Laboratory Scale Liquid Level System

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The State Dependent Model of the Helicopter

Model description: 

The helicopter which is the subject of this paper was developed by Humusoft as the 2 degrees of freedom educational model. The model is a multidimensional, unstable nonlinear system with two manipulated inputs and two measured outputs. It has also significant cross couplings. The system consists of the body, carrying two propellers driven by DC motors, and a massive support (See attached image). The body has two degrees of freedom. Both body position angles (horizontal and vertical) are influenced by rotation of propellers. The axes of a body rotation are perpendicular. Power amplifiers, with a pulse width modulation, drive the DC motors. Both angles are measured. Helicopter model is described by the non-linear state-space equations. The model has nine states, two inputs, which are the control signals for main and side propeller motors. The two outputs are the elevation and azimuth angles.

The dynamics of the helicopter are represented by the following non-linear continuous time state space model:

$$\begin{align*} \dot{x}_1 &= x_2 \\ \dot{x}_2 &= \dfrac{1}{I_\psi} (-\sin{x_1} \cdot \tau_g -x_2 b_{\psi} + a_1(x_3)^2 + b_1x_3 - k_{gyro} \cdot \cos{x_1} \cdot x_6 \cdot u_1) \\ \dot{x}_3 &= -\dfrac{1}{T_1}x_3 + \dfrac{1}{T_1}x_4 \\ \dot{x}_4 &= -\dfrac{1}{T_1}x_4 + \dfrac{1}{T_1}u_1 \\ \dot{x}_5 &= x_6 \\ \dot{x}_6 &= \dfrac{1}{I_{\phi}} \left(-x_6 \cdot b_{\phi} + a_2 (x_7)^2 + b_2 x_7 - x_9 - \dfrac{k_r t_{0r}}{t_{pr}}u_1\right) \\ \dot{x}_7 &= -\dfrac{1}{T_2}x_7 + \dfrac{1}{T_2}x_7 \\ \dot{x}_8 &= -\dfrac{1}{T_2}x_x + \dfrac{1}{T_2}u_2 \\ \dot{x}_9 &= -\dfrac{1}{t_{pr}}x_9 + \left(\dfrac{k_r}{t_{pr}} + \dfrac{k_r t_{0r}}{t_{pr}}\right)u_1, \end{align*}$$

where $I_{\psi}$, $b_{\psi}$, $\tau_g$, $k_{gyro}$, $I_{\phi}$, $b_{\phi}$, $k_{r}$, $t_{0r}$, $t_{pr}$, $a_1$, $b_1$, $a_2$, $b_2$, $k_{\psi}$, $k_{\phi}$ are the constant parameters.

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Model order: 

9

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Publication details: 

TitleNon-linear predictive control of 2 DOF helicopter model
Publication TypeConference Paper
Year of Publication2003
AuthorsDutka, A.S, Ordys A.W, and Grimble M.J.
Conference NameProceedings on Decision and Control, 2003.
Date Published12/2003
PublisherIEEE
ISBN Number0-7803-7924-1
Accession Number7929673
Keywordsaircraft control, helicopters, nonlinear control systems, predictive control, state-space methods, time-varying systems
AbstractThis paper presents the application of non-linear predictive control algorithm to a helicopter model. First, the model of the helicopter is discussed. Next, the nonlinear algorithm is introduced which is based on state-space GPC controller. The non-linearity is handled by converting the state-dependent state-space representation into the linear time-varying representation. The predictions of the future controls are used to calculate predictions of the future states and of the future time varying system parameters. Applied to the helicopter model, the algorithm performs well. It is capable of the stabilizing the system for maneuvers for which it's linear counterpart fails.
DOI10.1109/CDC.2003.1271768

Simplified Schmid pendulum

Model description: 

Simplified Schmid pendulum:

$$\begin{align*} \ddot{\psi} + a_{21}\omega - a_{11}\sin{\psi} &= -b_1u, \\ \dot{\omega} + a_{22}\omega + a_{12}\sin{\psi} &= b_2u, \end{align*}$$

where $\psi$ is the pendulum angle; $\omega$ is the wheel angular rate; $u$ is the controlling voltage, applied to the motor; $a_{11},a_{21},a_{12},b_1,b_2$ are positive constants, depending on the design parameter of the pendulum. It is assumed that the upper (unstable) equilibrium point corresponds to $\psi=0$.

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Publication details: 

TitleHybrid quantised observer for multi-input-multi-output nonlinear systems
Publication TypeConference Paper
Year of Publication2008
AuthorsFradkov, Alexander L., Andrievsky Boris, and Evans Robin J.
Conference NameIEEE International Conference on Control Applications, 2008. CCA 2008.
Date Published09/2008
PublisherIEEE
Conference LocationSan Antonio, Texas, USA
ISBN Number978-1-4244-2222-7
Accession Number10235153
KeywordsIMO systems, nonlinear control systems, observers, oscillators, pendulums
AbstractLimit possibilities of state estimation under information constraints (limited information capacity of the coupling channel) for multi-input-multi-output (MIMO) nonlinear systems are evaluated. We give theoretical analysis for state estimation of nonlinear systems represented in Lurie form (linear part plus nonlinearity depending only on measurable outputs) with a first-order coder-decoder. It is shown that the upper bound of the limit estimation error is proportional to the upper bound of the transmission error. As a consequence, the upper bound of limit estimation error is proportional to the maximum rate of the coupling signal and inversely proportional to the information transmission rate (channel capacity). The results are applied to state estimation of a nonlinear self-excited mechanical oscillator and a reaction-wheel pendulum.
DOI10.1109/CCA.2008.4629572

Self-excited nonlinear oscillator

Model description: 

Self-excited nonlinear oscillator:

$$\begin{align*} \dot{x}_1 &=x_2,\\ \dot{x}_2 &=-\omega_1^2\sin{y_1}-\varrho x_2 + k_p \arctan(k_c y_2),\\ \dot{x}_3 &=\omega_2 \cdot (x_1 - x_3), \\ y_1 &=x_1, \\ y_2 &=x_1-x_3, \end{align*}$$

where $y(t)\in \mathbb{R}^2$ is the sensor output vector (to be transmitted over the communication channel), $\omega_1, \omega_2, \varrho, k_p, k_c$ are system parameters, $x=[x_1,x_2,x_3]^{\mathrm T}\in\mathbb{R}^3$ of the system state vector $x(t)$ based on the signals $y_1(t),y_2(t)$, transmitted over the communication channel.

The system has the form

$\dot{x}(t)=Ax(t)+\varphi(y(t)),$ $y(t)=Cx(t),$

where

$A=\begin{bmatrix}0 & 1 & 0 \\ 0 & -\varrho & 0 \\ \omega_2 & 0 & -\omega_2\end{bmatrix},$

$C = \begin{bmatrix}1, & 0, & 0\\ 1, & 0, & -1\end{bmatrix},$

$ \varphi(y) = \begin{bmatrix} 0\\ \omega_1^2 \sin{y_1} + k_p \arctan{(k_c y_2)} \\ 0 \end{bmatrix}.$

Type: 

Form: 

Model order: 

3

Time domain: 

Linearity: 

Publication details: 

TitleHybrid quantised observer for multi-input-multi-output nonlinear systems
Publication TypeConference Paper
Year of Publication2008
AuthorsFradkov, Alexander L., Andrievsky Boris, and Evans Robin J.
Conference NameIEEE International Conference on Control Applications, 2008. CCA 2008.
Date Published09/2008
PublisherIEEE
Conference LocationSan Antonio, Texas, USA
ISBN Number978-1-4244-2222-7
Accession Number10235153
KeywordsIMO systems, nonlinear control systems, observers, oscillators, pendulums
AbstractLimit possibilities of state estimation under information constraints (limited information capacity of the coupling channel) for multi-input-multi-output (MIMO) nonlinear systems are evaluated. We give theoretical analysis for state estimation of nonlinear systems represented in Lurie form (linear part plus nonlinearity depending only on measurable outputs) with a first-order coder-decoder. It is shown that the upper bound of the limit estimation error is proportional to the upper bound of the transmission error. As a consequence, the upper bound of limit estimation error is proportional to the maximum rate of the coupling signal and inversely proportional to the information transmission rate (channel capacity). The results are applied to state estimation of a nonlinear self-excited mechanical oscillator and a reaction-wheel pendulum.
DOI10.1109/CCA.2008.4629572

Attitude Control of a Helicopter

Model description: 

Attached image depicts the model of this helicopter. The dynamics of this model simulate the attitude dynamics of a helicopter. Using Lagrange’s equations, one may readily show that the dynamical model of this system is given by:

$$\begin{align*} T_p &= I_x\ddot{\phi}-(I_y-I_z)\dot{\psi}^2S_{\phi}C_{\phi}+mgAC_{\phi} \\ T_y &= (I_yS_{\phi}^2 + I_zC_{\phi}^2)\ddot{\psi} + 1(I_y - I_z)\dot{\psi}\dot{\phi}S_{\phi}C_{\phi}, \end{align*}$$

where $\phi$ is the pitch angle in radians, $\psi$ is the yaw angle in radians, $I_x, I_y, I_z$ are inertia constants about the point of rotation, m is the total mass of the system, and $T_p, T_y$ are the pitch and yaw control torques, respectively.

The above can be written in general form:

$u = M(q,\delta)\ddot{q}+C(q,\dot{q},\delta)\dot{q}+G(q,\dot{q},\delta),$

where

$M(q,\delta) = \begin{bmatrix} I_x & 0\\ 0 & I_yS_{\phi}^2+I_zC_{\phi}^2\end{bmatrix}$

$G(q,\delta)=\begin{bmatrix}mgAC_{\phi}\\0\end{bmatrix}$

$C(q,\dot{q},\delta)=\begin{bmatrix}0&-(I_y-I_z)\dot{\psi}S_{\phi}C_{\phi} \\ (I_y-I_z)\dot{\psi}S_{\phi}C_{\phi} &(I_y-I_z)\dot{\phi}S_{\phi}C_{\phi}\end{bmatrix} $

$q=\begin{bmatrix}\phi \\ \psi\end{bmatrix}$

$u=\begin{bmatrix}T_p \\ T_y\end{bmatrix}$

The nominal values of the model parameters are $m=0.5719 kg$, $A = 0.0801 m^2$, $I_x = 0.0762 kg\cdot m^2$, $I_y = 3.86\times10^{-4}kg\cdot m^2$, $I_z = 0.0766 kg\cdot m^2,$ and $g = 9.81 m/sec^2$. It is straight forward to show that $[\dot{M}(q)-2C(q,\dot{q})]$ is skew symmetric.

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Publication details: 

TitleRobust Control of Uncertain Nonlinear Mechanical Systems Using a High Gain Observer
Publication TypeConference Paper
Year of Publication2000
AuthorsZenieht, Salah, and Elshafe Abdel Latif
Conference NameProceedings of the American Control Conference Chicago
Date Published06/2000
PublisherIEEE
Conference LocationChicago, Illinois
ISBN Number0-7803-5519-9
Accession Number6795603
Keywordsaircraft control, convergence, helicopters, nonlinear systems, observers, robust control, singularly perturbed systems, state feedback, tracking, uncertain systems
AbstractWe consider a general class of nonlinear uncertain mechanical systems. All uncertain terms belong to a known, nonempty compact set. A previously derived $r-\alpha$ robust, tracker that, guaraatees global exponential convergence of the tracling error via state feedback is extended to achieve semi-global tracking using output feedback. The proposed controller has the same structure as the state feedback controller, however, all missing states are estimated using a high gain observer. The high gain observer proposed in this paper generalizes an existing observer in the literature for SISO systems to MIMO systems. The interconnection between the observer dynamics and the nonlinear mechanical dynamics are cast into a singularly perturbed system. This technique proves that the full order system closely approximates the behavior of the reduced order system, in this case a full state feedback. Simulation results for the attitude stabilization of a helicopter model are also included for illustration.
DOI10.1109/ACC.2000.879245

Laboratory Scale Liquid Level System

Model description: 

The system consists of a DC water pump feeding a conical flask which in turn feeds a square tank, giving the system second-order dynamics. The controllable input is the voltage to the pump motor and the system output is the height of the water in the conical flask. The aim, under simulation conditions, is for the water height to follow some demand signal. The plant model was identified as

$$\begin{align*}z(t) &=0.9722z(t-1)+0.3578u(t-1)-0.1295u(t-2)-\\ &-0.3103z(t-1)u(t-1)-0.04228z^6(t-2)+0.1663z(t-2)u(t-2)+\\ &+0.2573z(t-2)e(t-1)-0.03259z^2(t-1)z(t-2) - 0.3513z^2(t-1)u(t-2)+\\ &+0.3084z(t-1)z(t-2)u(t-2)+0.2939z^2(t-2)e(t-1)+\\ &+0.1087z(t-2)u(t-1)u(t-2)+0.4770z(t-2)u(t-1)e(t-1)+\\ &+0.6389u^2(t-2)e(t-1)+e(t), \end{align*}$$

where $e(t)$ is a noise.

Type: 

Form: 

Model order: 

2

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Publication details: 

TitleSelf-tuning control of non-linear ARMAX models
Publication TypeJournal Article
Year of Publication1990
AuthorsSales, K. R., and Billings S. A.
JournalInternational Journal of Control
Volume51
Issue4
Pagination753-769
Date Published01/1990
ISSN1366-5820
AbstractA control-weighted self-tuning minimum-variance controller with a non-linear difference equation structure is described. An extended recursive least-squares estimation algorithm is employed to provide the adaptiveness. Performance analysis of the controller is discussed in terms of a cumulative loss function and high-order correlation functions of the system input, output and residuai sequences. Simulation results from an experiment using a model identified from a real system are also provided.
DOI10.1080/00207179008934096

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