Documentation

# musyn

Robust controller design using mu synthesis

## Syntax

``[K,CLperf] = musyn(P,nmeas,ncont)``
``[K,CLperf,info] = musyn(P,nmeas,ncont)``
``[K,CLperf,info] = musyn(P,nmeas,ncont,Kinit)``
``[K,CLperf,info] = musyn(___,opts)``
``[CL,CLperf] = musyn(CL0)``
``[CL,CLperf,info] = musyn(CL0)``
``[CL,CLperf,info] = musyn(CL0,blockvals)``
``[CL,CLperf,info] = musyn(___,opts)``
``[CL,CLperf,info,runs] = musyn(___,opts)``

## Description

`musyn` designs a robust controller for an uncertain plant using D-K iteration, which combines H synthesis (K step) with μ analysis (D step) to optimize closed-loop robust performance.

You can use `musyn` to:

• Synthesize "black box" unstructured robust controllers.

• Robustly tune a fixed-order or fixed-structure controller made up of tunable components such as PID controllers, state-space models, and static gains.

For additional information about performing μ synthesis and interpreting results, see Robust Controller Design Using Mu Synthesis.

### Full-Order Centralized Controllers

example

````[K,CLperf] = musyn(P,nmeas,ncont)` returns a controller `K` that optimizes the robust performance of the uncertain closed-loop system `CL = lft(P,K)`. The plant `P` is an uncertain plant with the partitioned form$\left[\begin{array}{c}z\\ y\end{array}\right]=\left[\begin{array}{cc}{P}_{11}& {P}_{12}\\ {P}_{21}& {P}_{22}\end{array}\right]\left[\begin{array}{c}w\\ u\end{array}\right],$where: w represents the disturbance inputs.u represents the control inputs.z represents the error outputs to be kept small.y represents the measurement outputs provided to the controller.`nmeas` and `ncont` are the numbers of signals in y and u, respectively. y and u are the last outputs and inputs of `P`, respectively. The closed-loop system `CL = lft(P,K)` achieves the robust performance `CLperf`, which is the μ upper bound, the robust performance metric calculated by `musynperf`.For this syntax, `musyn` uses `hinfsyn` for H∞ synthesis (the K step).```
````[K,CLperf,info] = musyn(P,nmeas,ncont)` returns additional information about each D-K iteration.```
````[K,CLperf,info] = musyn(P,nmeas,ncont,Kinit)` initializes the D-K iteration process with the controller `Kinit`. To restart D-K iteration using the results of the jth iteration from a previous run, use `Kinit = info(j).K`.```
````[K,CLperf,info] = musyn(___,opts)` uses additional options for the D-K iteration and underlying `hinfsyn` computations. Use `musynOptions` to create the option set. You can use this syntax with any of the previous input and output argument combinations.```

### Fixed-Structure Controllers

example

````[CL,CLperf] = musyn(CL0)` optimizes the robust performance by tuning the free parameters in the tunable, uncertain closed-loop model `CL0`. The `genss` model `CL0` is an uncertain and tunable model of the closed-loop system whose robust performance you want to optimize. The model contains:Uncertain control design blocks such as `ureal` and `ultidyn` to represent the uncertaintyTunable control design blocks such as `tunablePID`, `tunableSS`, and `tunableGain` to represent the tunable components of the control structure`musyn` returns the closed-loop model `CL` with the tunable control design blocks set to the tuned values. The best achieved robust performance is returned as `CLperf`. For this syntax, `musyn` uses `hinfstruct` for H∞ synthesis (K step).```
````[CL,CLperf,info] = musyn(CL0)` also returns additional information about each D-K iteration.```
````[CL,CLperf,info] = musyn(CL0,blockvals)` initializes the D-K iteration with the tunable block values in `blockvals`. You can specify the block values as a structure or by providing a closed-loop model whose blocks are tuned to the values you want to initialize. For instance, to use the tuned values obtained in a previous `musyn` run, set `blockvalues = CL`.```
````[CL,CLperf,info] = musyn(___,opts)` uses additional options for the D-K iteration and underlying `hinfstruct` computations. Use `musynOptions` to create the option set. You can use this syntax with any of the previous input and output argument combinations.```
````[CL,CLperf,info,runs] = musyn(___,opts)` also returns details about each independent tuning run when you use the `'RandomStart'` option of `musynOptions` to perform additional randomized runs.```

## Examples

collapse all

Synthesize a stabilizing robust controller K for the system in the following illustration, where the plant `G` includes some dynamic uncertainty. The controller must also reject disturbances injected at the plant output.

The nominal plant model `G0` is an unstable first-order system.

`G0 = tf(1,[1 -1]);`

The uncertainty in `G0` is as follows:

• At low frequency, below 2 rad/s, the plant can vary up to 25% from its nominal value.

• Around 2 rad/s, the percentage variation starts to increase, reaching 400% at approximately 32 rad/s.

Represent the frequency-dependent model uncertainty with the weight `Wu` and the uncertain LTI dynamic uncertainty `InputUnc`, an `ultidyn` control design block.

```Wu = 0.25*tf([1/2 1],[1/32 1]); InputUnc = ultidyn('InputUnc',[1 1]); G = G0*(1+InputUnc*Wu);```

`musyn` seeks a controller that optimizes robust performance from inputs to outputs. To set up this problem for `musyn`, then, insert a weighting function `Wp` that captures the disturbance rejection goal.

When you provide this augmented plant `P` to `musyn`, the function designs a controller that drives the transfer function from `d` to `e` below 1 at all frequencies. That transfer function is `Wp/S`, where `S = 1 – GK` is the sensitivity function. Thus, choose `Wp` to be the inverse of the desired sensitivity. For this example, choose `Wp` with:

• Low-frequency gain of 100 (40 dB)

• 0 dB crossover at 0.5 rad/s

• High-frequency gain of 0.25 (`–`12 dB)

```Wp = makeweight(100,[1 0.5],0.25); bodemag(Wp)```

You can now construct the plant as shown in the block diagram by naming the signals, defining a sum block, and using `connect`. Construct the plant so that the control input is the last input, and the measurement output is the last output.

```G.InputName = 'u'; G.OutputName = 'y1'; Wp.InputName = 'y'; Wp.OutputName = 'e'; SumD = sumblk('y = y1 + d'); inputs = {'d','u'}; outputs = {'e','y'}; P = connect(G,Wp,SumD,inputs,outputs);```

Use `musyn` to design a controller `K` for this uncertain system.

```nmeas = 1; ncont = 1; [K,CLperf,info] = musyn(P,nmeas,ncont); ```
```D-K ITERATION SUMMARY: ----------------------------------------------------------------- Robust performance Fit order ----------------------------------------------------------------- Iter K Step Peak MU D Fit D 1 1.345 1.344 1.36 8 2 0.7923 0.7904 0.7961 4 3 0.6789 0.6789 0.6857 10 4 0.6572 0.6572 0.6598 8 5 0.6538 0.6538 0.6542 8 6 0.6532 0.6532 0.6533 8 Best achieved robust performance: 0.653 ```

The display shows that the best achieved robust performance is about 0.65. This result means that the gain from `d` to `e` remains below 0.65 for up to 1/0.65 times the uncertainty specified in the plant. Thus, the controller achieves the robust performance objectives for the full range of modeled uncertainty. (For more information on interpreting `musyn` results, see Robust Controller Design Using Mu Synthesis.)

You can examine the robust performance using analysis commands such as `robgain` and `wcgainplot`. For instance, examine the worst-case gain of the closed-loop system.

```CL = lft(P,K); wcg = wcgain(CL)```
```wcg = struct with fields: LowerBound: 0.5283 UpperBound: 0.5294 CriticalFrequency: 0 ```

This result confirms that the actual worst-case gain over the modeled uncertainty is about 0.53, which is within the robust performance of 0.65 guaranteed by `musyn`.

For this problem, the controller returned by `musyn` is fairly high order.

`size(K)`
```State-space model with 1 outputs, 1 inputs, and 11 states. ```

You can try reducing the controller order with model-reduction commands such as `balred` or `reduce` to see whether you can maintain robust performance. (For an example, see the `musynperf` reference page.) Or, you can try specifying a lower order controller structure and use `musyn` to tune it. See Robust Tuning of Fixed-Structure Controller.

Tune a fixed-structure controller for the control system in Unstructured Robust Controller Synthesis, which shows how to use `musyn` to design an unstructured full-order centralized controller, and returns a controller of order 11. For this example, use the same control structure, as shown in the diagram, but restrict the structure of `K` to a fifth-order state-space model.

First, construct the same plant `P` as in Unstructured Robust Controller Synthesis. The plant is an uncertain plant `G` augmented by a sensitivity-weighting function `Wp`.

```G0 = tf(1,[1 -1]); Wu = 0.25*tf([1/2 1],[1/32 1]); InputUnc = ultidyn('InputUnc',[1 1]); G = G0*(1+InputUnc*Wu); G.InputName = 'u'; G.OutputName = 'y1'; Wp = makeweight(100,[1 0.5],0.25); Wp.InputName = 'y'; Wp.OutputName = 'e'; SumD = sumblk('y = y1 + d'); inputs = {'d','u'}; outputs = {'e','y'}; P = connect(G,Wp,SumD,inputs,outputs);```

Create a `tunableSS` control design block to represent the fixed controller structure, a fifth-order state-space model.

`C0 = tunableSS('K',5,1,1);`

Form the closed-loop system, which is a generalized state-space (`genss`) model that has both a tunable block and a uncertain block.

`CL0 = lft(P,C0)`
```CL0 = Generalized continuous-time state-space model with 1 outputs, 1 inputs, 8 states, and the following blocks: InputUnc: Uncertain 1x1 LTI, peak gain = 1, 1 occurrences K: Parametric 1x1 state-space model, 5 states, 1 occurrences. Type "ss(CL0)" to see the current value, "get(CL0)" to see all properties, and "CL0.Blocks" to interact with the blocks. ```

Use `musyn` to tune the free entries of the controller.

`[CL,CLperf,info] = musyn(CL0);`
```D-K ITERATION SUMMARY: ----------------------------------------------------------------- Robust performance Fit order ----------------------------------------------------------------- Iter K Step Peak MU D Fit D 1 1.342 1.341 1.356 10 2 0.7974 0.7952 0.8005 6 3 0.6846 0.6723 0.6773 8 4 0.6667 0.6615 0.6689 10 5 0.6645 0.6592 0.6646 10 6 0.6627 0.6568 0.6651 6 Best achieved robust performance: 0.657 ```

Even when you specify the structure of `K` as a fifth-order state-space model, `musyn` can find tuned parameter values that yield very similar robust performance to the 11th-order controller. You can try still lower controller orders to see whether the robust stability is preserved.

## Input Arguments

collapse all

Uncertain plant, specified as an uncertain state-space (`uss`) model. `P` has inputs [w;u] and outputs [z;y], where:

• w represents the disturbance inputs.

• u represents the control inputs.

• z represents the error outputs to be kept small.

• y represents the measurement outputs provided to the controller.

Construct `P` such that measurement outputs y are the last outputs, and the control inputs u are the last inputs.

`P` can optionally contain weighting functions (loop-shaping filters) that represent control objectives that you want the controller to robustly satisfy. For a detailed example that constructs such an augmented plant for μ synthesis, see Robust Control of an Active Suspension.

Number of measurement output signals in the plant, specified as a nonnegative integer. The function takes the last `nmeas` plant outputs as the measurements y. The returned controller `K` has `nmeas` inputs.

Number of control input signals in the plant, specified as a nonnegative integer. The function takes the last `ncont` plant inputs as the controls u. The returned controller `K` has `ncont` outputs.

Initial value of the controller, specified as a dynamic system model, such as a state-space (`ss`) model. By default, `musyn` begins by computing an H controller for the nominal system. Use `Kinit` to start with a different controller. Setting `Kinit = info(j).K` uses the controller computed in the jth D-K iteration of the `musyn` run that produced `info`.

One use of this input argument is to continue iterating after `musyn` reaches the maximum number of iterations specified by the `'MaxIter'` option of `musynOptions`. If the display shows that `musyn` is still making progress when it stops iterating, you can run `musyn` again, starting with the last synthesized controller, to see how much more `musyn` can improve the robust performance.

Additional options for the computation, specified as an options object you create using `musynOptions`. Available options include the following:

• Turn on a full display of algorithm progress that pauses after each D-K iteration so that you can examine intermediate results.

• Use mixed μ synthesis to treat real uncertain parameters as real rather than as complex, for a less conservative and possibly more robust controller.

• Set maximum orders for the functions used to fit the D and G scalings.

• Use parallel computing for independent optimization runs when tuning a fixed-structure controller.

For information about all available options, see `musynOptions`.

Closed-loop system with tunable controller elements, specified as a generalized state-space (`genss`) model with both uncertain and tunable control design blocks. Construct `CL0` by creating and interconnecting:

• Numeric LTI models representing the fixed components of the control system

• Uncertain control design blocks, such as `ureal` and `ultidyn` blocks, representing the uncertain components of the plant

• Optional LTI weighting functions (loop-shaping filters) that represent control objectives

• Tunable control design blocks such as `tunablePID`, `tunableSS`, and `tunableGain` to represent tunable components of the controller C0

For an example that shows how to build such a model, see Build Tunable Control System Model With Uncertain Parameters.

Initial values of the tunable parameters in `CL0`, specified as a structure or as a `genss` model. By default, `musyn` begins by tuning the controller parameters for the nominal system. Use `blockvals` to start with a different controller. Specify the initial parameter values as:

• A structure whose fields are the names of the tunable blocks, and whose values are control design blocks having the desired current values. For instance, if you have a tuned system `CL` obtained in a previous `musyn` run, you can initialize with the tuned values of the controller blocks in `CL` by setting ```blockvals = CL.Blocks```.

• A `genss` model whose tunable blocks have the desired current value.

Setting `blockvals = info(j).K` uses the tuned values computed in the jth D-K iteration of the `musyn` run that produced `info`. For instance, if you have a tuned system `CL`, you can initialize with its tuned values by setting `blockvals = CL`. Such initialization can be useful for continuing iteration after `musyn` reaches the maximum number of iterations. If the display shows that `musyn` is still making progress when it stops iterating, you can run `musyn` again, starting with the last synthesized controller, to see how much more `musyn` can improve the robust performance.

## Output Arguments

collapse all

Controller that yields the robust H performance `CLperf`, returned as a state-space (`ss`) model. The controller has `nmeas` inputs and `ncont` outputs.

The order of `K` depends on the order of the fitting functions for the D and G scalings and the number of uncertain blocks in your system. For information on how to reduce the order of the returned controller, see Improve Results of Mu Synthesis.

Best achieved robust performance, returned as a positive scalar. `musyn` attempts to optimize the controller `K` to minimize this value. The closed-loop gain from w to z remains below `CLperf` for uncertainty up 1/`CLperf` times the uncertainty specified in the plant. For instance, if `CLperf` is 1.125, then the closed-loop gain remains below 1.125 for up to 0.8 times the uncertainty specified in the plant.

For more information on the computation and interpretation of this quantity, see Robust Performance Measure for Mu Synthesis. For information on how to improve the best achieved robust performance, see Improve Results of Mu Synthesis.

Details of D-K iteration results, returned as a structure array. `info(j)` contains the results of the jth D-K iteration in the run. `info` has the following fields.

FieldDescription
`K`

Optimal controller found in the K step of this iteration, returned as a state-space (`ss`) model or as a structure containing tuned control design blocks.

• For unstructured controller synthesis, `info(j).K` is a state-space model.

• For fixed-structure controller tuning, `info(j).K` is a structure whose names are the names of the tunable blocks in `CL0`. The values are tunable control design blocks with current values set to the tuned value.

`gamma`Optimized scaled H performance, returned as a scalar. This scaled performance is achieved by optimal controller `info(j).K`. The default command-window display shows this value in the `K Step` column. For details about the computation and interpretation of this quantity, see Robust Performance Measure for Mu Synthesis.
`KInfo`

H synthesis data, returned as a structure.

• For centralized, full-order controller synthesis, this structure is the same as the `info` output argument of `hinfsyn`.

• For fixed-structure controller tuning, this structure is the same as the `info` output argument of `hinfstruct`.

`PeakMu`Robust performance $\overline{\mu }$ of the closed-loop system with controller `info(j).K`, returned as a positive scalar value. The default command-window display shows this value in the `Peak MU` column . For details about the computation and interpretation of this quantity, see Robust Performance Measure for Mu Synthesis.
`DG`

D and G scaling data from robust performance analysis in this iteration, returned as a structure with the following fields.

• `Frequency` — Vector of frequencies for which μ analysis was performed

• `Dr`, `Dc`, `Gcr` — Values of the scaling factors Dr(ω), Dc(ω), and Gcr(ω) at the corresponding frequencies.

For more information about D and G scaling, see Robust Performance Measure for Mu Synthesis.

`dr,dc,PSI`Rational fit of the D and G scaling data, returned as `ss` models. For details about how `musyn` fits the scaling data, see Robust Performance Measure for Mu Synthesis.
`FitOrder`Orders of the functions used to fit the scaling data in this iteration, returned as a two-element vector. The two entries are the fit order for the D and G scalings, respectively. The default command-window display shows these values in the ```Fit Order``` column.
`PeakMuFit`Scaled H performance achieved with `info(j).K` and the fitted D and G scalings, returned as a scalar. The default command-window display shows this value in the `DG Fit` column .

Tuned closed-loop system, returned as a generalized state-space `genss` model with the same uncertain and tunable control design blocks as `CL0`. The current values of the tunable blocks in `CL.Blocks` are set to the tuned values.

Information about each independent randomized run, returned as a structure array. Use this output with fixed-structure μ synthesis when you set the `'RandomStart'` option of `musynOptions` to N > 0. That option causes `musyn` to perform multiple independent D-K iteration runs initialized from different values of controller parameters. `runs(j)` contains the results of the jth independent restart in the following fields.

FieldDescription
`K`

Optimal controller found in this run, returned as a structure containing the tuned values of the control design blocks.

`muPerf`Best achieved robust H∞ performance for this run, returned as a positive scalar. The controller that `musyn` returns when you use multiple runs is the one for which `runs(j).muPerf` is smallest.
`Info`

Details of D-K iteration results, returned as a structure array. `runs(j).Info` contains fields corresponding to those of the `info` output of `musyn`, for the jth run.

## Algorithms

`musyn` uses an iterative process called D-K iteration. In this process, the function:

1. Uses H synthesis to find a controller that minimizes the closed-loop gain of the nominal system.

2. Performs a robustness analysis to estimate the robust H performance of the closed-loop system. This amount is expressed as a scaled H norm involving dynamic scalings called the D and G scalings (the D step).

3. Finds a new controller to minimize the scaled H norm obtained in step 2 (the K step).

4. Repeats steps 2 and 3 until the robust performance stops improving.

For more details about how this algorithm works, see D-K Iteration Process.

Get trial now