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Create State-Space Model with Both Fixed and Tunable Parameters

This example shows how to create a state-space `genss` model having both fixed and tunable parameters.

where a and b are tunable parameters, whose initial values are `-1` and `3`, respectively.

Create the tunable parameters using `realp`.

```a = realp('a',-1); b = realp('b',3);```

Define a generalized matrix using algebraic expressions of `a` and `b`.

`A = [1 a+b;0 a*b];`

`A` is a generalized matrix whose `Blocks` property contains `a` and `b`. The initial value of `A` is `[1 2;0 -3]`, from the initial values of `a` and `b`.

Create the fixed-value state-space matrices.

```B = [-3.0;1.5]; C = [0.3 0]; D = 0;```

Use `ss` to create the state-space model.

`sys = ss(A,B,C,D)`
```sys = Generalized continuous-time state-space model with 1 outputs, 1 inputs, 2 states, and the following blocks: a: Scalar parameter, 2 occurrences. b: Scalar parameter, 2 occurrences. Type "ss(sys)" to see the current value, "get(sys)" to see all properties, and "sys.Blocks" to interact with the blocks. ```

`sys` is a generalized LTI model (`genss`) with tunable parameters `a` and `b`. Confirm that the `A` property of `sys` is stored as a generalized matrix.

`sys.A`
```ans = Generalized matrix with 2 rows, 2 columns, and the following blocks: a: Scalar parameter, 2 occurrences. b: Scalar parameter, 2 occurrences. Type "double(ans)" to see the current value, "get(ans)" to see all properties, and "ans.Blocks" to interact with the blocks. ```