Model Predictive Controller (MPC)

Overview & Motivation

Model Predictive Control (MPC) is a receding-horizon optimal control strategy that computes control inputs by solving a finite-horizon optimization problem at each time step. Unlike LQR, which applies a fixed gain computed offline, MPC re-solves the optimization online, enabling it to handle constraints on control inputs directly.

At each sample instant, MPC predicts the future system trajectory over a prediction horizon $N_p$, optimizes a sequence of control moves over a control horizon $N_c \leq N_p$, applies only the first control move, and repeats at the next step. This receding-horizon approach provides feedback and robustness to disturbances.

Key advantages over LQR:

• Explicit handling of input constraints (actuator limits)
• Preview and anticipation of future reference changes
• Tunable trade-off between horizon length and computational cost

When to use MPC:

• Systems with actuator saturation or operational limits
• Multi-input multi-output (MIMO) systems requiring coordinated control
• Applications where the computational budget allows online optimization (typically $N_c \cdot m \leq 20$)

Mathematical Theory

Prerequisites

• Discrete-time linear state-space model: $x_{k+1} = Ax_k + Bu_k$
• Quadratic cost weighting matrices $Q \succeq 0$ (state), $R \succ 0$ (control)
• Optional terminal cost matrix $P \succeq 0$ (typically from DARE)

Core Definitions

Cost function over prediction horizon $N_p$ with control horizon $N_c$:

$J = \sum_{k=0}^{N_p-1} x_k^T Q\, x_k + \sum_{k=0}^{N_c-1} u_k^T R\, u_k + x_{N_p}^T P\, x_{N_p}$

For $k \geq N_c$, the control input is held at $u_{N_c-1}$ or zero (our implementation sets it to zero beyond $N_c$).

Prediction Matrices

The future state trajectory can be expressed as:

$\mathbf{X} = \Psi\, x_0 + \Theta\, \mathbf{U}$

where $\mathbf{X} = [x_1^T, x_2^T, \ldots, x_{N_p}^T]^T$ and $\mathbf{U} = [u_0^T, u_1^T, \ldots, u_{N_c-1}^T]^T$.

State propagation matrix $\Psi$ ($N_p n \times n$):

$\Psi = \begin{bmatrix} A \\ A^2 \\ \vdots \\ A^{N_p} \end{bmatrix}$

Control-to-state matrix $\Theta$ ($N_p n \times N_c m$):

$\Theta = \begin{bmatrix} B & 0 & \cdots & 0 \\ AB & B & \cdots & 0 \\ A^2B & AB & \cdots & 0 \\ \vdots & \vdots & \ddots & \vdots \\ A^{N_p-1}B & A^{N_p-2}B & \cdots & A^{N_p-N_c}B \end{bmatrix}$

QP Formulation

Substituting predictions into the cost yields a quadratic program in $\mathbf{U}$:

$J = \mathbf{U}^T H\, \mathbf{U} + 2\, x_0^T F^T \mathbf{U} + \text{const}$

where:

$H = \Theta^T \bar{Q}\, \Theta + \bar{R}$ $F = \Theta^T \bar{Q}\, \Psi$

$\bar{Q}$ is block-diagonal with $Q$ on the first $N_p - 1$ blocks and $P$ on the last block. $\bar{R}$ is block-diagonal with $R$ repeated $N_c$ times.

Unconstrained Solution

Setting $\nabla_{\mathbf{U}} J = 0$:

$\mathbf{U}^* = -H^{-1} F\, x_0$

Solved via Gaussian elimination ($H\, \mathbf{U}^* = -F\, x_0$) rather than explicit matrix inversion.

Box Constraints

For control input constraints $u_{\min} \leq u_k \leq u_{\max}$, a simple projected approach clamps each element of the unconstrained solution. This is a first-order approximation; for tight constraints or state constraints, a full QP solver would be required.

Complexity Analysis

Phase	Time	Space	Notes
Offline	$O(N_p^2 \cdot n^3)$	$O((N_c m)^2 + N_p n \cdot N_c m)$	Prediction matrices, Hessian, gradient precomputed
Online	$O((N_c m)^3)$	$O((N_c m)^2)$	Gaussian elimination to solve $H\mathbf{U}=-Fx$
Per step	$O(N_c m \cdot n)$	$O(N_c m)$	Matrix-vector product $Fx$ + constraint clamping

For typical embedded use ($n=2, m=1, N_c=10$): the online solve is a $10 \times 10$ linear system, well within real-time budgets.

Step-by-Step Walkthrough

System: Double integrator with $dt = 0.1$ s

$A = \begin{bmatrix} 1 & 0.1 \\ 0 & 1 \end{bmatrix}, \quad B = \begin{bmatrix} 0.005 \\ 0.1 \end{bmatrix}$

$Q = \begin{bmatrix} 10 & 0 \\ 0 & 1 \end{bmatrix}, \quad R = [0.1], \quad N_p = N_c = 3$

Step 1 — Build $\Psi$:

$\Psi = \begin{bmatrix} A \\ A^2 \\ A^3 \end{bmatrix} = \begin{bmatrix} 1 & 0.1 \\ 0 & 1 \\ 1 & 0.2 \\ 0 & 1 \\ 1 & 0.3 \\ 0 & 1 \end{bmatrix}$

Step 2 — Build $\Theta$:

$\Theta = \begin{bmatrix} 0.005 & 0 & 0 \\ 0.1 & 0 & 0 \\ 0.015 & 0.005 & 0 \\ 0.2 & 0.1 & 0 \\ 0.03 & 0.015 & 0.005 \\ 0.3 & 0.2 & 0.1 \end{bmatrix}$

Step 3 — Compute $H$ and $F$:

$H = \Theta^T \bar{Q} \Theta + \bar{R}$ produces a $3 \times 3$ positive definite matrix.

$F = \Theta^T \bar{Q} \Psi$ produces a $3 \times 2$ matrix.

Step 4 — Online solve for $x_0 = [1, 0]^T$:

Solve $H\, \mathbf{U}^* = -F \cdot [1, 0]^T$ via Gaussian elimination.

Apply first element $u_0^*$ to the plant.

Pitfalls & Edge Cases

• Horizon too short: If $N_p$ is too small, the controller is myopic and may produce oscillatory or unstable behavior. Use DARE terminal cost $P$ to mitigate.
• Ill-conditioned Hessian: Very large $Q/R$ ratios create poorly conditioned $H$. Keep $Q$ and $R$ within 3–4 orders of magnitude of each other.
• Constraint infeasibility: Box constraints that are too tight for the required control effort will clamp every step, degrading performance. Monitor constraint activity.
• Fixed-point overflow: For Q15/Q31 types, the Hessian elements grow with horizon length. Keep $N_c \cdot m$ small and scale weights carefully.
• Stack usage: All matrices are stack-allocated. A system with $n=3, m=2, N_c=10$ creates a $20 \times 20$ Hessian (1600 bytes for float). Monitor stack budget.
• Terminal cost omission: Without terminal cost $P$, the controller ignores cost-to-go beyond the horizon, leading to suboptimal or unstable behavior for short horizons.

Variants & Generalizations

Variant	Key Difference	Use Case
Nonlinear MPC (NMPC)	Nonlinear prediction model, requires iterative optimization	Nonlinear plants, high-accuracy
Explicit MPC	Precomputes control law as piecewise affine function	Very fast online evaluation
Move-Blocking MPC	Constrains control moves to change only at specific steps	Reduces QP size
Adaptive MPC	Updates plant model online	Time-varying or uncertain systems
Distributed MPC	Decomposes problem across subsystems	Large-scale multi-agent systems
Robust MPC	Accounts for bounded disturbances in constraints	Safety-critical applications

Applications

• Autonomous vehicles: Path tracking with steering and acceleration limits
• Process control: Chemical reactor temperature/pressure regulation with valve constraints
• Robotics: Joint torque-constrained trajectory tracking
• Power electronics: Voltage/current regulation in converters with switching constraints
• HVAC systems: Energy-efficient building climate control with comfort bounds
• Quadrotor control: Attitude and position control with thrust limits

Connections to Other Algorithms

graph LR
    DARE["Discrete Algebraic<br/>Riccati Equation"] -->|terminal cost P| MPC["Model Predictive<br/>Controller"]
    GE["Gaussian<br/>Elimination"] -->|solves H·U = -F·x| MPC
    LQR["LQR Controller"] -.->|special case<br/>N→∞, no constraints| MPC
    MPC -->|first control move| Plant["Plant Model"]
    Plant -->|state measurement| MPC

• LQR is the infinite-horizon, unconstrained special case of MPC. As $N_p \to \infty$ with DARE terminal cost, the MPC gain converges to the LQR gain.
• DARE computes the terminal cost matrix $P$, ensuring the finite-horizon problem approximates the infinite-horizon solution.
• Gaussian Elimination solves the dense linear system $H\mathbf{U} = -Fx$ at each step. For constrained problems, iterative QP solvers could replace this.
• Kalman Filter provides state estimates when full state measurement is unavailable (MPC + KF = output-feedback MPC).

References & Further Reading

• Maciejowski, J.M., Predictive Control with Constraints, Prentice Hall, 2002.
• Rawlings, J.B. and Mayne, D.Q., Model Predictive Control: Theory and Design, Nob Hill Publishing, 2009.
• Camacho, E.F. and Bordons, C., Model Predictive Control, 2nd ed., Springer, 2007.
• Borrelli, F., Bemporad, A., and Morari, M., Predictive Control for Linear and Hybrid Systems, Cambridge University Press, 2017.
• Wang, L., Model Predictive Control System Design and Implementation Using MATLAB, Springer, 2009.