Abstract: Two approaches for model development of a smart acoustic box are suggested in this paper: the finite element (FE) approach and the subspace identification. Both approaches result in a state-space model, which can be used for obtaining the frequency responses and for the controller design. In order to validate the developed FE model and to perform the subspace identification, an experimental set-up with the acoustic box and dSPACE system was used. Experimentally obtained frequency responses show good agreement with the frequency responses obtained from the FE model and from the identified model.
Abstract: This paper aims at to develop a robust optimization methodology for the mechatronic modules of machine tools by considering all important characteristics from all structural and control domains in one single process. The relationship between these two domains is strongly coupled. In order to reduce the disturbance caused by parameters in either one, the mechanical and controller design domains need to be integrated. Therefore, the concurrent integrated design method Design For Control (DFC), will be employed in this paper. In this connect, it is not only applied to achieve minimal power consumption but also enhance structural performance and system response at same time. To investigate the method for integrated optimization, a mechatronic feed drive system of the machine tools is used as a design platform. Pro/Engineer and AnSys are first used to build the 3D model to analyze and design structure parameters such as elastic deformation, nature frequency and component size, based on their effects and sensitivities to the structure. In addition, the robust controller,based on Quantitative Feedback Theory (QFT), will be applied to determine proper control parameters for the controller. Therefore, overall physical properties of the machine tool will be obtained in the initial stage. Finally, the technology of design for control will be carried out to modify the structural and control parameters to achieve overall system performance. Hence, the corresponding productivity is expected to be greatly improved.
Abstract: Markov games can be effectively used to design
controllers for nonlinear systems. The paper presents two novel
controller design algorithms by incorporating ideas from gametheory
literature that address safety and consistency issues of the
'learned' control strategy. A more widely used approach for
controller design is the H∞ optimal control, which suffers from high
computational demand and at times, may be infeasible. We generate
an optimal control policy for the agent (controller) via a simple
Linear Program enabling the controller to learn about the unknown
environment. The controller is facing an unknown environment and
in our formulation this environment corresponds to the behavior rules
of the noise modeled as the opponent. Proposed approaches aim to
achieve 'safe-consistent' and 'safe-universally consistent' controller
behavior by hybridizing 'min-max', 'fictitious play' and 'cautious
fictitious play' approaches drawn from game theory. We empirically
evaluate the approaches on a simulated Inverted Pendulum swing-up
task and compare its performance against standard Q learning.
Abstract: In this paper, we introduce a robust state feedback controller design using Linear Matrix Inequalities (LMIs) and guaranteed cost approach for Takagi-Sugeno fuzzy systems. The purpose on this work is to establish a systematic method to design controllers for a class of uncertain linear and non linear systems. Our approach utilizes a certain type of fuzzy systems that are based on Takagi-Sugeno (T-S) fuzzy models to approximate nonlinear systems. We use a robust control methodology to design controllers. This method not only guarantees stability, but also minimizes an upper bound on a linear quadratic performance measure. A simulation example is presented to show the effectiveness of this method.
Abstract: In this work, we treat the problems related to chemical and petrochemical plants of a certain complex process taking the centrifugal compressor as an example, a system being very complex by its physical structure as well as its behaviour (surge phenomenon). We propose to study the application possibilities of the recent control approaches to the compressor behaviour, and consequently evaluate their contribution in the practical and theoretical fields. Facing the studied industrial process complexity, we choose to make recourse to fuzzy logic for analysis and treatment of its control problem owing to the fact that these techniques constitute the only framework in which the types of imperfect knowledge can jointly be treated (uncertainties, inaccuracies, etc..) offering suitable tools to characterise them. In the particular case of the centrifugal compressor, these imperfections are interpreted by modelling errors, the neglected dynamics, no modelisable dynamics and the parametric variations. The purpose of this paper is to produce a total robust nonlinear controller design method to stabilize the compression process at its optimum steady state by manipulating the gas rate flow. In order to cope with both the parameter uncertainty and the structured non linearity of the plant, the proposed method consists of a linear steady state regulation that ensures robust optimal control and of a nonlinear compensation that achieves the exact input/output linearization.
Abstract: This paper presents a robust proportionalderivative
(PD) based cerebellar model articulation
controller (CMAC) for vertical take-off and landing flight
control systems. Successful on-line training and recalling
process of CMAC accompanying the PD controller is
developed. The advantage of the proposed method is mainly
the robust tracking performance against aerodynamic
parametric variation and external wind gust. The
effectiveness of the proposed algorithm is validated through
the application of a vertical takeoff and landing aircraft
control system.
Abstract: this paper focuses on designing of PSS and SVC
controller based on chaos and PSO algorithms to improve the
stability of power system. Single machine infinite bus (SMIB) system
with SVC located at the terminal of generator has been considered to
evaluate the proposed controllers where both SVC and PSS have the
same controller. The coefficients of PSS and SVC controller have
been optimized by chaos and PSO algorithms. Finally the system
with proposed controllers has been simulated for the special
disturbance in input power of generator, and then the dynamic
responses of generator have been presented. The simulation results
showed that the system composed with recommended controller has
outstanding operation in fast damping of oscillations of power system.
Abstract: This paper presents a controller design technique for
Synchronous Reluctance Motor to improve its dynamic performance
with fast response and high accuracy. The sliding mode control is the
most attractive and suitable method to use for this purpose, since it is
simple in design and for its insensitivity to parameter variations or
external disturbances. When this method implemented it yields fast
dynamic response without overshoot and a zero steady-state error.
The current loop control with decentralized sliding mode is presented
in this paper. The mathematical model for the synchronous machine,
the inverter and the controller is developed. The stability of the
sliding mode controller is analyzed. Simulation of synchronous
reluctance motor and the controller with PWM-inverter has been
curried out, using the SIMULINK software package of MATLAB.
Simulation results are presented to show the effectiveness of the
approach.
Abstract: This paper presents a systematic approach for designing Unified Power Flow Controller (UPFC) based supplementary damping controllers for damping low frequency oscillations in a single-machine infinite-bus power system. Detailed investigations have been carried out considering the four alternatives UPFC based damping controller namely modulating index of series inverter (mB), modulating index of shunt inverter (mE), phase angle of series inverter (δB ) and phase angle of the shunt inverter (δE ). The design problem of the proposed controllers is formulated as an optimization problem and Real- Coded Genetic Algorithm (RCGA) is employed to optimize damping controller parameters. Simulation results are presented and compared with a conventional method of tuning the damping controller parameters to show the effectiveness and robustness of the proposed design approach.
Abstract: This paper emphasizes on the application of genetic algorithm (GA) to optimize the parameters of the TMD for achieving the best results in the reduction of the building response under earthquake excitations. The Integral of the Time multiplied Absolute value of the Error (ITAE) based on relative displacement of all floors in the building is taken as a performance index of the optimization criterion. The problem of robustly TMD controller design is formatted as an optimization problem based on the ITAE performance index to be solved using GA that has a story ability to find the most optimistic results. An 11–story realistic building, located in the city of Rasht, Iran is considered as a test system to demonstrate effectiveness of the proposed GA based TMD (GATMD) controller without specifying which mode should be controlled. The results of the proposed GATMD controller are compared with the uncontrolled structure through timedomain simulation and some performance indices. The results analysis reveals that the designed GA based TMD controller has an excellent capability in reduction of the seismically excited example building and the ITAE performance, that is so for remains as unknown, can be introduced a new criteria - method for structural dynamic design.
Abstract: The typical coupled-tanks process that is TITO
plant has the difficulty in controller design because changing
of system dynamics and interacting of process. This paper
presents design methodology of auto-adjustable PI controller
using MRAC technique. The proposed method can adjust the
controller parameters in response to changes in plant and
disturbance real time by referring to the reference model that
specifies properties of the desired control system.
Abstract: In this paper, we propose a robust controller design method for discrete-time systems with sector-bounded nonlinearities and time-varying delay. Based on the Lyapunov theory, delaydependent stabilization criteria are obtained in terms of linear matrix inequalities (LMIs) by constructing the new Lyapunov-Krasovskii functional and using some inequalities. A robust state feedback controller is designed by LMI framework and a reciprocally convex combination technique. The effectiveness of the proposed method is verified throughout a numerical example.
Abstract: The main goal of the study is to analyze all relevant properties of the electro hydraulic systems and based on that to make a proper choice of the neural network control strategy that may be used for the control of the mechatronic system. A combination of electronic and hydraulic systems is widely used since it combines the advantages of both. Hydraulic systems are widely spread because of their properties as accuracy, flexibility, high horsepower-to-weight ratio, fast starting, stopping and reversal with smoothness and precision, and simplicity of operations. On the other hand, the modern control of hydraulic systems is based on control of the circuit fed to the inductive solenoid that controls the position of the hydraulic valve. Since this circuit may be easily handled by PWM (Pulse Width Modulation) signal with a proper frequency, the combination of electrical and hydraulic systems became very fruitful and usable in specific areas as airplane and military industry. The study shows and discusses the experimental results obtained by the control strategy of neural network control using MATLAB and SIMULINK [1]. Finally, the special attention was paid to the possibility of neuro-controller design and its application to control of electro-hydraulic systems and to make comparative with other kinds of control.
Abstract: One of the best ways for achievement of conventional
vehicle changing to hybrid case is trustworthy simulation result and
using of driving realities. For this object, in this paper, at first sevendegree-
of-freedom dynamical model of vehicle will be shown. Then
by using of statically model of engine, gear box, clutch, differential,
electrical machine and battery, the hybrid automobile modeling will
be down and forward simulation of vehicle for pedals to wheels
power transformation will be obtained. Then by design of a fuzzy
controller and using the proper rule base, fuel economy and
regenerative braking will be marked. Finally a series of
MATLAB/SIMULINK simulation results will be proved the
effectiveness of proposed structure.
Abstract: The main goal of the study is to analyze all relevant
properties of the electro hydraulic systems and based on that to make
a proper choice of the control strategy that may be used for the
control of the servomechanism system. A combination of electronic
and hydraulic systems is widely used since it combines the
advantages of both. Hydraulic systems are widely spread because of
their properties as accuracy, flexibility, high horsepower-to-weight
ratio, fast starting, stopping and reversal with smoothness and
precision, and simplicity of operations. On the other hand, the
modern control of hydraulic systems is based on control of the circuit
fed to the inductive solenoid that controls the position of the
hydraulic valve. Since this circuit may be easily handled by PWM
(Pulse Width Modulation) signal with a proper frequency, the
combination of electrical and hydraulic systems became very fruitful
and usable in specific areas as airplane and military industry.
The study shows and discusses the experimental results obtained
by the control strategy (classical feedback (PID) & neural network)
using MATLAB and SIMULINK [1]. Finally, the special attention
was paid to the possibility of neuro-controller design and its
application to control of electro-hydraulic systems and to make
comparative with classical control.
Abstract: This paper presents the application of discrete-time
variable structure control with sliding mode based on the 'reaching
law' method for robust control of a 'simple inverted pendulum on
moving cart' - a standard nonlinear benchmark system. The
controllers designed using the above techniques are completely
insensitive to parametric uncertainty and external disturbance. The
controller design is carried out using pole placement technique to find
state feedback gain matrix , which decides the dynamic behavior
of the system during sliding mode. This is followed by feedback gain
realization using the control law which is synthesized from 'Gao-s
reaching law'. The model of a single inverted pendulum and the
discrete variable structure control controller are developed, simulated
in MATLAB-SIMULINK and results are presented. The response of
this simulation is compared with that of the discrete linear quadratic
regulator (DLQR) and the advantages of sliding mode controller over
DLQR are also presented
Abstract: In this paper, the position control of an electronic
throttle actuator is outlined. The dynamic behavior of the actuator is
described with the help of an uncertain plant model. This motivates
the controller design based on the ideas of higher-order slidingmodes.
As a consequence anti-chattering techniques can be omitted.
It is shown that the same concept is applicable to estimate unmeasureable
signals. The control law and the observer are implemented on
an electronic control unit. Results achieved by numerical simulations
and real world experiments are presented and discussed.
Abstract: We propose a control design scheme that aims to
prevent undesirable liquid outpouring and suppress sloshing during
the forward and backward tilting phases of the pouring process, for
the case of liquid containers carried by manipulators. The proposed
scheme combines a partial inverse dynamics controller with a PID
controller, tuned with the use of a “metaheuristic" search algorithm.
The “metaheuristic" search algorithm tunes the PID controller based
on simulation results of the plant-s linearization around the operating
point corresponding to the critical tilting angle, where outpouring
initiates. Liquid motion is modeled using the well-known pendulumtype
model. However, the proposed controller does not require
measurements of the liquid-s motion within the tank.
Abstract: This paper features the proposed modeling and design
of a Robust Decentralized Periodic Output Feedback (RDPOF)
control technique for the active vibration control of smart flexible
multimodel Euler-Bernoulli cantilever beams for a multivariable
(MIMO) case by retaining the first 6 vibratory modes. The beam
structure is modeled in state space form using the concept of
piezoelectric theory, the Euler-Bernoulli beam theory and the Finite
Element Method (FEM) technique by dividing the beam into 4 finite
elements and placing the piezoelectric sensor / actuator at two finite
element locations (positions 2 and 4) as collocated pairs, i.e., as
surface mounted sensor / actuator, thus giving rise to a multivariable
model of the smart structure plant with two inputs and two outputs.
Five such multivariable models are obtained by varying the
dimensions (aspect ratios) of the aluminum beam, thus giving rise to
a multimodel of the smart structure system. Using model order
reduction technique, the reduced order model of the higher order
system is obtained based on dominant eigen value retention and the
method of Davison. RDPOF controllers are designed for the above 5
multivariable-multimodel plant. The closed loop responses with the
RDPOF feedback gain and the magnitudes of the control input are
observed and the performance of the proposed multimodel smart
structure system with the controller is evaluated for vibration control.
Abstract: Markov games are a generalization of Markov
decision process to a multi-agent setting. Two-player zero-sum
Markov game framework offers an effective platform for designing
robust controllers. This paper presents two novel controller design
algorithms that use ideas from game-theory literature to produce
reliable controllers that are able to maintain performance in presence
of noise and parameter variations. A more widely used approach for
controller design is the H∞ optimal control, which suffers from high
computational demand and at times, may be infeasible. Our approach
generates an optimal control policy for the agent (controller) via a
simple Linear Program enabling the controller to learn about the
unknown environment. The controller is facing an unknown
environment, and in our formulation this environment corresponds to
the behavior rules of the noise modeled as the opponent. Proposed
controller architectures attempt to improve controller reliability by a
gradual mixing of algorithmic approaches drawn from the game
theory literature and the Minimax-Q Markov game solution
approach, in a reinforcement-learning framework. We test the
proposed algorithms on a simulated Inverted Pendulum Swing-up
task and compare its performance against standard Q learning.