Twin Rotor Helicopter Model

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Controlled Van der Pol system (1)

Model description: 

A controlled Van der Pol system can be described by the following equation

$$\begin{align*} \dot{x}_{1} &= x_{2} \\ \dot{x}_{2} &= -x_{1}+\epsilon (1-x_{1}^{2})x_{2}+u, \epsilon > 0 \\ y &= x_{1} \end{align*}$$

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Model order: 

2

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Publication details: 

TitleNonlinear sampled-data models and zero dynamics
Publication TypeConference Paper
Year of Publication2009
AuthorsNishi, M., Ishitobi M., and Kunimatsu S.
Conference NameInternational Conference on Networking, Sensing and Control, 2009. ICNSC '09.
Date Published03/2009
PublisherIEEE
Conference LocationOkayama
ISBN Number978-1-4244-3491-6
Accession Number10646009
Keywordsclosed loop systems, continuous time systems, control system synthesis, nonlinear control systems, poles and zeros, sampled data systems, stability
AbstractOne of the approaches to sampled-data controller design for nonlinear continuous-time systems consists of obtaining an appropriate model and then proceeding to design a controller for the model. Hence, it is important to derive a good approximate sampled-data model because the exact sampled-data model for nonlinear systems is often unavailable to the controller designers. Recently, Yuz and Goodwin have proposed an accurate approximate model which includes extra zero dynamics corresponding to the relative degree of the continuous-time nonlinear system. Such extra zero dynamics are called sampling zero dynamics. A more accurate sampled-data model is, however, required when the relative degree of a continuous-time nonlinear plant is two. The reason is that the closed-loop system becomes unstable when the more accurate sampled-data model has unstable sampling zero dynamics and a controller design method based on cancellation of the zero dynamics is applied. This paper derives the sampling zero dynamics of the more accurate sampled-data model and shows a condition which assures the stability of the sampling zero dynamics of the model.
DOI10.1109/ICNSC.2009.4919304

Two van der Pol oscillators coupled via a bath

Model description: 

consider a vibration system with nonlinear springs shown in the attached image. The normal form of the system is given by

$$\begin{align*} \dot\xi_{1}^{1} &= \xi_{2}^{1} \\ \dot{\xi}_{2}^{1}&=-\frac{k_1}{m_{1}}(\xi_{1}^{1}-\xi_{1}^{2})-{\bar{k} _{1}\over m_{1}}(\xi_{1}^{1}-\xi_{1}^{2})^{3}-{c_1\over m_{1}}(\xi_{2}^{1}-\xi_{2}^{2})+{u_1\over m_{1}} \\ \dot{\xi}_{1}^{2} &= \xi_{2}^{2} \\ \dot\xi_{2}^{2}&=\frac{k_1}{m_{2}}(\xi_{1}^{1}-\xi_{1}^{2})+{\bar{k} _{1}\over m_{2}}(\xi_{1}^{1}-\xi_{1}^{2})^{3}+{c_1\over m_{2}}(\xi_{2}^{1}-\xi_{2}^{2}) \\ &-{k_2\over m_{2}}(\xi_{1}^{1}-\xi_{1}^{2})-{k_2\over m_{2}}(\xi_{1}^{1}-\xi_{1}^{2})^{3}-{c_2\over m_{2}}(\xi_{2}^{1}-\xi_{2}^{2})+{u_2\over m_{2}} \\ \dot{\eta}_{1} &= \eta_{2} \\ \dot{\eta}_{2} &= {k_2\over m_{3}}(\xi_{1}^{2}-\eta_{1})+{\bar{k} _{2}\over m_{3}}(\xi_{1}^{2}-\eta_{1})^{3}+{c_2\over m_{3}}(\xi_{2}^{2}-\eta_{2}) \\ &-{k_3\over m_{3}}\eta_{1}-{\bar{k} _{3}\over m_{3}}\eta_{1}^{3}-{c_3\over m_{3}}\eta_{2} \\ y_{1} &=\xi_{1}^{1} \\ y_{2} &=\xi_{1}^{2}, \end{align*}$$

where parameters $m_i, k_i$ and $\bar{k} (i=1,2,3)$ are positive constants. The system has the relative degrees $r_1 = 2, r_2 = 2$ and the following zero dynamics.

$\begin{align*} \dot{\eta}_{1} &=\eta_{2} \\ \dot{\eta}_{2} &=-\frac{k_2}{m_3}\eta_{1}-\frac{\bar{k}_2}{m_3}\eta_1^3+\frac{c_2}{m_3}(\xi_2^2-\eta_2) \\ &-\frac{k_3}{m_3}\eta_1-\frac{\bar{k}_3}{m_3}\eta_1^3-\frac{c_3}{m_3}\eta_2 \end{align*}$$

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Publication details: 

TitleSampled-Data Models for Decouplable Nonlinear Multivariable Systems
Publication TypeConference Paper
Year of Publication2010
AuthorsNishi, M., Ishitobi M., Liang Shan, and Kunimatsu S.
Conference NameProceedings of SICE Annual Conference 2010
Date Published08/2010
PublisherIEEE
Conference LocationTaipei
ISBN Number978-1-4244-7642-8
Accession Number11594970
Keywordscontinuous time systems, control system synthesis, MIMO systems, nonlinear control systems, sampled data systems
AbstractOne of the approaches to sampled-data controller design for nonlinear continuous-time systems consists of obtaining an appropriate model and then proceeding to design a controller for the model. Few studies have been investigated for obtaining sampled-data models of nonlinear multi-input multi-output systems (MIMO system) though we can find studies which consider nonlinear single-input single-output systems. This paper shows a more accurate sampled-data model than the Euler model for nonlinear multi-input multi-output systems and derives sampling zero dynamics of the model.
URLhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5602233

Two van der Pol oscillators coupled via a bath (1)

Model description: 

Consider nonlinear van der Pol oscillators coupled via a bath. The normal form of the system is expressed by

$$\begin{align*} \dot{\xi}_{1}^{1}&=\xi_{2}^{1} \\ \dot{\xi}_{2}^{1}&=-\xi_{1}^{1}+\epsilon\{1-(\xi_{1}^{1})^{2}\}\xi_{2}^{1}+k(\eta_{1}-\xi_{1}^{1})+u_{1} \\ \dot{\xi}_{1}^{2}&=\xi_{2}^{2} \\ \dot{\xi}_{2}^{2}&=-\xi_{1}^{2}+\epsilon\{1-(\xi_{1}^{2})^{2}\}\xi_{2}^{2}+k(\eta_{1}-\xi_{1}^{2})+u_{2} \\ \dot{\eta}_{1}&=k(\xi_{1}^{1}-\eta_{1})+k(\xi_{1}^{2}-\eta_{1}) \\ y_{1}&=\xi_{1}^{1} \\ y_{2}&=\xi_{1}^{2}, \end{align*}$$

where parameters $\epsilon$ and $k$ are positive constants. The system has the relative degrees $r_1 = 2, r_2 = 2$ and the following zero dynamics:

$ \dot{\eta}_{1}=-2k\eta_{1}.$

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Publication details: 

TitleSampled-Data Models for Decouplable Nonlinear Multivariable Systems
Publication TypeConference Paper
Year of Publication2010
AuthorsNishi, M., Ishitobi M., Liang Shan, and Kunimatsu S.
Conference NameProceedings of SICE Annual Conference 2010
Date Published08/2010
PublisherIEEE
Conference LocationTaipei
ISBN Number978-1-4244-7642-8
Accession Number11594970
Keywordscontinuous time systems, control system synthesis, MIMO systems, nonlinear control systems, sampled data systems
AbstractOne of the approaches to sampled-data controller design for nonlinear continuous-time systems consists of obtaining an appropriate model and then proceeding to design a controller for the model. Few studies have been investigated for obtaining sampled-data models of nonlinear multi-input multi-output systems (MIMO system) though we can find studies which consider nonlinear single-input single-output systems. This paper shows a more accurate sampled-data model than the Euler model for nonlinear multi-input multi-output systems and derives sampling zero dynamics of the model.
URLhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5602233

Inverted Pendulum on a Cart

Model description: 

The inverted pendulum on cart to be controlled is shown in the attached image. Its structure consists of a cart and pendulum where the pendulum is hinged to the cart via a pivot and only the cart is actuated, where:

$\theta$ is the pendulum angle $rad$
$x$ is the cart position $m$
$M$ is the mass of the cart $kg$
$m$ is the mass of the pendulum $kg$
$l$ is distance from turning center to center of mass of the pendulum $m$
$f$ is the cart's friction coefficient $kg/s$
$F$ is force applied to the cart $N$

Lagrange's equations are applied with respect to $\theta$ and $x$ coordinates. The nonlinear state space model of inverted pendulum on cart system can then be obtained as:

$$\begin{align*} \dot{x}_1 &= x_2 \\ \dot{x}_2 &= \dfrac{mg \sin{x_1}+ \dfrac{ m\cos{x_1}(u-f\dot{x})-m^2lx_2^2\cos{x_1}\sin{x_1}}{(M + m)}}{\dfrac{4}{3}ml - \dfrac{m^2l\cos^2{x_1}}{(M+m)}} \\ \dot{x}_3 &= x_4 \\ \dot{x}_4 &= \dfrac{u - f x_1 + \dfrac{3}{4}mg\cos{x_1}\sin{x_1} - mlx_2^2\sin{x_1}}{(M+m) - \dfrac{3}{4}m\cos^2{x_1}}, \end{align*}$$

where the state variables are consequently assigned as $x_1 = \theta$, $x_2 = \dot{\theta}$, $x_3 = x$ an $x_4=\dot{x}$, and where the input $u$ is the applied force $F$.

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4

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TitleHybrid Controller for Swinging up Inverted Pendulum System
Publication TypeConference Paper
Year of Publication2005
AuthorsNundrakwang, S, Benjanarasuth T, Ngamwiwit J, and Kominet N
Conference Name2005 Fifth International Conference on Information, Communications and Signal Processing
PublisherIEEE
Conference LocationBangkok
ISBN Number0-7803-9283-3
Accession Number9097400
Keywordscontrol system synthesis, linear quadratic control, PD control, pendulums, position control, servomechanisms, state feedback
AbstractA hybrid controller for swinging up inverted pendulum system is proposed in this paper. The controller composes of two parts. The first part is the PD position control for swinging up the pendulum from the natural pendent position by moving the cart back and forth until the pendulum swings up around the upright position. The second part is a servo state feedback control designed by LQR which will be switched to stabilize the inverted pendulum in its upright position. The effectiveness and reliability of the proposed hybrid controller for swinging up inverted pendulum on cart are also shown by the experimental results
DOI10.1109/ICICS.2005.1689094

Twin Rotor Helicopter Model

Model description: 

The nonliear model for elevator subsystem is as follows:

$$ f(x,u) = \begin{bmatrix} x_2\\ \dfrac{1}{I}(-\tau_g \sin{x_1} +k_{gyro}(u_1x_6\cos{x_1})- B_{\psi} x_2 + a_1 x_3 ^2 + b_1 x_3 )\\ x_4 \\ \dfrac{1}{T_1^2}(u_1 -x_3-2T_1x_4)\\ x_6 \\ \dfrac{1}{I_{\phi}} \left(-B_{\phi}x_6 - \left[K_r \dfrac{T_{or}}{T_{pr}}u_1 + x_9\right] + a_2x_7^2 + b_2x_7\right) \\ x_8 \\ \dfrac{1}{T_2^2}(u_2 - x_7 -2T_2x_8)\\ \dfrac{1}{T_{pr}}\left[K_r \left(1- \dfrac{T_{or}}{T_{pr}}\right)u_1 - x_9\right]\\ \end{bmatrix},$$

where $x_1$ is the elevator angle, $x_5$ is azimuth angle, and $g(x)=\begin{pmatrix}x_1k_{\psi} + y_{\psi \circ} \\ x_2k_{\phi} + y_{\phi \circ }\end{pmatrix}$

$y_{\psi}$ Elevator angle read by sensor
$k_{\psi}$ Elevator constant
$y_{\psi \circ}$ Elevator angle offset
$y_{\phi}$ Azimuth angle read by the sensor
$k_{\phi}$ Azimuth constant
$y_{\phi \circ}$ Azimuth angle offset
$u_a$ Control coltage applied to rotors
$k$ Amplifier gain

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Publication details: 

TitleRobust Feedback Linearization Control for a non Linearizable MIMO Nonlinear System in the Presence of Model Uncertainties
Publication TypeConference Paper
Year of Publication2006
AuthorsKarimi, H.R, and Motlagh M.R.J
Conference NameIEEE International Conference on Service Operations and Logistics, and Informatics
Date Published06/2006
PublisherIEEE
Conference LocationShanghai
ISBN Number1-4244-0317-0
Accession Number9165735
Keywordsaircraft control, control nonlinearities, control system synthesis, helicopters, linearisation techniques, Lyapunov methods, MIMO systems, nonlinear control systems, robust control, rotors, state feedback, uncertain systems
AbstractDuring the last decades a considerable progress has been made in the design of stabilizing controllers for nonlinear systems with known and unknown model. Feedback linearization approach via coordinate transformation is considered to be useful to tackle the control problem. Usually however, feedback linearization control does not guarantee exact linearization and robustness in the presence of uncertainties. Meanwhile most of the results developed are applicable to single-input feedback-linearizable systems. In this paper in order to cope the model uncertainties of a non linearizable MIMO nonlinear system, a robust feedback linearization scheme based on Lyapunov function is proposed. To verify the validity and effectiveness of the designed method, the suggested technique is applied to a twin rotor system
DOI10.1109/SOLI.2006.328881

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