Single link manipulator with flexible joints

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.

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

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

Type: 

Form: 

Time domain: 

Linearity: 

Publication details: 

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.

Type: 

Form: 

Time domain: 

Linearity: 

Publication details: 

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

Power plant superheater

Model description: 

In the operation of a power plant superheater, exacting demands are made on the steam temperature maintenance at the outlet. For temperature control at the outlet of a superheater, the relevant system state is the temperature pattern along the superheater tube. This is described by a distributed-parameter system, which involves an infinite number of state variables. To derive a simplified model for control purposes, the superheater is divided into segments, and a lumped model is derived, which represents a finite number of intermediate temperatures.

Assuming that the pressure inside the tube is constant, the enthalpy of the steam satisfies the relation $dH = C_pdT(kcal/kg)$ , where $C_p(kcal/kg^{\circ}C)$ is the constant-pressure specific heat. Hence, we conclude that the heat supplied to the following fluid(steam) only increases its enthalpy, $dH = dQ$ , where $Q$ denotes the heat. In the above equations, it is assumed that convection is the exclusive heat transfer mode for the superheater. Hence the heat transfer from to metal $Q_{ms}(kcal/s)$ and from gas to metal $Q_{gm}(kcal/s)$ are expressed in terms of the heat transfer rates from gas to metal $\alpha_{gm}(kcal/m^2s^{\circ}C)$ and from metal to steam $\alpha_{ms}(kcal/m^2s^{\circ}C)$ and heating surface $S(m^2)$ :

$$\begin{align*} \alpha_{ms}S_1(T(l,t)-T(l,t)) &=Q_{ms} \\ \alpha_{gm}S_2(T_m(l,t)-T(l,t)) &=Q_{gm}. \end{align*}$$

It is also assumed that the heat transfer rates $\alpha_{gm}$ and $\alpha_{ms}$ are constants.

Now, to simulate the profile of superheated steam precisely, it is necessary to divide the superheater into $n$ segments as shown in the attached image.

In the first segment, the desuperheater is included and system is modified as follows:

$$\begin{align*} V_s\rho C_p\frac{{\mathrm d} x_1}{{\mathrm d}t} &={C_{p}}{T_{i}}{w_{i}}-{C_{p}}({w_{i}}+{w_{d}}){x_{1}} +{\alpha_{ms}}{S_{1}}({z_{1}}-{x_{1}})+{C_{pd}}{T_{d}}{w_{d}}\\ M_mC_m \frac{{\mathrm d}z_1}{{\mathrm d}t} &={\alpha_{gm}}{S_{2}}(T{g_{1}}-{z_{1}})-{\alpha_{ms}}{S_{1}}({z_{1}}-{x_{1}}), \end{align*}$$

where $x=[x_1,x_2,\ldots,x_n]^{\mathrm T}=[T_1,T_2,\ldots,T_n]^{\mathrm T}$, $z=[z_1,z_2,\ldots,z_n]^{\mathrm T}=[T_{m1},T_{m2},\ldots,t_{mn}]^{\mathrm T}$, and $T_{mi}(^{\circ}C)$ are metal temperature, $T_i(^{\circ}C)$ are steam temperature, $i=1,\ldots,n$.

Type: 

Form: 

Time domain: 

Linearity: 

Attachment: 

Publication details: 

TitleController design for the bilinear system
Publication TypeConference Paper
Year of Publication2001
AuthorsLee, Sang-Hyuk, Jeon Byeong-Seok, Song Chang-Kyu, Kim Ju-Sik, Kim Sung-Soo, and Jang Young-Soo
Conference NameIEEE International Symposium on Industrial Electronics, 2001
Date Published06/2001
PublisherIEEE
Conference LocationPusan
ISBN Number0-7803-7090-2
Accession Number7091972
Keywordsbilinear systems, control system synthesis, iterative methods, linear quadratic control, state estimation, state feedback, temperature control, thermal power stations
AbstractIn this paper, we construct the controller for the bilinear system using an iterative method. For applying the linear quadratic control theory, we formulate the bilinear system to execute iteration. We estimate bilinear system state for the purpose of state feedback controller design. We also apply the iterative controller to the thermal power plant superheater system temperature control, and computer simulation to show that the output steam temperature is properly maintained
DOI10.1109/ISIE.2001.932003

A linear system

Model description: 

Consider a linear system represented by the transfer function

$$G(s)=\dfrac{c}{s(s+a)}$$

where $a$ and $c>0$ are unknowns constants, and the reference model

$$G_m(s)=\dfrac{\omega^2}{s^2 + 2\zeta\omega s + \omega^2}.$$

Type: 

Form: 

Model order: 

2

Time domain: 

Linearity: 

Attachment: 

Publication details: 

TitleAdaptive output feedback control of nonlinear systems represented by input-output models
Publication TypeJournal Article
Year of Publication1996
AuthorsKhalil, H.K.
JournalIEEE Transactions on Automatic Control
Volume41
Start Page177
Issue2
Pagination177-188
Date Published02/1996
ISSN0018-9286
Accession Number5202146
Keywordsadaptive control, linearisation techniques, nonlinear control systems, state feedback
AbstractWe consider a single-input-single-output nonlinear system which can be represented globally by an input-output model. The system is input-output linearizable by feedback and is required to satisfy a minimum phase condition. The nonlinearities are not required to satisfy any global growth condition. The model depends linearly on unknown parameters which belong to a known compact convex set. We design a semiglobal adaptive output feedback controller which ensures that the output of the system tracks any given reference signal which is bounded and has bounded derivatives up to the nth order, where n is the order of the system. The reference signal and its derivatives are assumed to belong to a known compact set. It is also assumed to be sufficiently rich to satisfy a persistence of excitation condition. The design process is simple. First we assume that the output and its derivatives are available for feedback and design the adaptive controller as a state feedback controller in appropriate coordinates. Then we saturate the controller outside a domain of interest and use a high-gain observer to estimate the derivatives of the output. We prove, via asymptotic analysis, that when the speed of the high-gain observer is sufficiently high, the adaptive output feedback controller recovers the performance achieved under the state feedback one
DOI10.1109/9.481517

Single link manipulator with flexible joints

Model description: 

A single link manipulator with flexible joints and negligible damping can be represented by

$$\begin{align*} I\ddot{q}_1 + MgL\sin{q_1} + k(q_1 - q_2) &= 0 \\ J\ddot{q}_2-k(q_1-q_2) &=u, \end{align*}$$

where $q_1$ and $q_2$ are the angular positions, and $u$ is a torque input. The physical parameters $g, I, J, k, L,$ and $M$ are all positive. Taking $y=q_1$ as the output, it can be verified that $y$ satisfies the fourth-order differential equation

$$y^{(4)}=\dfrac{gLM}{I}(\dot{y}^2\sin{y}-\ddot{y}\cos{y})- \left(\dfrac{k}{I}+\dfrac{k}{J}\right)\ddot{y}-\dfrac{gkLM}{IJ}\sin{y}+\dfrac{k}{IJ}u.$$

Type: 

Form: 

Model order: 

4

Time domain: 

Linearity: 

Publication details: 

TitleAdaptive output feedback control of nonlinear systems represented by input-output models
Publication TypeJournal Article
Year of Publication1996
AuthorsKhalil, H.K.
JournalIEEE Transactions on Automatic Control
Volume41
Start Page177
Issue2
Pagination177-188
Date Published02/1996
ISSN0018-9286
Accession Number5202146
Keywordsadaptive control, linearisation techniques, nonlinear control systems, state feedback
AbstractWe consider a single-input-single-output nonlinear system which can be represented globally by an input-output model. The system is input-output linearizable by feedback and is required to satisfy a minimum phase condition. The nonlinearities are not required to satisfy any global growth condition. The model depends linearly on unknown parameters which belong to a known compact convex set. We design a semiglobal adaptive output feedback controller which ensures that the output of the system tracks any given reference signal which is bounded and has bounded derivatives up to the nth order, where n is the order of the system. The reference signal and its derivatives are assumed to belong to a known compact set. It is also assumed to be sufficiently rich to satisfy a persistence of excitation condition. The design process is simple. First we assume that the output and its derivatives are available for feedback and design the adaptive controller as a state feedback controller in appropriate coordinates. Then we saturate the controller outside a domain of interest and use a high-gain observer to estimate the derivatives of the output. We prove, via asymptotic analysis, that when the speed of the high-gain observer is sufficiently high, the adaptive output feedback controller recovers the performance achieved under the state feedback one
DOI10.1109/9.481517

Pages