benchflow-ai/skillsbench/finite-horizon-lqr

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Solving finite-horizon LQR via dynamic programming for MPC.

Install

npx vskill@latest install benchflow-ai/skillsbench/finite-horizon-lqr

SKILL.md

Finite-Horizon LQR for MPC

Problem Formulation

Minimize cost over horizon N:

J = Σ(k=0 to N-1) [x'Qx + u'Ru] + x_N' P x_N

Backward Riccati Recursion

Initialize: P_N = Q (or LQR solution for stability)

For k = N-1 down to 0:

python
K_k = inv(R + B'P_{k+1}B) @ B'P_{k+1}A
P_k = Q + A'P_{k+1}(A - B @ K_k)

Forward Simulation

Starting from x_0:

python
u_k = -K_k @ x_k
x_{k+1} = A @ x_k + B @ u_k

Python Implementation

python
def finite_horizon_lqr(A, B, Q, R, N, x0):
    nx, nu = A.shape[0], B.shape[1]
    K = np.zeros((nu, nx, N))
    P = Q.copy()
    # Backward pass
    for k in range(N-1, -1, -1):
        K[:,:,k] = np.linalg.solve(R + B.T @ P @ B, B.T @ P @ A)
        P = Q + A.T @ P @ (A - B @ K[:,:,k])
    # Return first control
    return -K[:,:,0] @ x0

MPC Application

At each timestep:

  1. Measure current state x
  2. Solve finite-horizon LQR from x
  3. Apply first control u_0
  4. Repeat next timestep

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Source

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325
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