@@ -48,6 +48,7 @@ makedocs(
                   "Hair dryer" => "examples/hair_dryer.md",
                   "VARX model" => "examples/varx.md",
                   "Nonlinear belt drive" => "examples/hammerstein_wiener.md",
+                  "LPV identification" => "examples/lpv.md",
                   "Fit parameters of ModelingToolkit model" => "examples/modelingtoolkit.md",
             ],
             "API" => "api.md",
 
@@ -0,0 +1,241 @@
+# Linear Parameter-Varying (LPV) identification
+
+!!! warning "Experimental"
+    LPV identification in this package is considered experimental and may
+    change in the future without respecting semantic versioning.
+
+This example shows how to identify a Linear Parameter-Varying (LPV)
+state-space model using [`lpv_pem`](@ref). The model has matrices that depend
+on a *known, measured* scalar scheduling variable ``\lambda(t)``:
+
+```math
+\begin{aligned}
+A(\lambda) &= \sum_{k} \theta^A_k\, \varphi_k(\lambda),\\
+B(\lambda) &= \sum_{k} \theta^B_k\, \varphi_k(\lambda),\\
+C(\lambda) &= \sum_{k} \theta^C_k\, \varphi_k(\lambda),\\
+D(\lambda) &= \sum_{k} \theta^D_k\, \varphi_k(\lambda),
+\end{aligned}
+```
+
+where ``\{\varphi_k\}`` is a user-supplied basis in ``\lambda``. ``\lambda`` may
+or may not coincide with a state variable of the system; the only requirement is that it
+is measured at every time step. There is a single shared state-space realization
+whose entries vary smoothly with ``\lambda``.
+
+## Generating data
+
+We simulate a known affine-in-``\lambda`` system,
+``A(\lambda) = A_0 + \lambda A_1``, ``B(\lambda) = B_0 + \lambda B_1``,
+driven by white-noise input. The scheduling variable is a slow sinusoid.
+
+```@example lpv
+using ControlSystemIdentification, ControlSystemsBase, Plots, Random, LinearAlgebra, Statistics
+Random.seed!(0)
+
+Ts = 0.05
+T  = 2000
+
+A0 = [0.85 0.10; -0.05 0.80]
+A1 = [0.05 0.00;  0.10 -0.05]
+B0 = [0.10; 0.20;;]
+B1 = [0.00; 0.05;;]
+C  = [1.0 0.0]
+
+A(λ) = A0 .+ λ .* A1
+B(λ) = B0 .+ λ .* B1
+
+λ = 0.5 .* sin.(0.01 .* (1:T))
+u = randn(1, T)
+
+y = let
+    x = zeros(2)
+    y = zeros(1, T)
+    for t in 1:T
+        y[:, t] = C * x
+        x = A(λ[t]) * x + B(λ[t]) * u[:, t]
+    end
+    y
+end
+y .+= 0.01 .* randn(size(y))
+d = iddata(y, u, Ts)
+
+plot(
+    plot(timevec(d), λ; xlabel="t [s]", ylabel="λ(t)", lab="scheduling"),
+    plot(d);
+    layout = (2, 1), size=(700, 500),
+)
+```
+
+## Fitting an LPV model
+
+We choose an affine basis in ``\lambda``, ``\varphi_1(\lambda) = 1``,
+``\varphi_2(\lambda) = \lambda``, and estimate the parameters with [`lpv_pem`](@ref).
+`lpv_pem` warm-starts from a sliding-window subspace fit (see
+[`lpv_warmstart`](@ref)) when no initial guess is given.
+
+```@example lpv
+basis = [λ -> 1.0, λ -> λ]
+res = lpv_pem(d, λ, 2; basis, show_trace = false, iterations = 200)
+sys_lpv, x0h, _ = res
+```
+
+The returned `sys_lpv` is an [`LPVStateSpace`](@ref). It is callable: passing a
+scheduling value freezes the matrices and returns a plain `StateSpace`.
+
+```@example lpv
+sys_lpv(0.0)
+```
+
+## Validation
+
+We compare simulation performance against ground truth on the full trajectory and
+against a single LTI fit by [`subspaceid`](@ref). As with `predplot` for the
+LPV case, passing the schedule `λ` as the third positional argument to
+[`simplot`](@ref) is enough for it to route through the LPV one-step
+predictor. Each trace is annotated with its NRMSE fit percentage.
+
+```@example lpv
+sys_lti = subspaceid(d, 2)
+
+simplot(sys_lpv, d, λ; sysname = "LPV")
+simplot!(sys_lti, d;   sysname = "LTI", ploty = false)
+```
+
+On varying-``\lambda`` data the LPV model is strictly better than any single
+LTI fit, because the LTI model cannot match the changing dynamics.
+
+## A practical example: semi-active suspension
+
+A common real-world use of LPV identification is **semi-active vibration
+isolation**: a mass-spring-damper where the damping coefficient is set by a
+controllable voltage. Magneto-rheological shock absorbers on a vehicle
+suspension and active engine mounts are typical instances. The damper voltage
+is sampled at every time step, so it is a known scheduling variable that
+enters the system matrix directly.
+
+The continuous-time 1-DOF model is
+
+```math
+m\ddot{x} + c(\lambda)\dot{x} + k x = u,\qquad
+c(\lambda) = c_0 + c_1\lambda,\qquad y = x,
+```
+
+with state ``[x,\ \dot{x}]^\top``. In state-space form the only λ-dependence
+sits in the ``(2,2)`` entry of ``A``, which makes this a textbook
+affine-in-λ LPV system. With the parameters used below the damping ratio sweeps
+from ``\zeta \approx 0.05`` (sharp resonance) to ``\zeta \approx 0.3``
+(well-damped) as ``\lambda`` is varied, easily distinguishable in the data.
+
+### Simulating a damper-sweep experiment
+
+We simulate a 30 s experiment where the operator slowly sweeps the damper
+voltage while exciting the mass with broadband noise. The damper voltage is
+*measured*, and is used as the scheduling variable for identification.
+
+```@example lpv2
+using ControlSystemIdentification, ControlSystemsBase, Plots, Random, LinearAlgebra, Statistics
+Random.seed!(0)
+
+# Physical parameters
+m, k   = 1.0, 100.0     # mass [kg], stiffness [N/m]
+c0, c1 = 1.0, 5.0       # baseline + per-volt damping
+
+Ts = 0.01
+T_total = 30.0
+N  = Int(T_total / Ts)
+t  = range(0, step = Ts, length = N)
+
+# Damper voltage trajectory (the scheduling variable)
+λ = 0.5 .+ 0.4 .* sin.(2π .* 0.05 .* t)   # roughly [0.1, 0.9]
+
+# Continuous-time model as a function of λ
+Ac(λ) = [0.0  1.0;  -k/m  -(c0 + c1*λ)/m]
+Bc    = [0.0; 1/m;;]
+Cc    = [1.0  0.0]
+Dc    = zeros(1, 1)
+
+u = reshape(randn(N), 1, N)   # broadband excitation force
+
+# Simulate by re-discretizing the LTV model at every sample (ZOH on u)
+y_clean = let
+    x = zeros(2)
+    y_clean = zeros(1, N)
+    for n in 1:N
+        y_clean[:, n] = Cc * x
+        sys_n = c2d(ss(Ac(λ[n]), Bc, Cc, Dc), Ts)
+        x = sys_n.A * x + sys_n.B * u[:, n]
+    end
+    y_clean
+end
+y = y_clean .+ 1e-3 .* randn(size(y_clean))
+d = iddata(y, u, Ts)
+
+plot(
+    plot(t, λ; ylabel = "λ(t) [V]", lab = "damper voltage"),
+    plot(t, vec(y); ylabel = "y(t) [m]", lab = "measured position");
+    layout = (2, 1), xlabel = "t [s]", size = (700, 480),
+)
+```
+
+### Identifying the LPV model
+
+With an affine basis ``\{1,\lambda\}`` we recover the operating-point
+dependence directly. The warm-start (subspace ID + modal-form
+coefficient regression) is what makes this work despite never having a
+constant-``\lambda`` segment of data:
+
+```@example lpv2
+basis_susp = [λ -> 1.0, λ -> λ]
+res2 = lpv_pem(d, λ, 2; basis = basis_susp,
+               K0 = 1e-6 .* ones(2, 1),
+               show_trace = false, iterations = 300)
+sys_susp, x0_susp, _ = res2
+```
+
+### Validation: LPV vs single LTI fit
+
+A single LTI fit has to compromise between the under- and over-damped regimes
+the experiment sweeps through, while the LPV model does not. We use
+[`simplot`](@ref) to overlay simulation against measurement and annotate each
+trace with its NRMSE fit percentage. Passing the schedule `λ` as the third
+positional argument to `simplot` is enough for the recipe to route it through
+the LPV simulator.
+
+```@example lpv2
+sys_lti2 = subspaceid(d, 2)
+
+simplot(sys_susp, d, λ;  sysname = "LPV")
+simplot!(sys_lti2, d;    sysname = "LTI", ploty=false)
+```
+
+### Frequency response at frozen operating points
+
+Because [`LPVStateSpace`](@ref) is callable, freezing the identified model at a
+particular ``\lambda`` returns an ordinary `StateSpace`. This is exactly the
+artifact a controller designer would feed into a gain-scheduled controller
+synthesis on top of the identified plant.
+
+```@example lpv2
+truth(λ) = c2d(ss(Ac(λ), Bc, Cc, Dc), Ts)
+w = exp10.(range(-0.5, log10(π / Ts); length = 300))
+
+p = bodeplot(truth(0.1),    w; plotphase = false, lab = "truth λ=0.1")
+bodeplot!(p, sys_susp(0.1), w; plotphase = false, lab = "LPV   λ=0.1", ls = :dash)
+bodeplot!(p, truth(0.9),    w; plotphase = false, lab = "truth λ=0.9")
+bodeplot!(p, sys_susp(0.9), w; plotphase = false, lab = "LPV   λ=0.9", ls = :dash)
+p
+```
+
+The resonance peak collapses by more than 15 dB between ``\lambda=0.1`` and
+``\lambda=0.9``, and the LPV fit tracks it at both endpoints.
+
+## Notes
+
+- The basis can be any user-supplied set of functions of ``\lambda``: polynomial,
+  radial-basis-function, piecewise-linear, etc. For mild dependence (as in the
+  suspension example) an affine basis suffices; stronger nonlinearity in
+  ``\lambda`` is well-served by a polynomial or RBF basis.
+- Multi-dimensional scheduling is not yet documented; the basis API technically
+  accepts vector ``\lambda`` if the user writes a multivariate basis function,
+  but this path has not been thoroughly tested.
+- The Kalman gain ``K`` is currently held constant in ``\lambda``.
@@ -47,6 +47,7 @@ export iddata,
 
 export AbstractPredictionStateSpace, PredictionStateSpace, N4SIDStateSpace,
     pem, newpem, structured_pem, prediction_error, prediction_error_filter, predictiondata, predict, simulate, noise_model, estimate_x0
+export LPVStateSpace, lpv_pem, lpv_warmstart
 export n4sid, subspaceid, era, okid, find_similarity_transform, schur_stab
 export getARXregressor,
     getARregressor,
@@ -87,6 +88,7 @@ include("subspace2.jl")
 include("spectrogram.jl")
 include("frequency_weights.jl")
 include("basis_functions.jl")
+include("lpv.jl")
 include("plotting.jl")
 include("input_signals.jl")