---
id: wiki-2026-0508-model-predictive-control-mpc
title: Model Predictive Control (MPC)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [MPC, Receding-Horizon-Control, RHC]
duplicate_of: none
source_trust_level: A
confidence_score: 0.95
verification_status: applied
tags: [control, optimization, robotics, autonomous-vehicles, receding-horizon]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: cvxpy-acados
---

# Model Predictive Control (MPC)

## 매 한 줄
> **"매 step마다 future를 optimize, 첫 action만 실행, 매 반복"**. Receding horizon control은 매 dynamics model + cost + constraints를 매 online QP/NLP로 풀어낸다. 2026 자율주행·드론·humanoid robot의 매 dominant control paradigm으로 reinforcement learning과도 hybrid 구성된다.

## 매 핵심

### 매 정식화
```
min_{u_0..u_{N-1}} Σ ℓ(x_k, u_k) + V_f(x_N)
s.t. x_{k+1} = f(x_k, u_k)
     g(x_k, u_k) ≤ 0
     x_0 = x_current
```
매 첫 u_0만 적용, 다음 step에서 매 새 measurement로 재최적화.

### 매 종류
- **Linear MPC**: f linear, ℓ quadratic → QP.
- **Nonlinear MPC (NMPC)**: NLP (IPOPT, acados).
- **Robust MPC**: tube/min-max — uncertainty 처리.
- **Stochastic MPC**: chance constraints.
- **Learning-based MPC**: f를 NN/GP로.

### 매 응용
1. Autonomous driving — trajectory tracking, lane change.
2. Quadrotor / drone control.
3. Humanoid locomotion (Boston Dynamics, Tesla Optimus 추정).
4. Process industry — refinery, chemical plant.
5. HVAC, smart grid.

## 💻 패턴

### Linear MPC with CVXPY
```python
import cvxpy as cp, numpy as np
def lin_mpc(A, B, x0, N=10, Q=None, R=None):
    nx, nu = B.shape
    Q = Q if Q is not None else np.eye(nx)
    R = R if R is not None else 0.1*np.eye(nu)
    x = cp.Variable((nx, N+1))
    u = cp.Variable((nu, N))
    cost, cons = 0, [x[:, 0] == x0]
    for k in range(N):
        cost += cp.quad_form(x[:, k], Q) + cp.quad_form(u[:, k], R)
        cons += [x[:, k+1] == A @ x[:, k] + B @ u[:, k]]
        cons += [cp.norm(u[:, k], 'inf') <= 1.0]
    cp.Problem(cp.Minimize(cost), cons).solve()
    return u[:, 0].value
```

### NMPC with CasADi/acados (sketch)
```python
import casadi as ca
x = ca.SX.sym('x', nx); u = ca.SX.sym('u', nu)
f = ca.Function('f', [x, u], [dynamics(x, u)])
# build NLP with multiple shooting...
```

### Receding horizon loop
```python
for t in range(T):
    x_meas = sensor.read()
    u_star = mpc_solve(x_meas)
    actuator.apply(u_star)
```

### Reference tracking cost
```python
cost += cp.quad_form(x[:, k] - x_ref, Q)
```

### Soft constraint via slack
```python
slack = cp.Variable(N, nonneg=True)
cons += [g(x[:, k], u[:, k]) <= slack[k]]
cost += 1e3 * cp.sum(slack)
```

### Warm-start (next iter uses prev solution)
```python
solver.set_initial_guess(shift(u_prev))
```

## 매 결정 기준
| 상황 | MPC variant |
|---|---|
| Linear plant, quadratic cost | QP-based linear MPC |
| Nonlinear dynamics | NMPC (acados, CasADi) |
| Bounded uncertainty | Tube MPC |
| Probabilistic constraint | Stochastic MPC |
| Hard real-time (kHz) | Explicit MPC (precomputed) |

**기본값**: Linear MPC + warm-start (cycle time < 10 ms).

## 🔗 Graph
- 부모: [[Optimal-Control-Theory]] · [[Optimization]]
- 응용: [[Autonomous-Vehicle-Path-Planning]]
- Adjacent: [[Reinforcement-Learning]] · [[Kalman-Filter-and-State-Tracking]] · [[Feedback-Control-Systems]]

## 🤖 LLM 활용
**언제**: Constrained dynamic systems, real-time replanning, model + cost are known.
**언제 X**: Model 알 수 없거나 long-horizon strategic decision (use RL).

## ❌ 안티패턴
- **Horizon 너무 짧음**: 매 myopic control.
- **Constraint feasibility 무시**: infeasible 시 fallback 없음.
- **Cold-start 매 iteration**: 매 latency 폭발 — warm-start 필수.
- **Plant-model mismatch 무시**: 매 robust/adaptive 가 필요.

## 🧪 검증 / 중복
- Verified (Rawlings, Mayne, Diehl "Model Predictive Control").
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — MPC formulation + CVXPY/acados patterns |