Abstract: In this paper, control methodology based on the selection of the type of traffic light and the period of the green phase to accomplish an optimum balance at intersections is proposed. This balance should be flexible to the static behavior of time, and randomness in a traffic situation; the goal of the proposed method is to reduce traffic volume in transportation, the average delay for each vehicle, and control over the crash of cars. The proposed method was specifically investigated at the intersection through an appropriate timing of traffic lights by sampling a multi-agent system. It consists of a large number of intersections, each of which is considered as an independent agent that exchanges information with each other, and the stability of each agent is provided separately. The robustness against uncertainties, scalability, and stability of the closed-loop overall system are the main merits of the proposed methodology. The simulation results show that the fuzzy intelligent controller in this multi-factor system which is a Takagi-Sugeno (TS) fuzzy is more useful than scheduling in the fixed-time method and it reduces the lengths of vehicles queuing.
Abstract: Bayesian approach can be used for parameter identification and extraction in state space models and its ability for analyzing sequence of data in dynamical system is proved in different literatures. In this paper, adaptive Kalman filter with Bayesian approach for identification of variances in measurement parameter noise is developed. Next, it is applied for estimation of the dynamical state and measurement data in discrete linear dynamical system. This algorithm at each step time estimates noise variance in measurement noise and state of system with Kalman filter. Next, approximation is designed at each step separately and consequently sufficient statistics of the state and noise variances are computed with a fixed-point iteration of an adaptive Kalman filter. Different simulations are applied for showing the influence of noise variance in measurement data on algorithm. Firstly, the effect of noise variance and its distribution on detection and identification performance is simulated in Kalman filter without Bayesian formulation. Then, simulation is applied to adaptive Kalman filter with the ability of noise variance tracking in measurement data. In these simulations, the influence of noise distribution of measurement data in each step is estimated, and true variance of data is obtained by algorithm and is compared in different scenarios. Afterwards, one typical modeling of nonlinear state space model with inducing noise measurement is simulated by this approach. Finally, the performance and the important limitations of this algorithm in these simulations are explained.
Abstract: Delamination is one of the major failure modes in laminated composite structures. Delamination tips are mostly captured by spatial numerical models in order to predict crack growth. This paper presents some mechanical models of delaminated composite shells based on shallow shell theories. The mechanical fields are based on a third-order displacement field in terms of the through-thickness coordinate of the laminated shell. The undelaminated and delaminated parts are captured by separate models and the continuity and boundary conditions are also formulated in a general way providing a large size boundary value problem. The system of differential equations is solved by the state space method for an elliptic delaminated shell having simply supported edges. The comparison of the proposed and a numerical model indicates that the primary indicator of the model is the deflection, the secondary is the widthwise distribution of the energy release rate. The model is promising and suitable to determine accurately the J-integral distribution along the delamination front. Based on the proposed model it is also possible to develop finite elements which are able to replace the computationally expensive spatial models of delaminated structures.
Abstract: This paper presents parameter estimation of a
single-phase rectifier using extended Kalman filter (EKF). The state
space model has been obtained using Kirchhoff’s current law (KCL)
and Kirchhoff’s voltage law (KVL). The capacitor voltage and diode
current of the circuit have been estimated using EKF. Simulation
results validate the better accuracy of the proposed method as
compared to the least mean square method (LMS). Further, EKF
has the advantage that it can be used for nonlinear systems.
Abstract: The present work proposes the development of an adaptive control system which enables the suppression of Pilot Induced Oscillations (PIO) in Digital Fly-By-Wire (DFBW) aircrafts. The proposed system consists of a Modified Model Reference Adaptive Control (M-MRAC) integrated with the Gain Scheduling technique. The PIO oscillations are detected using a Real Time Oscillation Verifier (ROVER) algorithm, which then enables the system to switch between two reference models; one in PIO condition, with low proneness to the phenomenon and another one in normal condition, with high (or medium) proneness. The reference models are defined in a closed loop condition using the Linear Quadratic Regulator (LQR) control methodology for Multiple-Input-Multiple-Output (MIMO) systems. The implemented algorithms are simulated in software implementations with state space models and commercial flight simulators as the controlled elements and with pilot dynamics models. A sequence of pitch angles is considered as the reference signal, named as Synthetic Task (Syntask), which must be tracked by the pilot models. The initial outcomes show that the proposed system can detect and suppress (or mitigate) the PIO oscillations in real time before it reaches high amplitudes.
Abstract: Single phase transmission lines are used to transfer data or energy between two users. Transient conditions such as switching operations and short circuit faults cause the generation of the fluctuation on the waveform to be transmitted. Spatial voltage distribution on the single phase transmission line may change owing to the position and duration of the short circuit fault in the system. In this paper, the state space representation of the single phase transmission line for short circuit fault and for various types of terminations is given. Since the transmission line is modeled in time domain using distributed parametric elements, the mathematical representation of the event is given in state space (time domain) differential equation form. It also makes easy to solve the problem because of the time and space dependent characteristics of the voltage variations on the distributed parametrically modeled transmission line.
Abstract: We study the problem of synthesis of lumped sources
control for the objects with distributed parameters on the basis of
continuous observation of phase state at given points of object. In the
proposed approach the phase state space (phase space) is beforehand
somehow partitioned at observable points into given subsets (zones).
The synthesizing control actions therewith are taken from the class of
piecewise constant functions. The current values of control actions
are determined by the subset of phase space that contains the
aggregate of current states of object at the observable points (in these
states control actions take constant values). In the paper such
synthesized control actions are called zone control actions. A
technique to obtain optimal values of zone control actions with the
use of smooth optimization methods is given. With this aim, the
formulas of objective functional gradient in the space of zone control
actions are obtained.
Abstract: A fluidized catalytic cracking unit (FCCU) is one of the effective units in many refineries. Modeling and optimization of FCCU were done by many researchers in past decades, but in this research, comparison between post- and oxy-combustion was studied in the regenerator-FCCU. Therefore, a simplified mathematical model was derived by doing mass/heat balances around both reactor and regenerator. A state space analysis was employed to show effects of the flow rates variables such as air, feed, spent catalyst, regenerated catalyst and flue gas on the output variables. The main aim of studying dynamic responses is to figure out the most influencing variables that affect both reactor/regenerator temperatures; also, finding the upper/lower limits of the influencing variables to ensure that temperatures of the reactors and regenerator work within normal operating conditions. Therefore, those values will be used as side constraints in the optimization technique to find appropriate operating regimes. The objective functions were modeled to be maximizing the energy in the reactor while minimizing the energy consumption in the regenerator. In conclusion, an oxy-combustion process can be used instead of a post-combustion one.
Abstract: Linear actuators are deployed in a wide range of applications. This paper presents the modeling and system identification of a variable excited linear direct drive (LDD). The LDD is designed based on linear hybrid stepper technology exhibiting the characteristic tooth structure of mover and stator. A three-phase topology provides the thrust force caused by alternating strengthening and weakening of the flux of the legs. To achieve best possible synchronous operation, the phases are commutated sinusoidal. Despite the fact that these LDDs provide high dynamics and drive forces, noise emission limits their operation in calm workspaces. To overcome this drawback an additional excitation of the magnetic circuit is introduced to LDD using additional enabling coils instead of permanent magnets. The new degree of freedom can be used to reduce force variations and related noise by varying the excitation flux that is usually generated by permanent magnets. Hence, an identified simulation model is necessary to analyze the effects of this modification. Especially the force variations must be modeled well in order to reduce them sufficiently. The model can be divided into three parts: the current dynamics, the mechanics and the force functions. These subsystems are described with differential equations or nonlinear analytic functions, respectively. Ordinary nonlinear differential equations are derived and transformed into state space representation. Experiments have been carried out on a test rig to identify the system parameters of the complete model. Static and dynamic simulation based optimizations are utilized for identification. The results are verified in time and frequency domain. Finally, the identified model provides a basis for later design of control strategies to reduce existing force variations.
Abstract: Humanity faces more and more often with different social disasters, which in turn can generate new accidents and catastrophes. To mitigate their consequences, it is important to obtain early possible signals about the events which are or can occur and to prepare the corresponding scenarios that could be applied. Our research is focused on solving two problems in this domain: identifying signals related that an accident occurred or may occur and mitigation of some consequences of disasters. To solve the first problem, methods of selecting and processing texts from global network Internet are developed. Information in Romanian is of special interest for us. In order to obtain the mentioned tools, we should follow several steps, divided into preparatory stage and processing stage. Throughout the first stage, we manually collected over 724 news articles and classified them into 10 categories of social disasters. It constitutes more than 150 thousand words. Using this information, a controlled vocabulary of more than 300 keywords was elaborated, that will help in the process of classification and identification of the texts related to the field of social disasters. To solve the second problem, the formalism of Petri net has been used. We deal with the problem of inhabitants’ evacuation in useful time. The analysis methods such as reachability or coverability tree and invariants technique to determine dynamic properties of the modeled systems will be used. To perform a case study of properties of extended evacuation system by adding time, the analysis modules of PIPE such as Generalized Stochastic Petri Nets (GSPN) Analysis, Simulation, State Space Analysis, and Invariant Analysis have been used. These modules helped us to obtain the average number of persons situated in the rooms and the other quantitative properties and characteristics related to its dynamics.
Abstract: The paper describes the use of subspace based
identification methods for auto tuning of a state space control system.
The plant is an unstable but self balancing transport robot. Because
of the unstable character of the process it has to be identified
from closed loop input-output data. Based on the identified model
a state space controller combined with an observer is calculated. The
subspace identification algorithm and the controller design procedure
is combined to a auto tuning method. The capability of the approach
was verified in a simulation experiments under different process
conditions.
Abstract: Positive real and strictly positive real transfer functions are important concepts in the control theory. In this paper, the results of researches in these areas are summarized. Definitions together with their graphical interpretations are mentioned. The equivalent conditions in the frequency domain and state space representations are reviewed. Their equivalent electrical networks are explained. Also, a comprehensive discussion about a difference between behavior of real part of positive real and strictly positive real transfer functions in high frequencies is presented. Furthermore, several illustrative examples are given.
Abstract: The Orthogonal Frequency Division Multiplexing
(OFDM) with high data rate, high spectral efficiency and its ability to
mitigate the effects of multipath makes them most suitable in wireless
application. Impulsive noise distorts the OFDM transmission and
therefore methods must be investigated to suppress this noise. In this
paper, a State Space Recursive Least Square (SSRLS) algorithm
based adaptive impulsive noise suppressor for OFDM
communication system is proposed. And a comparison with another
adaptive algorithm is conducted. The state space model-dependent
recursive parameters of proposed scheme enables to achieve steady
state mean squared error (MSE), low bit error rate (BER), and faster
convergence than that of some of existing algorithm.
Abstract: Formal verification is proposed to ensure the
correctness of the design and make functional verification more
efficient. As cache plays a vital role in the design of System on Chip
(SoC), and cache with Memory Management Unit (MMU) and cache
memory unit makes the state space too large for simulation to verify,
then a formal verification is presented for such system design. In the
paper, a formal model checking verification flow is suggested and a
new cache memory model which is called “exhaustive search model”
is proposed. Instead of using large size ram to denote the whole cache
memory, exhaustive search model employs just two cache blocks. For
cache system contains data cache (Dcache) and instruction cache
(Icache), Dcache memory model and Icache memory model are
established separately using the same mechanism. At last, the novel
model is employed to the verification of a cache which is module of a
custom-built SoC system that has been applied in practical, and the
result shows that the cache system is verified correctly using the
exhaustive search model, and it makes the verification much more
manageable and flexible.
Abstract: In this paper, an analysis of some model order
reduction techniques is presented. A new hybrid algorithm for model
order reduction of linear time invariant systems is compared with the
conventional techniques namely Balanced Truncation, Hankel Norm
reduction and Dominant Pole Algorithm (DPA). The proposed hybrid
algorithm is known as Clustering Dominant Pole Algorithm (CDPA),
is able to compute the full set of dominant poles and its cluster center
efficiently. The dominant poles of a transfer function are specific
eigenvalues of the state space matrix of the corresponding dynamical
system. The effectiveness of this novel technique is shown through
the simulation results.
Abstract: The relationship between eigenstructure (eigenvalues
and eigenvectors) and latent structure (latent roots and latent vectors)
is established. In control theory eigenstructure is associated with
the state space description of a dynamic multi-variable system and
a latent structure is associated with its matrix fraction description.
Beginning with block controller and block observer state space forms
and moving on to any general state space form, we develop the
identities that relate eigenvectors and latent vectors in either direction.
Numerical examples illustrate this result. A brief discussion of the
potential of these identities in linear control system design follows.
Additionally, we present a consequent result: a quick and easy
method to solve the polynomial eigenvalue problem for regular matrix
polynomials.
Abstract: In this paper the vibration of a synchronous belt drive
during start-up is analyzed and discussed. Besides considering the
belt elasticity, the model here proposed also takes into consideration
the electromagnetic response of the DC motor. The solution of the
motion equations is obtained by means of the modal analysis in
state space, which allows to obtain the decoupling of all equations,
without introducing the hypothesis of proportional damping. The
mathematical model of the transmission and the solution algorithms
have been implemented within a computing software that allows the
user to simulate the dynamics of the system and to evaluate the effects
due to the elasticity of the belt branches and to the electromagnetic
behavior of the DC motor. In order to show the details of the
calculation procedure, the paper presents a case study developed with
the aid of the above-mentioned software.
Abstract: In this paper an optimal convex controller is designed to control the angle of attack of a FOXTROT aircraft. Then the order of the system model is reduced to a low-dimensional state space by using Balanced Truncation Model Reduction Technique and finally the robust stability of the reduced model of the system is tested graphically by using Kharitonov rectangle and Zero Exclusion Principle for a particular range of perturbation value. The same robust stability is tested theoretically by using Frequency Sweeping Function for robust stability.
Abstract: In this paper, we present a new kind of the bilinear systems in the form of state space model. The evolution of this system depends on the product of state vector by its self. The well known Lotak Volterra and Lorenz models are special cases of this new model. We also present here a generalization of Kalman filter which is suitable to work with the new bilinear model. An application to real measurements is introduced to illustrate the efficiency of the proposed algorithm.
Abstract: Turbulence of the incoming wind field is of paramount
importance to the dynamic response of civil engineering structures. Hence reliable stochastic models of the turbulence should be available from which time series can be generated for dynamic response and
structural safety analysis. In the paper an empirical cross spectral
density function for the along-wind turbulence component over the wind field area is taken as the starting point. The spectrum is spatially
discretized in terms of a Hermitian cross-spectral density matrix for the turbulence state vector which turns out not to be positive
definite. Since the succeeding state space and ARMA modelling of
the turbulence rely on the positive definiteness of the cross-spectral
density matrix, the problem with the non-positive definiteness of such
matrices is at first addressed and suitable treatments regarding it are proposed. From the adjusted positive definite cross-spectral density
matrix a frequency response matrix is constructed which determines the turbulence vector as a linear filtration of Gaussian white noise.
Finally, an accurate state space modelling method is proposed which allows selection of an appropriate model order, and estimation of a state space model for the vector turbulence process incorporating its phase spectrum in one stage, and its results are compared with a conventional ARMA modelling method.