Ball and Plate System

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A nonlinear ARX (NARX) system

Model description: 

$$\begin{align*} y(t)&=\begin{bmatrix}0.1 & -0.1 & 0.25 & 0.5 \end{bmatrix} \varphi (t) +\\ &+ \frac{L}{2}(\|\varphi (t)\|^2 - 2(max\{\|\varphi(t)\|^2, 1\} -1) +\\ &+ 2(max\{\|\varphi(t)\|^2, 2\} - 2) - (max\{\|\varphi(t)\|^2, 3\} -3)) + e(t), \end{align*} $$

where

$\varphi(t)=\begin{bmatrix} y(t-1) & y(t-2) & u(t-1) & u(t-2)\end{bmatrix}^{\mathrm T}$

$L = 0.1$

$e(t) \in N(0, 0.01)$, i.e. $\sigma = 0.1$.

Type: 

Form: 

Time domain: 

Linearity: 

Publication details: 

TitleNonlinear system identification via direct weight optimization
Publication TypeJournal Article
Year of Publication2005
AuthorsRoll, Jacob, Nazin Alexander, and Ljung Lennart
JournalAutomatica
Volume41
Pagination475 - 490
Date Published01/2005
ISSN0005-1098
URLhttp://dx.doi.org/10.1016/j.automatica.2004.11.010

Bilinear system of non-minimum phase

Model description: 

$$\begin{align*} y(t) &= y(t-1) + u(t-1) + 1.3u(t-2) + 0.3u(t-1)y(t-1) \\ &+0.5u(t-2)y(t-2)+e(t)/\Delta, \end{align*}$$

where $e(t)$ is normal school white noise signal with covariance 0.1.

Type: 

Form: 

Model order: 

2

Time domain: 

Publication details: 

TitleGeneralized Predictive Control for a Class Of Bilinear Systems
Publication TypeConference Paper
Year of Publication1970
AuthorsLiu, Guizhi, and Li and Ping
Conference NameControl, Automation, Robotics and Vision
Date Published2006
AbstractA new generalized predictive control algorithm for a kind of input-output bilinear system is proposed in the paper (BGPC). The algorithm combines bilinear and linear terms of I/O bilinear system, and constitutes an ARIMA model analogous to linear systems. Using optimization predictive information fully, the algorithm carries out multi-step predictions by recursive approximation. The heavy computation of generic nonlinear optimization is avoided with control law of analytical form being used to the non-minimum phase bilinear systems. Simulation results show the effectiveness of the algorithm and the performance of the algorithm is better than linear generalized predictive control (LGPC). Key words: bilinear systems; bilinear generalized predictive control (BGPC); recursive approaches; non-minimum phase systems; analytical control laws
DOI10.1109/ICARCV.2006.345181

Ball and Plate System

Model description: 

The ball and plate system is a system, where a metal ball stays on a rigid square plate with each side length of 1m. The slope of the plate can be manipulated by two perpendicularly installed step motors, so that the tilting of the plate will make the ball moving.

$$\begin{align*} \begin{bmatrix} \dot{x}_1 \\ \dot{x}_2\\ \dot{x}_3\\ \dot{x}_4\\ \dot{x}_5\\ \dot{x}_6\\ \dot{x}_7\\ \dot{x}_8\\ \end{bmatrix} &= \begin{bmatrix} x_2 \\ B(x_1x_4^2 + x_4x_5x_8 - g \sin x_3)\\ x_4\\ 0\\ x_6\\ B(x_5x_8^2 + x_1x_4x_8 - g \sin x_7)\\ x_8\\ 0\\ \end{bmatrix} + \begin{bmatrix} 0 & 0\\ 0 & 0\\ 0 & 0\\ 1 & 0\\ 0 & 0\\ 0 & 0\\ 0 & 0\\ 0 & 1\\ \end{bmatrix} \begin{bmatrix} u_x\\ u_y\\ \end{bmatrix}, \\ Y &= h(X) = (x_1,x_5)^{\mathrm T}, \end{align*}$$

where $B= m/(m + J/R^2)$ and $X = (x_1; x_2; x_3; x_4; x_5; x_6; x_7; x_8)^{\mathrm T} = (x; \dot{x}; \theta_x; \dot{\theta}_x; y; \dot{y}; \theta_y; \dot{\theta}_y)^{\mathrm T}$

Parameters are presented in the table below.

Symbol Description Parameter value and unit
$m$ Mass of the ball $0.11$ $Kg$
$R$ Radius of the ball $0.02$ $m$
$S$ Dimension of the ball $1.0 \times 1.0$ $m^2$
$x$ Position of the ball in the $x$-axis $m$
$y$ Position of the ball in the $y$-axis $m$
$\dot{x}$ Velocity of the ball in the $x$-axis $m/s$
$\dot{y}$ Velocity of the ball in the $y$-axis $m/s$
$w$ Rolling angular velocity of the ball $Arc/s$
$\dot{r}$ Velocity of the ball, $\dot{r}^2 = x^2 + y^2$ $m/s$
$v_{max}$ Maximum velocity of the ball $4$ $mm/s$
$\tau_x$ Torque applied to the plate in the $x$-axis $Kg$ $m^2/s^2$
$\tau_y$ Torque applied to the plate in the $y$-axis $Kg$ $m^2/s^2$
$\theta_x$ Angle of the plate in the $x$-axis $Arc$
$\theta_y$ Angle of the plate in the $y$-axis $Arc$
$\dot{\theta}_x$ Angle velocity of the plate in the $x$-axis $Arc/s$
$\dot{\theta}_y$ Angle velocity of the plate in the $y$-axis $Arc/s$
$u_x$ Angle acceleration velocity of the plate from $x$-axis $Arc/s^2$
$u_y$ Angle acceleration velocity of the plate from $y$-axis $Arc/s^2$
$J_P$ Mass moment of inertia of the plate $0.5$ $Kg$ $m^2$
$J$ Mass moment of inertia of the ball $1.76e$ $-$ $5$ $Kg$ $m^2$
$g$ Acceleration due to gravity $9.8$ $m/s^2$

Type: 

Form: 

Model order: 

8

Time domain: 

Linearity: 

Attachment: 

Publication details: 

TitleTrajectory planning and tracking of ball and plate system using hierarchical fuzzy control scheme
Publication TypeJournal Article
Year of Publication2004
AuthorsFan, Xingzhe, Zhang Naiyao, and Teng Shujie
JournalFuzzy Sets and Systems
Volume144
Pagination297-312
Date PublishedJun
ISSN0165-0114
DOI10.1016/S0165-0114(03)00135-0

Pages