Lumped-parameter model of dual–drive gantry stages

Deprecation warning

This website is now archived. Please check out the new website for Centre for Intelligent Systems which includes both A-Lab Control Systems Research lab and Re:creation XR lab.

However, the Dynamic System Model Database can still be used and may be updated in the future.

Discrete bilinear plant

Model description: 

The plant is

$$y(k)=1.2y(k-1)-0.8y(k-2)+0.2y(k-1)u(k-1)+u(k-1)+0.6u(k-2) + d(k),$$

where $d(k)$ is a disturbance.

Type: 

Form: 

Model order: 

2

Time domain: 

Linearity: 

Publication details: 

TitleAdaptive Bilinear Model Predictive Control
Publication TypeConference Paper
Year of Publication1986
AuthorsYeo, Y.K., and Williams D.C.
Conference NameAmerican Control Conference, 1986
Date Published06/1986
PublisherIEEE
Conference LocationSeattle, WA
Keywordsadaptive control, control system synthesis, Delay, Error correction, Least squares approximation, Mathematical model, parameter estimation, predictive control, Predictive models, Programmable control
AbstractAn adaptive controller for bilinear plants without delay and with stable inverses is defined based upon a bilinear model predictive control law and a classical recursive identification algorithm. For the case with no disturbance both the control error and the identification error converge to zero. For the case with a bounded disturbance, the control error is bounded and the identification converges. For the case with a constant disturbance, the control error often converges to zero and the identification converges.
URLhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=4789155&queryText%3DADAPTIVE+BILINEAR+MODEL+PREDICTIVE+CONTROL

SISO NLTI plant

Model description: 

Consider the following unknown discrete nonlinear dynamic system:

$$\begin{align*} y(k+1)&=p[{\bf q}(k), u(k)]=0.2\cos[0.8(y(k)+y(k-1))] \\ & +0.4\sin[0.8(y(k)+y(k-1))+2u(k)+u(k-1)] \\ &+0.1[9+y(k)+y(k-1)]+\left[{2(u(k)+u(k-1))\over 1+\cos(y(k))}\right] \end{align*}$$

for $k=0,1,2,\ldots$ with $y(k)=0,u(k)=0$, for $k=0,-1,-2,\ldots$, $\Delta t := t(k+1)-t(k)=0.02sec$, for $k=0,1,2,\ldots$.

Type: 

Form: 

Model order: 

2

Time domain: 

Linearity: 

Publication details: 

TitleRobust nonlinear adaptive control using neural networks
Publication TypeConference Paper
Year of Publication2001
AuthorsAdetona, O., Sathananthan S., and Keel L.H.
Conference NameProceedings of the 2001 American Control Conference, 2001
Date Published06/2001
PublisherIEEE
Conference LocationArlington, VA
ISBN Number0-7803-6495-3
Accession Number7092721
Keywordsadaptive control, asymptotic stability, neurocontrollers, nonlinear control systems, radial basis function networks, robust control
AbstractThis paper provides a robust indirect adaptive control method for non-affine plants. Subject to some mild assumptions, the method can be applied to both minimum and non-minimum phase plants with operating regions of any finite size while avoiding a set of restrictions, at least one of which is imposed by all existing methods. The benefits are achieved under the following assumptions: 1) the operating region is limited to the basin of attraction of an asymptotically stable equilibrium point of the plant; 2) the desired output of the plant is sufficiently slowly varying; and 3) the output of the plant must be sufficiently sensitive to the input signal. It is shown that the adaptive control system will be stable in the presence of unknown bounded modeling errors
DOI10.1109/ACC.2001.946247

2-input 2-output nonlinear system

Model description: 

The suggested tracker scheme is tested with a 2-input 2-output nonlinear system given by:

$$\begin{align*} y_{1} (k) & = 0.21y_{1} (k-1)-0.12y_{2} (k-2) \\ & + 0.3y_{1} (k-1)u_{2} (k-1)-1.6u_{2} (k-1) \\ & + 1.2u_{1} (k-1), \\ y_{2} (k) & = 0.25y_{2} (k-1)-0.1y_{1} (k-2) \\ &- 0.2 y_{2} (k-1)u_{1} (k-1)-2.6u_{1} (k-1) \\ &-1.2u_{2} (k-1). \end{align*}$$

Type: 

Form: 

Time domain: 

Linearity: 

Publication details: 

TitleU-model Based Adaptive Tracking Scheme for Unknown MIMO Bilinear Systems
Publication TypeConference Paper
Year of Publication2006
AuthorsAzhar, A.S.S., Al-Sunni F.M., and Shafiq M.
Conference Name1ST IEEE Conference on Industrial Electronics and Applications, 2006
Date Published05/2006
PublisherIEEE
Conference LocationSingapore
ISBN Number0-7803-9513-1
Accession Number9097014
Keywordsbilinear systems, discrete time systems, linear systems, MIMO systems, neurocontrollers, radial basis function networks
AbstractBilinear systems are attractive candidates for many dynamical processes, since they allow a significantly larger class of behaviour than linear systems, yet retain a rich theory which is closely related to the familiar theory of linear systems. A new technique for the control of unknown MIMO bilinear systems is introduced. The scheme is based on the U-model with identification based on radial basis functions neural networks which is known for mapping any nonlinear function. U-model is a control oriented model used to represent a wide range of non-linear discrete time dynamic plants. The proposed tracking scheme is presented and verified using simulation examples
DOI10.1109/ICIEA.2006.257063

3 DOF model of a helicopter

Model description: 

Under the aforementioned assumptions, dynamics of the 3DOF motion of “Helicopter” in a general form may be described by the following equations:

$\dot{\overrightarrow{K}} + \overrightarrow{\omega} \times \overrightarrow{K} =\overrightarrow{M},$ $\overrightarrow{K}=J\overrightarrow{\omega}$

$\omega_x=\dot{\theta}$, $\omega_y=\dfrac{\dot{\lambda}}{\cos{\epsilon}} - \dot{\theta}\tan{\epsilon},$ $\omega_z=\dot{\epsilon}$,

where $\overrightarrow{K}$ is a kinetic moment, $J$ is the inertia tensor, $\overrightarrow{M}$ is the sum of the moment of the propeller torques, the gravitational forces (bar plus the two motors) and the viscous friction torque. The vector $\omega$ is expressed in mobile coordinate system of the Helicopter.

The model describes nonlinear system with cross-talk coupling. At the stage of control law design, it is reasonable to make further simplifying assumptions. Since the propeller torque about the pitch axis does not depend on the travel and elevation angles (see attached image), and the moments of inertia about the travel and elevation axes are similar, the pitch motion may be considered independently of the other ones. In this way the following simplified model is obtained:

$\begin{align*} \ddot \theta &= - a_{m_x}^{\omega _x}\dot \theta - a_{m_x}^\theta \sin (\theta (t) - \theta _0) + a_{{m_x}}^v({v_f} - {v_r}) \\ \ddot \omega &= - a_{{m_z}}^{{\omega _z}}\dot \varepsilon - a_{{m_z}}^\varepsilon \sin (\varepsilon (t) - {\varepsilon _0}) - a_{{m_z}}^{{\omega _y}}\sin (2\varepsilon){{\dot \lambda }^2} + a_{{m_z}}^v({v_f} + {v_r})\cos \theta \\ \ddot \lambda &= - a_{{m_y}}^{{\omega _y}}\dot \lambda + a_{{m_y}}^v({v_f} + {v_r})\sin \theta. \end{align*} $

Parameters $a_j^i$ are considered to be unknown and subjected to estimation by means of adaptive identification algorithm. The constants $\theta_0$ and $\epsilon_0$ stand for pitch and elevation balance angles. The value of $\epsilon_0$ depends on the weight $M_x$ position (see attached image) and may be varying in different experiments at the operators commands (the “ADO option”). The term with $\lambda^2$ in the second equation describes influence of the “Helicopter” rotation about the vertical axis on the elevation caused by the centrifugal force. This term may be neglected if the travel motion is “sluggish”.

Type: 

Form: 

Time domain: 

Linearity: 

Attachment: 

Publication details: 

TitleAdaptive Identification of Angular Motion Model Parameters for LAAS Helicopter Benchmark
Publication TypeConference Paper
Year of Publication2007
AuthorsPeaucelle, Dimitri, Fradkov A.L., and Andrievsky B.
Conference NameIEEE International Conference on Control Applications, 2007.
Date Published2007
PublisherIEEE
Conference LocationSingapore
ISBN Number978-1-4244-0442-1
Accession Number9796858
Keywordsadaptive control, aircraft control, helicopters
AbstractThe paper is devoted to design and experimental testing the adaptive identification algorithms of pitch and elevation model parameters for "LAAS helicopter benchmark". The adaptive identification algorithms for separate pitch and elevation motions are proposed and the experimental results are presented. Laboratory experiments clarify the properties of the adaptive identification algorithms in real-world conditions.
DOI10.1109/CCA.2007.4389335

Lumped-parameter model of dual–drive gantry stages

Model description: 

The lumped-parameter model in the attached images can be described using eleven physical parameters.

  1. Four masses $m_b,m_k,m_1$ and $m_2$ corresponding to the mass of the beam, the moving head and actuators $X_1$ and $X_2$.
  2. Five friction coefficients $C_{g1}, C_{g2}, C_y, C_{b1}$ and $C_{b2}$, corresponding to the viscous friction coefficients of $X_1, X_2$, and $Y$ actuators, and to the damping coefficients of the flexible joints.
  3. Two stiffness coefficients $k_{b1}$ and $k_{b2}$ of the flexible joints of the beam to actuator junctions.

The beam and the head have lengths $L_b$ and $L_h$. The head's position is measured from the center of mass of the beam and is denoted by $Y$ and $d$, In the attached image, $X$ denotes the linear position of the center of mass of the beam and $\Theta$ is the yaw angle of the beam. They are defined as

$\begin{bmatrix} X \\ \Theta \\ Y \end{bmatrix} = \begin{bmatrix} 1/2 &1/2 & 0 \\ 1/L_{b} & -1/L_{b} & 0 \\ 0 & 0 &1 \end{bmatrix}\begin{bmatrix} X_1 \\ X_2 \\ Y \end{bmatrix}$

The Lagrange-Euler formalism is applied to derive the motion equations of the system.

$$M\ddot{q}+H\dot{q}+C\dot{q}+Kq=F,$$

where $M, C$ and $K$ are the inertia, viscous damping and stiffness matrices, $H$is the coriolis and centripetal acceleration matrix, $F$ is the vector of forces and $q$ is the vector of coordinates composed of $X, \Theta$ and $Y$ (8).

$\begin{align*} M &=\begin{bmatrix} M_{11} & M_{12} & m_{h}\sin(\Theta) \\ M_{12} & J_{t}+m_{h}Y^{2} &-m_{h}d \\ m_{h}\sin(\Theta) & -m_{h}d & m_{h} \end{bmatrix}, \\ H &=\begin{bmatrix} 0 & If_{12}\dot{\Theta} & -2m_{h}\dot{\Theta} & \cos(\Theta) \\ 0 & 2m_{h}Y\dot{Y} & 0 \\ 0 & -m_{h}Y\Theta & 0 \end{bmatrix}, \\ C &= \begin{bmatrix} C_{11} & C_{12} & 0 \\ C_{12} & C_{22} & 0 \\ 0 & 0 & c_{y} \end{bmatrix},\ K=\begin{bmatrix} 0 & 0 & 0 \\ 0 & K_{22} & 0 \\ 0 & 0 & 0 \end{bmatrix}, \\ F &=[F_{c}\ M_{c}\ F_{y}]^{\mathrm T},\ q=[X \ \Theta \ Y]^{\mathrm T }. \end{align*}$

Type: 

Form: 

Time domain: 

Linearity: 

Attachment: 

Publication details: 

TitleDecoupling basis control of dual-drive gantry stages for path-tracking applications
Publication TypeConference Paper
Year of Publication2010
AuthorsGarciaherreros, I., Kestelyn X., Gomand J., and Barre P.-J.
Conference NameIEEE International Symposium on Industrial Electronics (ISIE), 2010.
Date Published07/2010
PublisherIEEE
Conference LocationBari
ISBN Number978-1-4244-6390-9
Accession Number11653025
Keywordselectronic equipment manufacture, feedback, feedforward, flat panel displays, industrial control, inspection, MIMO systems, motion control, synchronisation
AbstractDual-drive gantry stages are used for high-speed high-precision motion control applications such as flat panel display manufacturing and inspection. Industrially, they are usually controlled using independent axis control without taking into consideration the effect of inter-axis mechanical coupling over positioning accuracy and precision. To improve this and minimize the effect of mechanical coupling over synchronization and tracking errors, we propose to model and control the dual-drive gantry stage on a decoupling basis. This approach allows representing the highly coupled Multiple Input Multiple Output (MIMO) system as a set of independent Single Input Single Output (SISO) systems. Based on this representation, a model-based feedback-feedforward control scheme is deduced. Experimental results show that the proposed decoupling basis control scheme leads to an improved motion control of the point-tool in comparison to the present industrial control.
DOI10.1109/ISIE.2010.5637612

Pages