206 research outputs found
Identification of continuous-time models for nonlinear dynamic systems from discrete data
A new iOFR-MF (iterative orthogonal forward regression--modulating function) algorithm is proposed to identify continuous-time models from noisy data by combining the MF method and the iOFR algorithm. In the new method, a set of candidate terms, which describe different dynamic relationships among the system states or between the input and output, are first constructed. These terms are then modulated using the MF method to generate the data matrix. The iOFR algorithm is next applied to build the relationships between these modulated terms, which include detecting the model structure and estimating the associated parameters. The relationships between the original variables are finally recovered from the model of the modulated terms. Both nonlinear state-space models and a class of higher order nonlinear input–output models are considered. The new direct method is compared with the traditional finite difference method and results show that the new method performs much better than the finite difference method. The new method works well even when the measurements are severely corrupted by noise. The selection of appropriate MFs is also discussed
Identification of continuous-time models with slowly time-varying parameters
The off-line estimation of the parameters of continuous-time, linear, time-invariant transfer function models can be achieved straightforwardly using linear prefilters on the measured input and output of the system. The on-line estimation of continuous-time models with time-varying parameters is less straightforward because it requires the updating of the continuous-time prefilter parameters. This paper shows how such on-line estimation is possible by using recursive instrumental variable approaches. The proposed methods are presented in detail and also evaluated on a numerical example using both single experiment and Monte Carlo simulation analysis. In addition, the proposed recursive algorithms are tested using data from two real-life systems
"Intelligent" controllers on cheap and small programmable devices
It is shown that the "intelligent" controllers which are associated to the
recently introduced model-free control synthesis may be easily implemented on
cheap and small programmable devices. Several successful numerical experiments
are presented with a special emphasis on fault tolerant control
Stability margins and model-free control: A first look
We show that the open-loop transfer functions and the stability margins may
be defined within the recent model-free control setting. Several convincing
computer experiments are presented including one which studies the robustness
with respect to delays.Comment: 13th European Control Conference, Strasbourg : France (2014
Estimation of the parameters of continuous-time systems using data compression
This chapter provides a unified introductory account of the estimation of the parameters of continuous-time systems using data compression based on a number of previous publication
A simple and efficient feedback control strategy for wastewater denitrification
Due to severe mathematical modeling and calibration difficulties open-loop
feedforward control is mainly employed today for wastewater denitrification,
which is a key ecological issue. In order to improve the resulting poor
performances a new model-free control setting and its corresponding
"intelligent" controller are introduced. The pitfall of regulating two output
variables via a single input variable is overcome by introducing also an
open-loop knowledge-based control deduced from the plant behavior. Several
convincing computer simulations are presented and discussed.Comment: IFAC 2017 World Congress, Toulouse, Franc
A new model-free design for vehicle control and its validation through an advanced simulation platform
A new model-free setting and the corresponding "intelligent" P and PD
controllers are employed for the longitudinal and lateral motions of a vehicle.
This new approach has been developed and used in order to ensure simultaneously
a best profile tracking for the longitudinal and lateral behaviors. The
longitudinal speed and the derivative of the lateral deviation, on one hand,
the driving/braking torque and the steering angle, on the other hand, are
respectively the output and the input variables. Let us emphasize that a "good"
mathematical modeling, which is quite difficult, if not impossible to obtain,
is not needed for such a design. An important part of this publication is
focused on the presentation of simulation results with actual and virtual data.
The actual data, used in Matlab as reference trajectories, have been obtained
from a properly instrumented car (Peugeot 406). Other virtual sets of data have
been generated through the interconnected platform SiVIC/RTMaps. It is a
dedicated virtual simulation platform for prototyping and validation of
advanced driving assistance systems. Keywords- Longitudinal and lateral vehicle
control, model-free control, intelligent P controller (i-P controller),
algebraic estimation, ADAS (Advanced Driving Assistance Systems).Comment: in 14th European Control Conference, Jul 2015, Linz, Austria. 201
Inverse Problems for Matrix Exponential in System Identification: System Aliasing
This note addresses identification of the -matrix in continuous time
linear dynamical systems on state-space form. If this matrix is partially known
or known to have a sparse structure, such knowledge can be used to simplify the
identification. We begin by introducing some general conditions for solvability
of the inverse problems for matrix exponential. Next, we introduce "system
aliasing" as an issue in the identification of slow sampled systems. Such
aliasing give rise to non-unique matrix logarithms. As we show, by imposing
additional conditions on and prior knowledge about the -matrix, the issue of
system aliasing can, at least partially, be overcome. Under conditions on the
sparsity and the norm of the -matrix, it is identifiable up to a finite
equivalence class.Comment: 7 page
Non-asymptotic fractional order differentiators via an algebraic parametric method
Recently, Mboup, Join and Fliess [27], [28] introduced non-asymptotic integer
order differentiators by using an algebraic parametric estimation method [7],
[8]. In this paper, in order to obtain non-asymptotic fractional order
differentiators we apply this algebraic parametric method to truncated
expansions of fractional Taylor series based on the Jumarie's modified
Riemann-Liouville derivative [14]. Exact and simple formulae for these
differentiators are given where a sliding integration window of a noisy signal
involving Jacobi polynomials is used without complex mathematical deduction.
The efficiency and the stability with respect to corrupting noises of the
proposed fractional order differentiators are shown in numerical simulations
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