(3, 3); cmats = cell(3, 3); dmats = cell(3, 3); amats{1, 1} = -(sym('OMEGA')); amats{2, 2} = -2; amats{3, 3} = [0 1; -0.05 -0.01]; amats{3, 1} = [-1; -3]; bmats{1, 1} = 1; bmats{2, 2} = 2; bmats{3, 3} = 3*eye(2); cmats{1, 1} = 1; cmats{2, 2} = 1; cmats{3, 3} = eye(2); dmats{1, 3} = [6 6]; dmats{2, 1} = 6; dmats{3, 2} = [6; 6]; sys = ssm(plist(... 'amats', amats, 'bmats', bmats, 'cmats', cmats, 'dmats', dmats, ... 'timestep', timestep, ... 'name', name, 'statenames', statenames, 'inputnames', inputnames, 'outputnames', outputnames)) ------ ssm/1 ------- amats: { [1 x1 ] [] [] [] [-2] [] [2 x1 ] [] [2 x2 ] } [3x3] bmats: { [1] [] [] [] [2] [] [] [] [2 x2 ] } [3x3] cmats: { [1] [] [] [] [1] [] [] [] [2 x2 ] } [3x3] dmats: { [0] [] [1 x2 ] [6] [0] [] [] [2 x1 ] [] } [3x3] timestep: 0 inputs: [1x3 ssmblock] 1 : INPUT1 | variable_1 [] 2 : INPUT2 | variable_1 [] 3 : INPUT3 | variable_1 [], variable_2 [] states: [1x3 ssmblock] 1 : SS1 | variable_1 [] 2 : SS2 | variable_1 [] 3 : SS3 | variable_1 [], variable_2 [] outputs: [1x3 ssmblock] 1 : OUTPUT1 | variable_1 [] 2 : OUTPUT2 | variable_1 [] 3 : OUTPUT3 | variable_1 [], variable_2 [] numparams: (empty-plist) params: (empty-plist) Ninputs: 3 inputsizes: [1 1 2] Noutputs: 3 outputsizes: [1 1 2] Nstates: 3 statesizes: [1 1 2] Nnumparams: 0 Nparams: 0 isnumerical: false hist: ssm.hist procinfo: [] plotinfo: [] name: sys description: UUID: ff67a657-e5fb-4f80-8a40-02725ee1801c --------------------
Then the field “params” must be set by the user using the syntax “sys.setParams(<parameter plist>)”. Otherwise the toolbox will simply assume there are no parameters in the system and the matrices should be exclusively numerical in this case..
The content of “params” is a plist with parameters whose:
KEY is the name of the parameter in the matrices
VALUE is a property which must be set before proceeding numerical substitutions
MIN, MAX, and SIGMA which are useful for an optimizer if system identification is proceeded.
sys.setParams(plist({'OMEGA', 'system frequency'}, 2))
------ ssm/1 -------
amats: { [1 x1 ] [] []
[] [-2] []
[2 x1 ] [] [2 x2 ] } [3x3]
bmats: { [1] [] []
[] [2] []
[] [] [2 x2 ] } [3x3]
cmats: { [1] [] []
[] [1] []
[] [] [2 x2 ] } [3x3]
dmats: { [0] [] [1 x2 ]
[6] [0] []
[] [2 x1 ] [] } [3x3]
timestep: 0
inputs: [1x3 ssmblock]
1 : INPUT1 | variable_1 []
2 : INPUT2 | variable_1 []
3 : INPUT3 | variable_1 [], variable_2 []
states: [1x3 ssmblock]
1 : SS1 | variable_1 []
2 : SS2 | variable_1 []
3 : SS3 | variable_1 [], variable_2 []
outputs: [1x3 ssmblock]
1 : OUTPUT1 | variable_1 []
2 : OUTPUT2 | variable_1 []
3 : OUTPUT3 | variable_1 [], variable_2 []
numparams: (empty-plist)
params: (OMEGA=2)
Ninputs: 3
inputsizes: [1 1 2]
Noutputs: 3
outputsizes: [1 1 2]
Nstates: 3
statesizes: [1 1 2]
Nnumparams: 0
Nparams: 1
isnumerical: false
hist: ssm.hist
procinfo: []
plotinfo: []
name: sys
description:
UUID: 15da354b-aa4f-44e8-bb73-30316359b20b
--------------------The method “setParams” should not be confused with “setParameters” which only allows to modify the property VALUE in the “params” field.
Input blocks and input variables will be automatically named after their index number in this case, and the description field will be empty.
A complete description (with parameters) example is:
name = 'sys'; statenames = {'ss1' 'ss2' 'ss3'}; inputnames = {'input1' 'input2' 'input3'}; outputnames = {'output1' 'output2' 'output3'}; timestep = 0; amats = cell(3, 3); bmats = cell(3, 3); cmats = cell(3, 3); dmats = cell(3, 3); amats{1, 1} = -(sym('OMEGA')); amats{2, 2} = -2; amats{3, 3} = [0 1; -0.05 -0.01]; amats{3, 1} = [-1; -3]; bmats{1, 1} = 1; bmats{2, 2} = 2; bmats{3, 3} = 3*eye(2); cmats{1, 1} = 1; cmats{2, 2} = 1; cmats{3, 3} = eye(2); dmats{1, 3} = [6 6]; dmats{2, 1} = 6; dmats{3, 2} = [6; 6]; params = plist({'OMEGA', 'system frequency'}, 2); sys = ssm(plist(... 'amats', amats, 'bmats', bmats, 'cmats', cmats, 'dmats', dmats, ... 'timestep', timestep, ... 'name', name, 'statenames', statenames, 'inputnames', inputnames, 'outputnames', outputnames, ... 'params', params)) ------ ssm/1 ------- amats: { [1 x1 ] [] [] [] [-2] [] [2 x1 ] [] [2 x2 ] } [3x3] bmats: { [1] [] [] [] [2] [] [] [] [2 x2 ] } [3x3] cmats: { [1] [] [] [] [1] [] [] [] [2 x2 ] } [3x3] dmats: { [0] [] [1 x2 ] [6] [0] [] [] [2 x1 ] [] } [3x3] timestep: 0 inputs: [1x3 ssmblock] 1 : INPUT1 | variable_1 [] 2 : INPUT2 | variable_1 [] 3 : INPUT3 | variable_1 [], variable_2 [] states: [1x3 ssmblock] 1 : SS1 | variable_1 [] 2 : SS2 | variable_1 [] 3 : SS3 | variable_1 [], variable_2 [] outputs: [1x3 ssmblock] 1 : OUTPUT1 | variable_1 [] 2 : OUTPUT2 | variable_1 [] 3 : OUTPUT3 | variable_1 [], variable_2 [] numparams: (empty-plist) params: (OMEGA=2) Ninputs: 3 inputsizes: [1 1 2] Noutputs: 3 outputsizes: [1 1 2] Nstates: 3 statesizes: [1 1 2] Nnumparams: 0 Nparams: 1 isnumerical: false hist: ssm.hist procinfo: [] plotinfo: [] name: sys description: UUID: 439fa9e2-b24c-4306-99b0-db7a1b868857 --------------------
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