238 research outputs found
Experimental modeling of a web-winding machine: LPV approaches
This chapter presents the identification of a web-winding system as a linear parameter varying (LPV) system with the reel radius as the time-varying parameter. This system is nonlinear, time-varying and input–output unstable. Two identification methods are considered: in the first one, an LPV model is estimated in a single step using a novel approach based on sparse identification and set membership optimality evaluation. In the second one, several local linear time-invariant (LTI) models are identified using classical identification algorithms, and the overall LPV model is constructed as a weighted sum of the local models. The two methods are applied to experimental data measured on a real web-winding machine
Realization of multi-input/multi-output switched linear systems from Markov parameters
This paper presents a four-stage algorithm for the realization of
multi-input/multi-output (MIMO) switched linear systems (SLSs) from Markov
parameters. In the first stage, a linear time-varying (LTV) realization that is
topologically equivalent to the true SLS is derived from the Markov parameters
assuming that the submodels have a common MacMillan degree and a mild condition
on their dwell times holds. In the second stage, zero sets of LTV Hankel
matrices where the realized system has a linear time-invariant (LTI) pulse
response matching that of the original SLS are exploited to extract the
submodels, up to arbitrary similarity transformations, by a clustering
algorithm using a statistics that is invariant to similarity transformations.
Recovery is shown to be complete if the dwell times are sufficiently long and
some mild identifiability conditions are met. In the third stage, the switching
sequence is estimated by three schemes. The first scheme is based on
forward/backward corrections and works on the short segments. The second scheme
matches Markov parameter estimates to the true parameters for LTV systems and
works on the medium-to-long segments. The third scheme also matches Markov
parameters, but for LTI systems only and works on the very short segments. In
the fourth stage, the submodels estimated in Stage~2 are brought to a common
basis by applying a novel basis transformation method which is necessary before
performing output predictions to given inputs. A numerical example illustrates
the properties of the realization algorithm. A key role in this algorithm is
played by time-dependent switching sequences that partition the state-space
according to time, unlike many other works in the literature in which
partitioning is state and/or input dependent
Full Envelope Control of Nonlinear Plants with Parameter Uncertainty by Fuzzy Controller Scheduling
A full envelope controller synthesis technique is developed for multiple-input single-output (MISO) nonlinear systems with structured parameter uncertainty. The technique maximizes the controller\u27s valid region of operation, while guaranteeing pre-specified transient performance. The resulting controller does not require on-line adaptation, estimation, prediction or model identification. Fuzzy Logic (FL) is used to smoothly schedule independently designed point controllers over the operational envelope and parameter space of the system\u27s model. These point controllers are synthesized using techniques chosen by the designer, thus allowing an unprecedented amount of design freedom. By using established control theory for the point controllers, the resulting nonlinear dynamic controller is able to handle the dynamics of complex systems which can not otherwise be addressed by Fuzzy Logic Control. An analytical solution for parameters describing the membership functions allows the optimization to yield the location of point designs: both quantifying the controller\u27s coverage, and eliminating the need of extensive hand tuning of these parameters. The net result is a decrease in the number of point designs required. Geometric primitives used in the solution all have multi-dimensional interpretations (convex hull, ellipsoid, Voronoi-Delaunay diagrams) which allow for scheduling on n-dimensions, including uncertainty due to nonlinearities and parameter variation. Since many multiple-input multiple-output (MIMO) controller design techniques are accomplished by solving several MISO problems, this work bridges the gap to full envelope control of MIMO nonlinear systems with parameter variation
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
There exists an increasing demand for a flexible and computationally
efficient controller for micro aerial vehicles (MAVs) due to a high degree of
environmental perturbations. In this work, an evolving neuro-fuzzy controller,
namely Parsimonious Controller (PAC) is proposed. It features fewer network
parameters than conventional approaches due to the absence of rule premise
parameters. PAC is built upon a recently developed evolving neuro-fuzzy system
known as parsimonious learning machine (PALM) and adopts new rule growing and
pruning modules derived from the approximation of bias and variance. These rule
adaptation methods have no reliance on user-defined thresholds, thereby
increasing the PAC's autonomy for real-time deployment. PAC adapts the
consequent parameters with the sliding mode control (SMC) theory in the
single-pass fashion. The boundedness and convergence of the closed-loop control
system's tracking error and the controller's consequent parameters are
confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's
efficacy is evaluated by observing various trajectory tracking performance from
a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing
micro aerial vehicle called hexacopter. Furthermore, it is compared to three
distinctive controllers. Our PAC outperforms the linear PID controller and
feed-forward neural network (FFNN) based nonlinear adaptive controller.
Compared to its predecessor, G-controller, the tracking accuracy is comparable,
but the PAC incurs significantly fewer parameters to attain similar or better
performance than the G-controller.Comment: This paper has been accepted for publication in Information Science
Journal 201
Fractional - order system modeling and its applications
In order to control or operate any system in a closed-loop, it is important to know its behavior in the form of
mathematical models. In the last two decades, a fractional-order model has received more attention in system identification instead of classical integer-order model transfer function. Literature shows recently that some techniques on fractional calculus and fractional-order models have been presenting valuable contributions to real-world processes and achieved better results. Such new developments have impelled research into extensions of the classical identification techniques to advanced fields of science and engineering. This article surveys the recent methods in the field and other related challenges to implement the fractional-order derivatives and miss-matching with conventional science. The comprehensive discussion on available literature would help the readers to grasp the concept of fractional-order modeling and can facilitate future investigations. One can anticipate manifesting recent advances in fractional-order modeling in this paper and unlocking more opportunities for research
EMPIRICAL MODELLING AND FUZZY CONTROL SIMULATION OF A HEAT EXCHANGER
Aheat exchanger is one of the most important systems that have been installed in
many process plants. It is a device that transfers heat from liquid to another without
allowing them to mix. In order to ensure its smooth operation, modelling and
simulation can be made so that its performance can beanalyzed and improved.
At Process Control Lab, there is no simulation model for laboratory-scale heat
exchanger pilot plant. Most ofthe time, the plant is being used for ordinary laboratory
practice and the performance of this plant is not being analyzed. This project is
therefore conducted to study the plant behavior and to optimize its performance by
simulating it withnewtype of controller.
The first goal of this project is to model the heat exchanger pilot plant by using
empirical modelling method. It will yield the plant transfer function, GP that can be
used for temperature controller analysis. Besides empirical modelling, mathematical
modelling is also being carried out to study the heat exchanger behavior. By having
the model, there is an alternative way to obtain forecasted data and result without
extra cost.
The second part of this project is to analyze the model temperature controller
performance. Two controllers are being compared, namely PID and Fuzzy Logic
Controller. First, PID controller is tested to yield the best tuning parameters for
control valve. Ziegler-Nichols and fine tuning method is used to serve this purpose.
Next, the data from PI controller simulation is fed into ANFIS toolbox in MATLAB
for adaptive learning process. The FIS generated by ANFIS is based on Takagi-
Sugeno fuzzy model. The FIS which is subsequently used by the Fuzzy Logic
Controller will imitate the PI controller performance and perform based on range of
data it has been trained before by ANFIS toolbox. Finally, the comparison between
both controllers is concluded where Fuzzy Logic Controller is successfully imitating
the PI controller with slightly better performance in terms of rise time, settling time
and overshoot percentage
Robust fault detection based on adaptive threshold generation using interval LPV observers
In this paper, robust fault detection based on adaptive threshold generation of a non-linear system described
by means of a linear parameter-varying (LPV) model is addressed. Adaptive threshold is generated using
an interval LPV observer that generates a band of predicted outputs taking into account the parameter
uncertainties bounded using intervals. An algorithm that propagates the uncertainty based on zonotopes is
proposed. The design procedure of this interval LPV observer is implemented via pole placement using
linear matrix inequalities. Finally, the minimum detectable fault is characterized using fault sensitivity
analysis and residual uncertainty bounds. Two examples, one based on a quadruple-tank system and
another based on a two-degree of freedom helicopter, are used to assess the validity of the proposed fault
detection approach.Postprint (published version
Model-based and data-based frequency domain design of fixed structure robust controller: a polynomial optimization approach
L'abstract è presente nell'allegato / the abstract is in the attachmen
LPV subspace identification for robust fault detection using a set-membership approach: Application to the wind turbine benchmark
This paper focuses on robust fault detection for Linear Parameter Varying (LPV) systems using a set-membership approach. Since most of models which represent real systems are subject to modeling errors, standard fault detection (FD) LPV methods should be extended to be robust against model uncertainty. To solve this robust FD problem, a set-membership approach based on an interval predictor is used considering a bounded description of the modeling uncertainty. Satisfactory results of the proposed approach have been obtained using several fault scenarios in the pitch subsystem considered in the wind turbine benchmark introduced in IFAC SAFEPROCESS 2009.Postprint (author's final draft
- …