Controlled Van der Pol system (2)

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MIMO nonlinear system

Model description: 

$$\begin{align*} y_{1}(k+1) &=0.9y_{1}(k)-0.3y_{1}(k-1)/[1+y_{2}^{2}(k-1)]+0.7u_{1}(k) \\ &+0.1y_{1}^{2}(k-1)y_{2}^{2}(k)+0.3\sin(u_{1}(k-1))-0.7u_{2}(k)+0.6u_{2}(k-1) \\ y_{2}(k+1) &=-0.1y_{2}(k-1)+0.3y_{1}(k-1)y_{2}(k)+0.8\sin(u_{1}(k)) \\ &+0.1u_{1}(k-1)+0.9u_{2}(k)+0.2u_{2}(k-1)+0.1u_{2}^{2}(k-1) \end{align*}$$

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TitleStable adaptive neural network control of MIMO nonaffine nonlinear discrete-time systems
Publication TypeConference Paper
Year of Publication2008
AuthorsZhai, Lianfei, Chai Tianyou, Yang Chenguang, Ge S.S, and Lee Tong Heng
Conference Name47th IEEE Conference on Decision and Control, 2008.
Date Published12/2008
PublisherIEEE
Conference LocationCancun
ISBN Number978-1-4244-3123-6
Accession Number10442029
Keywordsadaptive control, closed loop systems, control system synthesis, discrete time systems, MIMO systems, neurocontrollers, nonlinear control systems, stability
AbstractIn this paper, stable adaptive neural network (NN) control, a combination of weighted one-step-ahead control and adaptive NN is developed for a class of multi-input-multi-output (MIMO) nonaffine nonlinear discrete-time systems. The weighted one-step-ahead control is designed to stabilize the nominal linear system, while the adaptive NN compensator is introduced to deal with the nonlinearities. Under the assumption that the inverse control gain matrix has an either positive definite or negative definite symmetric part, the obstacle in NN weights tuning for the MIMO systems is transformed to unknown control direction problem for single-input-single-output (SISO) system. Discrete Nussbaum gain is introduced into the NN weights adaptation law to overcome the unknown control direction problem. It is proved that all signals of the closed-loop system are bounded, while the tracking error converges to a compact set. Simulation result illustrates the effectiveness of the proposed control.
DOI10.1109/CDC.2008.4738830

MAGLEV

Model description: 

The model of the MAGLEV system is unstable and nonlinear

$$ m\ddot{x}=mg-\dfrac{K_{c}V^{2}}{x^{2}}, $$

where $x$ is the metal ball position being the system output, $V$ is the system input as the voltage. Other parameters are $m$ as the mass of the metal ball, $K_c$ as constant for magnet circuit, and $g$ is the gravitational acceleration of 9.8 m/s$^2$. A free-body diagram is shown also in the attached image.

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2

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TitleIdentification of a class of unstable processes
Publication TypeConference Paper
Year of Publication2009
AuthorsShahab, M., and Doraiswami R.
Conference Name5th IEEE GCC Conference & Exhibition, 2009.
Date Published03/2009
PublisherIEEE
Conference LocationKuwait City
ISBN Number978-1-4244-3885-3
Accession Number11875656
Keywordsconstraint theory, identification, least squares approximations, magnetic levitation, transfer functions
AbstractIdentification of a practical process, especially if unstable, is challenging as its model is generally stochastic and nonlinear. In this work we consider a class of unstable processes where the model is identified in a closed-loop operating regime. Important issues in identification are addressed, namely: identification scheme, the closed loop identification of unstable plants, choice of sampling period, and constraints on the estimated model parameters. Further the structure of the identified model may not be identical to that of the physical system due to noise artifacts, and inability to capture faster dynamics. Generally least-squares identification is employed to estimate the parameters of the system wherein all the coefficients of numerator and the denominator coefficients of system transfer function are estimated. In many practical system there are constraints on the model parameters. The identified coefficients using the conventional scheme may not obey the constraint. In this work a novel constrained least-squares identification scheme is proposed where in a priori known structural constraint is factored in parameter estimation. This scheme is evaluated on a physical magnetic lévitation system.
DOI10.1109/IEEEGCC.2009.5734284

Nonlinear Time Series

Model description: 

The following time series is modeled using RBF networks

$$y(t)=\left(0.8-0.5e^{-y^{2}(t-1)}\right)y(t-1)-\left(0.3+0.9e^{-y^{2}(t-1)}\right)y(t-2)+0.1\sin(\pi y(t-1))+\xi(t),$$

where $\xi(t)$ is a zero-mean Gaussian white noise sequence with variance 0.01.

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TitleTwo-Stage Mixed Discrete–Continuous Identification of Radial Basis Function (RBF) Neural Models for Nonlinear Systems
Publication TypeJournal Article
Year of Publication2008
AuthorsLi, Kang, Peng Jian-Xun, and Bai E.-W.
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Volume56
Start Page630
Issue3
Pagination630-643
Date Published08/2008
ISSN1549-8328
Accession Number10543358
Keywordscomputational complexity, integer programming, Nonlinear dynamical systems, radial basis function networks
AbstractThe identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.
DOI10.1109/TCSI.2008.2002545

Three Degree of Freedom Helicopter Model

Model description: 

We consider the attached image where the VARIO helicopter mounted on an experimental platform is represented. It is important to say that in this particular case the helicopter is in an OGE condition. The effects of the compressed air in take-off and landing are then neglected. The model has the form

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

where $M(q)\in\mathbb{R}^{3\times3}$ is the inertia matrix, $C(q,\dot{q})\in\mathbb{R}^{3\times3}$ is the Coriolis matrix, $G(q)\in\mathbb{R}^3$ is the vector of conservative forces, $Q(u)=\begin{bmatrix}f_z & \tau_z & \tau_\gamma \end{bmatrix}^{\mathrm T}$ is the vector of generalized forces, $q = \begin{bmatrix} z & \phi & \gamma \end{bmatrix}^{\mathrm T}$ is the vector of generalized coordinates and $u=\begin{bmatrix}h_M & h_T \end{bmatrix}^{\mathrm T}$ is the vector of control inputs. Here $f_Z, \tau_Z$ and $\tau_{\gamma}$ are the vertical forces, the yaw torque and the main rotor torque, respectively. The height $z < 0$ upwards, $\phi$ is the yaw angle and $\gamma$ is the main rotor azimuth angle.

$M(q)=\begin{bmatrix} c_0 & 0 & 0 \\ 0 & c_1 + c_2 \cos^2{(c_3\gamma)} & c_4\\ 0 & c_4 & c_5 \end{bmatrix},$

$C(q,\dot{q})=\begin{bmatrix} 0 & 0 & 0\\ 0 & c_6\sin{(2c_3\gamma)}\dot{\gamma} & c_6\sin{(2c_3\gamma)}\dot{\phi} \\ 0 & -c_6\sin{(2c_3\gamma)}\dot{\phi} & 0\end{bmatrix},$

$G(q)=\begin{bmatrix}c_7 \\ 0 \\ 0 \end{bmatrix},$

where $c_i$'s $i = 0, ..., 7$ are the physical constants given in the table below.

The generalized forces vector is given by

$Q(u)=\begin{bmatrix} c_8\dot{\gamma}^2u_1 + c_9\dot{\gamma} + c_{10} \\ c_{11}\dot{\gamma}^2u_2\\ (c_{12}\dot{\gamma}^2 + c_{13})u_1 + c_{14}\dot{\gamma}^2 + c_{15} \end{bmatrix}$

$c_i$ Numerical value
$c_0$ $7.5$ $kg$
$c_1$ $0.4305$ $kg\times m^2$
$c_2$ $3 \times 10^{-4}$ $kg\times m^2$
$c_3$ $-4.143$
$c_4$ $0.108$ $kg\times m^2$
$c_5$ $0.4993$ $kg\times m^2$
$c_6$ $-6.214 \times 10^{-4}$ $kg\times m^2$
$c_7$ $-73.58$ $N$
$c_8$ $3.411$ $kg$
$c_9$ $0.6004$ $kg \times m/s$
$c_{10}$ $3.679$ $N$
$c_{11}$ $-0.1525$ $mg \times m$
$c_{12}$ $12.01$ $kg \times m/s$
$c_{13}$ $1 \times 10^{5}$ $N$
$c_{14}$ $1.206 \times 10^{-4}$ $kg \times m^2$
$c_{15}$ $2.642$ $N$

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TitleNonlinear modelling and control of helicopters
Publication TypeJournal Article
Year of Publication2003
AuthorsVilchis, J.C. Avila, Brogliato B., Dzul A., and Lozano R.
JournalAutomatica
Volume39
Pagination1583-1596
Date Published09/2003
ISSN0005-1098
KeywordsAerodynamics, Helicopter; Drone, Nonlinear control, nonlinear systems, Underactuated
AbstractThis paper presents the development of a nonlinear model and of a nonlinear control strategy for a VARIO scale model helicopter. Our global interest is a 7-DOF (degree-of-freedom) general model to be used for the autonomous forward-flight of helicopter drones. However, in this paper we focus on the particular case of a reduced-order model (3-DOF) representing the scale model helicopter mounted on an experimental platform. Both cases represent underactuated systems ($u \in \mathbb{R}^4$ for the 7-DOF model and $u \in \mathbb{R}^2$ for the 3-DOF model studied in this paper). The proposed nonlinear model possesses quite specific features which make its study an interesting challenge, even in the 3-DOF case. In particular aerodynamical forces result in input signals and matrices which significantly differ from what is usually considered in the literature on mechanical systems control. Numerical results and experiments on a scale model helicopter illustrate the theoretical developments, and robustness with respect to parameter uncertainties is studied.
DOI10.1016/s0005-1098(03)00168-7

Controlled Van der Pol system (2)

Model description: 

Using

$z_{i+1,k+1}=y_{k+1}^{(i)}\approx y_{k}^{(i)}+Ty_{k}^{(i+1)}+\frac{T^2}{2}y_{k}^{(i+2)}+\cdots+\frac{T^{r-i}}{(r-i)!}y_{k}^{(r)}+\frac{T^{r-i+1}}{(r-i+1)!}y_{k}^{(r +1)}$

for $i=0, \cdots,r-1$ the controlled Van der Pol system from Controlled Van der Pol system (1) can be rewritten as:

$$\begin{align*} x_{1,k+1} &=x_{1,k}+Tx_{2,k}+\frac{T^2}{2}[-cx_{2,k}-d\sin x_{1,k}+u_{1,k}] \\ &+\frac{T^{2}}{3!}[-c(-cx_{2,k}-d\sin x_{1,k}+u_{1,k})-dx_{2,k}\cos x_{1,k} \\ &\times\{-x_{1,k}+\epsilon(1-x_{1,k}^2)x_{2,k}+u_k] \\ x_{2,k+1} &=x_{2,k}+T[-cx_{2,k}-d\sin x_{1,k}+u_{1,k}] \\ &+\frac{T^2}{2}[-cx_{2,k}-d\sin x_{1,k}+u_{1,k}-dx_{2,k}\cos x_{1,k} \\ &\times\{-x_{1,k}+\epsilon(1-x_{1,k}^2)x_{2,k}+u_k] \\ y_{k} &=x_{1,k} \end{align*}$$

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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

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