Tension leg platform system

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A single link robotic manipulator

Model description: 

The dynamic equations governing the behavior of a single link robot with flexible joint are traditionally obtained from Lagrangian dynamics considerations. The simple robot system under study is shown in the attached image. Let $x_1=\theta_m$ be the motor angular position, the corresponding angular velocity $x_2 = d\theta/dt$, the elastic force $x_3 = k_s(\theta_t - \theta_m)$ and $x_4 = \{ d\theta_l/dt - d\theta_m/dt\}/\rho$, where $\rho^2=1/k_s$. Then the state variable representation is:

$$\begin{align*} \dot{x}_1(t) &= x_2(t)\\ \dot{x}_2(t) &= -a_5x_2(t)+a_1x_3(t)+a_1u(t)\\ \dot{x}_3(t) &= x_4(t)/\rho\\ \dot{x}_4(t) &= \{ -a_2a_3\sin{[\rho^2x_3(t)+x_1(t)]}-a_4x_3(t)-a_7x_2(t)-a_6\rho x_4(t) - a_1u(t)\}/\rho \end{align*}$$

with $a_1=1/J_m$, $a_2=1/J_l$, $a_3=mgl$,$a_4=a_1+a_2$,$a_5=B_m/J_m;a_6=B_l/J_l$,$a_7=a_6-a_5$ and $u(t)=\tau(t)$.

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TitlePhysical parameter estimation of the nonlinear dynamics of a single link robotic manipulator with flexible joint using the HMF method
Publication TypeConference Paper
Year of Publication1997
AuthorsDaniel-Berhe, S., and Unbehauen H.
Conference NameProceedings of the 1997 American Control Conference, 1997.
Date Published06/1997
PublisherIEEE
Conference LocationAlbuquerque, NM
ISBN Number0-7803-3832-4
Accession Number6016897
Keywordsalgebra, continuous time systems, manipulator dynamics, Nonlinear dynamical systems, parameter estimation
AbstractThe application of the Hartley modulating functions (HMF) method is investigated to estimate the physical parameters of a single link robotic manipulator with flexible joint. The approach uses a weighted least-squares algorithm in the frequency domain. Knowing the structure of a continuous-time system, the identification method will only focus on the estimation of the physically-based system parameters using input and noise-corrupted output signal records. The methodology facilitates the conversion of a system differential equation into an algebraic equation in the parameters. Numerical simulations for a single link robotic manipulator with flexible joint are reported, which illustrate the application and performance of the methodology. The HMF method shows promising results for the identification of physically-based continuous-time nonlinear systems in the presence of noticeable measurement noises
DOI10.1109/ACC.1997.610763

VTOL system

Model description: 

Consider the VTOL example. The following shortcuts ${\rm c}(\cdot)=\cos(\cdot),\quad {\rm s}(\cdot)=\sin(\cdot)$ are used. The $\Delta_0$ is given as

$$\begin{align*} \omega_{0}^{1} &: {\mathrm d}x-v_{x}{\mathrm d}t \\ \omega_{0}^{2} &: {\mathrm d}v_{x}-(u^{2}\epsilon {\mathrm c}(\theta)-u^{1}{\mathrm s}(\theta)){\mathrm d}t \\ \omega_{0}^{3} &: {\mathrm d}z-v_{z}{\mathrm d}t \\ \omega_{0}^{4} &: {\mathrm d}v_{z}-(u^{1}{\mathrm c}(\theta)+u^{2}\epsilon {\mathrm s}(\theta)-1){\mathrm d}t \\ \omega_{0}^{5} &: {\mathrm d}\theta-\omega {\mathrm d}t \\ \omega_{0}^{6} &: {\mathrm d}\omega-u^{2}{\mathrm d}t, \end{align*}$$

where $\epsilon$ is a constant parameter. It can be shown that $\Delta_{0,\mathrm{d}t}^{\perp} = \mathrm{span}\{\delta_{u^1},\delta_{u^2}\}$. Construct $\Delta_1 \in \Delta_0$ such that $v_0(\Delta_1)\in \Delta_1$ holds with $v_0=\Delta_{0,{\mathrm d}t}^{\perp}$, i.e. $\Delta_1$ is given as:

$\begin{align*} \omega_{1}^{1} &: {\mathrm d}x-v_{x}{\mathrm d}t \\ \omega_{1}^{2} &: {\mathrm c}(\theta){\mathrm d}v_{x}+{\mathrm s}(\theta){\mathrm d}v_{z}-\epsilon {\mathrm d}\omega+{\mathrm s}(\theta){\mathrm d}t \\ \omega_{1}^{3} &: {\mathrm d}z-v_{z}{\mathrm d}t \\ \omega_{1}^{4} &: {\mathrm d}\theta-\omega {\mathrm d}t \end{align*}$

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TitleOn calculating flat outputs for Pfaffian systems by a reduction procedure - Demonstrated by means of the VTOL example
Publication TypeConference Paper
Year of Publication2011
AuthorsSchoberl, M., and Schlacher K.
Conference Name9th IEEE International Conference on Control and Automation (ICCA), 2011
Date Published12/2011
PublisherIEEE
Conference LocationSantiago
ISBN Number978-1-4577-1475-7
Accession Number12496118
Keywordsaircraft control, machinery, partial differential equations, sequences
AbstractThis paper addresses the problem of generating a flat system parametrization for Pfaffian systems in a constructive manner. The main idea behind the procedure is a subsequent application of transformations that decompose a given Pfaffian system into a sequence of systems. This splitting of a Pfaffian system possesses the property that a parametrization of the bottom part can be elementarily obtained provided a rank criterion is met and the parametrization for the upper part is known. Then (if possible) this procedure will be repeated with the upper system to generate a sequence of systems by gradual reduction of the complexity of the problem. The application of the whole machinery to the VTOL example will demonstrate the effectiveness of the procedure. In fact the well known flat output for the VTOL and an alternative one are derived using this constructive machinery in a systematic fashion.
DOI10.1109/ICCA.2011.6137922

Two-link Rigid Robot Manipulator

Model description: 

Consider a two-link rigid robot manipulator moving a horizontal plane. The dynamic equations of this MIMO system are

$$\left[\matrix{ \ddot{q}_{1}\cr \ddot{q}_{2} }\right]=\left[\matrix{ M_{11} & M_{12}\cr M_{21} & M_{22} }\right]^{-1} \left\{\left[\matrix{ u_{1}\cr u_{2} }\right]-\left[\matrix{ -h\dot{q}_{2} & -h(\dot{q}_{1}+\dot{q}_{2})\cr h\dot{q}_{1} & 0 }\right]\left[\matrix{ \dot{q}_{1}\cr \dot{q}_{2} }\right]\right\},$$

where

$\begin{align*} M_{11}&=a_{1}+2a_{3}\cos(q_{2})+2a_{4} \sin (q_{2}),\ M_{22}=a_{2} \\ M_{12}&=M_{21}=a_{2}+\alpha_{3}\cos(q_{2})+a_{4}\sin(q_{2}) \\ h&=a_{3}\sin(q_{2})-a_{4}\cos(q_{2}) \end{align*}$

with

$\begin{align*} a_{1}&=I_{1}+m_{1}l_{c1}^{2}+I_{e}+m_{e}l_{ce}^{2}+m_{e}l_{1}^{2} \\ a_{2}&=I_{e}+m_{e}l_{ce}^{2} \\ a_{3}&=m_{e}l_{1}l_{ce}\cos(\delta_{e}) \\ a_{4}&=m_{e}l_{1}l_{ce}\sin(\delta_{e}). \end{align*}$

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TitleIndirect adaptive fuzzy control for a class of MIMO nonlinear systems with unknown control direction
Publication TypeConference Paper
Year of Publication2010
AuthorsWuxi, Shi
Conference Name29th Chinese Control Conference (CCC), 2010
Date Published06/2010
PublisherIEEE
Conference LocationBeijing
ISBN Number978-1-4244-6263-6
Accession Number11612096
Keywordsadaptive control, approximation theory, closed loop systems, fuzzy control, matrix algebra, MIMO systems, nonlinear control systems, uncertain systems
AbstractIn this paper, an indirect adaptive fuzzy controller is developed for a class of uncertain MIMO nonlinear systems with unknown sign of the control gain matrix. Within this scheme,the fuzzy logic systems are used to approximate the plant's unknown nonlinear functions. The estimated gain matrix is decomposed into the product of one diagonal matrix and two orthogonal matrixes. In order to compensate the lumped errors,all parameter adaptive laws are adjusted by the time-varying dead-zone of the filtered tracking errors,which its size is adjusted adaptively with the estimated bounds on the approximation errors. The proposed scheme guarantees that all the signals in the resulting closed-loop system are bounded, and the tracking error converges to a small neighborhood of the origin. A simulation example is used to demonstrate the effectiveness of the proposed scheme.
URLhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5572851&queryText%3DIndirect+Adaptive+Fuzzy+Control+for+a+Class+of+MIMO+Nonlinear+Systems+with+Unknown+Control+Direction

Muscle-knee state space model

Model description: 

The state model of the knee-quadriceps can be expressed as

$$\begin{cases} \begin{align*} \dot{x}_1 &= \left[ s_0 \alpha K_m + s_v q\dfrac{s_0\alpha F_mx_1 - s_ux_2x_1}{1 + px_1 - s_vqx_2}\right] u_{ch} - s_ux_1u_{ch} - \dfrac{s_v ax_1 r_p x_4}{L_0 (1+px_1-s_vqx_2)}\\ \dot{x}_2 &= \left[ \dfrac{s_0\alpha F_m - s_ux_2}{1 + px_1 - s_vqx_2} \right]u_{ch} + \dfrac{bx_1r_px_4 - s_vax_2r_px_4}{L_0(1+px_1-s_vqx_2)}\\ \dot{x}_3 &= x_4\\ \dot{x}_4 &= \dfrac{1}{I}[x_2r_p - \lambda x_3 - \mu x_4 - mgl_c \cos{x_3}], \end{align*} \end{cases}$$

where $\textbf{x}=[x_1, \ldots, x_4]^{\mathrm T} = [K_c, F_c, \theta, \dot{\theta}]^{\mathrm T}$ is the state vector and $\textbf{u}=[u_{ch},\alpha ]^{\mathrm T}$ the control vector. The variable $\theta$ represents the knee joint angle and the variables $K_c, F_c, u_{ch}, \alpha$ represent the state variables of the quadriceps muscle model.

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TitleToward lower limbs movement restoration with input-output feedback linearization and model predictive control through functional electrical stimulation
Publication TypeJournal Article
Year of Publication2012
AuthorsMohammed, S., Poignet P., Fraisse P., and Guiraud D.
JournalControl Engineering Practice
Volume20
Issue2
Pagination182-195
Date Published02/2012
ISSN0967-0661
KeywordsFunctional electrical stimulation, Input–output feedback linearization, Model predictive control, Muscle modeling, Rehabilitation engineering
DOI10.1016/j.conengprac.2011.10.010

Tension leg platform system

Model description: 

The present study of tension leg platform is the first commercial application of a revolutionary design of offshore production platform developed by well-known oil company. Intended for oil and gas production in water depths beyond the reach of traditional fixed structures, the tension leg platform was designed as a rectangular shaped floating platform which was connected to the ocean floor by 16 vertical steel tethers or legs, four per corner. The legs were kept in tension so that vertical movement was suppressed, while limited horizontal movement may occur.

The estimation model is:

$$\begin{align*} y(k) &= 0.590y(k − 3)+1.0598y(k − 1) − 1.0931y(k − 2) + 121.13u(k − 1)u(k− 1)u(k − 9) \\ &− 116.54u(k − 6)u(k − 6)u(k− 6) − 19.797u(k − 4)u(k − 8)u(k − 8) \\ &+ 214.04u(k − 5)u(k − 9) − 34.877u(k− 1)u(k − 1)u(k − 1) − 3.7983u(k − 1)u(k− 2)u(k − 7) \\ &− 25.04u(k − 4)u(k − 8)u(k− 11) + 165.93u(k − 2)u(k − 3)u(k − 4) \\ &− 173.85u(k − 6)u(k − 7) − 69.693u(k− 4)u(k − 12) + 203.12u(k − 5)u(k − 6)u(k − 6) \\ &+ 727.86u(k − 2)u(k − 3)u(k − 5) − 11.107u(k− 3)u(k − 10)u(k − 11) \\ &+ 11.506u(k − 6)u(k− 6)u(k − 12) − 68.607u(k − 2)u(k − 4)u(k− 6) \\ &− 366.75u(k − 3)u(k − 5)u(k − 6)− 25.696u(k − 4)u(k − 8)u(k − 12) \\ &+ 137.86u(k − 1)u(k − 2)u(k − 5)− 142.24u(k − 2)u(k − 2)u(k − 9) \\ &+ 101.44u(k− 1)u(k − 6)u(k − 9) − 9.0283u(k − 3)u(k− 3)u(k − 12) \\ &− 168.30u(k − 2)u(k − 5)u(k − 6)+ 30.295u(k − 5)u(k − 6)u(k − 8) \\ &− 0.158u(k− 1)u(k − 2)u(k − 2) − 433.21u(k − 2)u(k− 2)u(k − 4) \\ &+ 39.88u(k − 3)u(k − 8)u(k − 11)− 162.26u(k − 1)u(k − 4)u(k − 11) \\ &− 212.08u(k− 1)u(k − 1)u(k − 5) − 438.7u(k − 3)u(k− 3)u(k − 5) \\ &+ 162.15u(k − 2)u(k − 4)u(k − 11)− 3.607u(k − 4)u(k − 4)u(k − 11) \\ &+ 13.262u(k− 6)u(k − 9)u(k − 9)+448.4u(k − 3)u(k − 4)u(k− 6) \\ &− 46.475u(k − 4)u(k − 4)u(k − 9) + 119.95u(k − 1)u(k − 1)u(k − 2) + noise \:terms. \end{align*}$$

Here, the input is the wave, and the output is the pitch. Sample rate is 2.2473 Hz

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TitleNon-linear pitch motion identification and interpretation of a tension leg platform
Publication TypeJournal Article
Year of Publication2004
AuthorsLiu, Jui-Jung, Huang Yun-Fu, and Lin Hung-Wei
JournalJournal of Marine Science and Technology
Volume12
Issue4
Start Page309
Pagination309-318
Date Published01/2004
ISSN0948-4280
AbstractThe present study is concerned with the identification of the non-linear wave force effects known as 'ringing' on an offshore structure. Ringing is a highly non-linear behaviour in which the motion resonances are outside the region of dominating wave energy. The purpose of this paper is to provide a better prediction of the higher frequency responses of wave forces on the cylinder and to interpret the non-linear effects of 'ringing' using the NARMAX method and the higher order frequency response functions.
URLhttp://jmst.ntou.edu.tw/marine/12-4/309-318.pdf

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