README.md

MPC-water-tank-implemention

This tutorial shows an implementation of a linear quadratic model predictive controller (MPC) to control a quadruple-tank model, simulated in Matlab/Simulink. Consider that the plant to control is modeled as a linear time-invariant system given by

$$ \begin{align} x^+&=Ax+Bu \\ y&=Cx+Du \end{align} $$

where $x\in R^n$ his the current state of the system, $u \in R^m $ is the current input, $y \in R^p$ is the controlled output and $x^+$ the successor state.

This linear model is derived from the linearization of the model at an operation equilibrium point given by the triplet $(x_0, u_0, y_0)$. Therefore, denoting as $(x(j), u(j), y (j))$ the state, inputs and outputs of the plant, we have that $x(j) = x(j) − x_0$, $u(j) = u(j) − u_0$ and $y(j) = y (j) − y_0$.

The objective is to implement an MPC control law $U(j) = \kappa(x(j), R)$ such that the controlled system is asymptotically stable and the controlled variables $y (j)$ converge to the reference $R$ if this is reachable. For a given state of the prediction model $x = x−x_0$ and a reference $r = R −y_0$, the MPC control law is derived from the online solution of the following optimization control problem:

$$ \begin{align} V_{\text{N}}(x,y_{sp};\mathbf{u})&= \sum_{j=0}^{N-1}\parallel x(j)-x_r\parallel_{Q}^{2} + \parallel u(j)-u_r\parallel_{R}^{2} + \parallel x(N)-x_{r}\parallel_{P}^{2} \\ s.t.& \quad x(j+1)=Ax(j)+Bu(j),\\ & \quad x(0)=x, \\ & \quad u_{min} \leq u \leq u_{max}, \\ & \quad (x_r,u_r)=M_r, \\ & \quad x(N) =0 \end{align} $$

where $M$ is a suitable matrix that maps the steady state and input given by the reference. Once a solution $u^{∗}(x, r)$ is obtained, the control law is calculated by the receding horizon technique as follows $\kappa_N (x, r) = u^{*} (0; x, r)$. The control law implemented in the real plant will be $u(k) =\kappa_N (x(j) − x_O, R − y_0) + u_0$.

Lifted System Dynamics

the notation of a lifted system dynamics

$$ \mathbf{x}=G_x x(j) + G_u \mathbf{u} $$

is used, where the whole state sequence can be determined with the aid of the input sequence $\mathbf{u}$ for a given initial state $x(j)$. The state sequence and the input sequence are

$$ \mathbf{x}=\left[\begin{array}{c} x_{0}\\ x_{1}\\ \vdots\\ x_{N} \end{array}\right],u=\left[\begin{array}{c} u_{0}\\ u_{1}\\ \vdots\\ u_{N-1} \end{array}\right] $$

The lifted system matrix and the lifted input matrix are

$$ G_{x}=\left[\begin{array}{c} I\\ A\\ A^{2}\\ \vdots\\ A^{N} \end{array}\right],G_{u}=\left[\begin{array}{cccc} 0 & 0 & \cdots & 0\\ B & 0 & \cdots & 0\\ AB & B & \cdots & 0\\ \vdots & \vdots & \vdots & \vdots\\ A^{N-1}B & A^{N-2}B & \cdots & B \end{array}\right] $$

Quadratic Program

The implementation with the MATLAB built-in functions quadprog is shown here.

$$ \begin{align} \underset{\mathbf{u}}{\min}V_{N}(x,x_{r};\mathbf{u}) &= (\mathbf{x}-\mathbf{x_r})^TQ(\mathbf{x}-\mathbf{x_r})+ (\mathbf{u}-\mathbf{u}_r)^TR(\mathbf{u}-\mathbf{u}_r)\\ s.t.& \quad \mathbf{x}=G_xx(j)+G_u\mathbf{u},\\ &\quad \widetilde{G}\mathbf{u} \leq \widetilde{g} \end{align} $$

$$ \begin{align} \underset{\mathbf{u}}{\min}V_{N}(x,x_{r};\mathbf{u})& = \mathbf{u}^T(G_u Q G_u + R )\mathbf{u} + 2( x(j)^{T} G_{x}^{T} Q G_u)\mathbf{u}-2( x_{ R}^{T} Q G_u - \mathbf{u_{r}}^{T} R )\mathbf{u}\\ s.t.& \quad \mathbf{x}=G_xx(j)+G_u\mathbf{u},\\ &\quad \widetilde{G}\mathbf{u} \leq \widetilde{g} \end{align} $$

$$ \begin{align} \underset{\mathbf{u}}{\min}V_{N}(x,x_{r};\mathbf{u}) &= \mathbf{u}^T H \mathbf{u} + 2(F_1-F_2)\mathbf{u}\\ s.t.& \quad \mathbf{x}=G_xx(j)+G_u\mathbf{u},\\ &\quad \widetilde{G}\mathbf{u} \leq \widetilde{g} \end{align} $$

Results