International Science Index
International Journal of Electrical and Information Engineering
Inferential Reasoning for Heterogeneous Multi-Agent Mission
We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.
Multi-Agent Searching Adaptation Using Levy Flight and Inferential Reasoning
In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.
Robust Stabilization of Rotational Motion of Underwater Robots against Parameter Uncertainties
This paper provides a robust stabilization method
for rotational motion of underwater robots against parameter
uncertainties. Underwater robots are expected to be used for
various work assignments. The large variety of applications of
underwater robots motivates researchers to develop control systems
and technologies for underwater robots. Several control methods
have been proposed so far for the stabilization of nominal system
model of underwater robots with no parameter uncertainty. Parameter
uncertainties are considered to be obstacles in implementation of the
such nominal control methods for underwater robots. The objective
of this study is to establish a robust stabilization method for rotational
motion of underwater robots against parameter uncertainties. The
effectiveness of the proposed method is verified by numerical
Achieving Shear Wave Elastography by a Three-element Probe for Wearable Human-machine Interface
Shear elastic modulus of skeletal muscles can be
obtained by shear wave elastography (SWE) and has been
linearly related to muscle force. However, SWE is currently
implemented using array probes. Price and volumes of these probes
and their driving equipment prevent SWE from being used in
wearable human-machine interfaces (HMI). Moreover, beamforming
processing for array probes reduces the real-time performance. To
achieve SWE by wearable HMIs, a customized three-element probe
is adopted in this work, with one element for acoustic radiation
force generation and the others for shear wave tracking. In-phase
quadrature demodulation and 2D autocorrelation are adopted to
estimate velocities of tissues on the sound beams of the latter two
elements. Shear wave speeds are calculated by phase shift between
the tissue velocities. Three agar phantoms with different elasticities
were made by changing the weights of agar. Values of the shear
elastic modulus of the phantoms were measured as 8.98, 23.06 and
36.74 kPa at a depth of 7.5 mm respectively. This work verifies the
feasibility of measuring shear elastic modulus by wearable devices.
Model Reference Adaptive Approach for Power System Stabilizer for Damping of Power Oscillations
In recent years, electricity trade between neighboring countries has become increasingly intense. Increasing power transmission over long distances has resulted in an increase in the oscillations of the transmitted power. The damping of the oscillations can be carried out with the reconfiguration of the network or the replacement of generators, but such solution is not economically reasonable. The only cost-effective solution to improve the damping of power oscillations is to use power system stabilizers. Power system stabilizer represents a part of synchronous generator control system. It utilizes semiconductor’s excitation system connected to the rotor field excitation winding to increase the damping of the power system. The majority of the synchronous generators are equipped with the conventional power system stabilizers with fixed parameters. The control structure of the conventional power system stabilizers and the tuning procedure are based on the linear control theory. Conventional power system stabilizers are simple to realize, but they show non-sufficient damping improvement in the entire operating conditions. This is the reason that advanced control theories are used for development of better power system stabilizers. In this paper, the adaptive control theory for power system stabilizers design and synthesis is studied. The presented work is focused on the use of model reference adaptive control approach. Control signal, which assures that the controlled plant output will follow the reference model output, is generated by the adaptive algorithm. Adaptive gains are obtained as a combination of the "proportional" term and with the σ-term extended "integral" term. The σ-term is introduced to avoid divergence of the integral gains. The necessary condition for asymptotic tracking is derived by means of hyperstability theory. The benefits of the proposed model reference adaptive power system stabilizer were evaluated as objectively as possible by means of a theoretical analysis, numerical simulations and laboratory realizations. Damping of the synchronous generator oscillations in the entire operating range was investigated. Obtained results show the improved damping in the entire operating area and the increase of the power system stability. The results of the presented work will help by the development of the model reference power system stabilizer which should be able to replace the conventional stabilizers in power systems.
Design and Development of iLON Smart Server Based Remote Monitoring System for Induction Motors
Electrical energy demand in the World and particularly in India, is increasing drastically more than its production over a period of time. In order to reduce the demand-supply gap, conserving energy becomes mandatory. Induction motors are the main driving force in the industries and contributes to about half of the total plant energy consumption. By effective monitoring and control of induction motors, huge electricity can be saved. This paper deals about the design and development of such a system, which employs iLON Smart Server and motor performance monitoring nodes. These nodes will monitor the performance of induction motors on-line, on-site and in-situ in the industries. The node monitors the performance of motors by simply measuring the electrical power input and motor shaft speed; coupled to genetic algorithm to estimate motor efficiency. The nodes are connected to the iLON Server through RS485 network. The web server collects the motor performance data from nodes, displays online, logs periodically, analyzes, alerts, and generates reports. The system could be effectively used to operate the motor around its Best Operating Point (BOP) as well as to perform the Life Cycle Assessment of Induction motors used in the industries in continuous operation.
Level Shifted Carrier Signal Based Scalar Random Pulse Width Modulation Algorithms for Cascaded Multilevel Inverter Fed Induction Motor Drive
Acoustic noise becoming ever more obnoxious radiated by voltage source inverter fed induction motor drive in modern and industrial applications. The drive utilized for industrial and modern applications should use “spread spectrum” innovation known as Random pulse width modulation (PWM) algorithms where acoustic noise emanates through the machine should be critically concerned. This paper illustrates three types of random PWM control algorithms with fixed switching frequency namely 1) Random modulating PWM 2) Random carrier PWM and 3) Random modulating-carrier PWM. The spectrum plots of the motor stator current demonstrate the strength and robustness of the proposed PWM algorithms. To affirm the proposed algorithms, experimental tests have been conducted using dSPACE rt1104 control board on a v/f control three phase induction motor drive fed by DC link cascaded multilevel inverter.
Investigating the Regulation System of the Synchronous Motor Excitation Mode Serving as a Reactive Power Source
The efficient usage of the compensation abilities of the electrical drive synchronous motors used in production processes can essentially improve the technical and economic indices of the process. Reducing the flows of the reactive electrical energy due to the compensation of reactive power allows to significantly reduce the load losses of power in the electrical networks. As a result of analyzing the scientific works devoted to the issues of regulating the excitation of the synchronous motors, the need for comprehensive investigation and estimation of the excitation mode has been substantiated. By means of the obtained transmission functions, in the Simulink environment of the software package MATLAB, the transition processes of the excitation mode have been studied. As a result of obtaining and estimating the graph of the Nyquist plot and the transient process, the necessity of developing the Proportional-Integral-Derivative (PID) regulator has been justified. The transient processes of the system of the PID regulator have been investigated, and the amplitude–phase characteristics of the system have been estimated. The analysis of the obtained results has shown that the regulation indices of the developed system have been improved. The developed system can be successfully applied for regulating the excitation voltage of different-power synchronous motors, operating with a changing load, ensuring a value of the power coefficient close to 1.
Developing a Regulator for Improving the Operation Modes of the Electrical Drive Motor
The operation modes of the synchronous motors used in the production processes are greatly conditioned by the accidentally changing technological and power indices. As a result, the electrical drive synchronous motor may appear in irregular operation regimes. Although there are numerous works devoted to the development of the regulator for the synchronous motor operation modes, their application for the motors working in the irregular modes is not expedient. In this work, to estimate the issues concerning the stability of the synchronous electrical drive system, the transfer functions of the electrical drive synchronous motors operating in the synchronous and induction modes have been obtained. For that purpose, a model for investigating the frequency characteristics has been developed in the LabView environment. Frequency characteristics for assessing the transient process of the electrical drive system, operating in the synchronous and induction modes have been obtained, and based on their assessment, a regulator for improving the operation modes of the motor has been proposed. The proposed regulator can be successfully used to prevent the irregular modes of the electrical drive synchronous motor, as well as to estimate the operation state of the drive motor of the mechanism with a changing load.
Iterative Image Reconstruction for Sparse-View Computed Tomography via Total Variation Regularization and Dictionary Learning
Recently, low-dose computed tomography (CT) has become highly desirable due to increasing attention to the potential risks of excessive radiation. For low-dose CT imaging, ensuring image quality while reducing radiation dose is a major challenge. To facilitate low-dose CT imaging, we propose an improved statistical iterative reconstruction scheme based on the Penalized Weighted Least Squares (PWLS) standard combined with total variation (TV) minimization and sparse dictionary learning (DL) to improve reconstruction performance. We call this method "PWLS-TV-DL". In order to evaluate the PWLS-TV-DL method, we performed experiments on digital phantoms and physical phantoms, respectively. The experimental results show that our method is in image quality and calculation. The efficiency is superior to other methods, which confirms the potential of its low-dose CT imaging.
Fault-Tolerant Control Study and Classification: Case Study of a Hydraulic-Press Model Simulated in Real-Time
Society demands more reliable manufacturing processes
capable of producing high quality products in shorter production
cycles. New control algorithms have been studied to satisfy this
paradigm, in which Fault-Tolerant Control (FTC) plays a significant
role. It is suitable to detect, isolate and adapt a system when a harmful
or faulty situation appears. In this paper, a general overview about
FTC characteristics are exposed; highlighting the properties a system
must ensure to be considered faultless. In addition, a research to
identify which are the main FTC techniques and a classification
based on their characteristics is presented in two main groups:
Active Fault-Tolerant Controllers (AFTCs) and Passive Fault-Tolerant
Controllers (PFTCs). AFTC encompasses the techniques capable of
re-configuring the process control algorithm after the fault has been
detected, while PFTC comprehends the algorithms robust enough
to bypass the fault without further modifications. The mentioned
re-configuration requires two stages, one focused on detection,
isolation and identification of the fault source and the other one in
charge of re-designing the control algorithm by two approaches: fault
accommodation and control re-design. From the algorithms studied,
one has been selected and applied to a case study based on an
industrial hydraulic-press. The developed model has been embedded
under a real-time validation platform, which allows testing the FTC
algorithms and analyse how the system will respond when a fault
arises in similar conditions as a machine will have on factory. One
AFTC approach has been picked up as the methodology the system
will follow in the fault recovery process. In a first instance, the fault
will be detected, isolated and identified by means of a neural network.
In a second instance, the control algorithm will be re-configured to
overcome the fault and continue working without human interaction.
Map Matching Performance under Various Similarity Metrics for Heterogeneous Robot Teams
Aerial and ground robots have various advantages of usage in different missions. Aerial robots can move quickly and get a different sight of view of the area, but those vehicles cannot carry heavy payloads. On the other hand, unmanned ground vehicles (UGVs) are slow moving vehicles, since those can carry heavier payloads than unmanned aerial vehicles (UAVs). In this context, we investigate the performances of various Similarity Metrics to provide a common map for Heterogeneous Robot Team (HRT) in complex environments. Within the usage of Lidar Odometry and Octree Mapping technique, the local 3D maps of the environment are gathered. In order to obtain a common map for HRT, informative theoretic similarity metrics are exploited. All types of these similarity metrics gave adequate as allowable simulation time and accurate results that can be used in different types of applications. For the heterogeneous multi robot team, those methods can be used to match different types of maps.
A Posterior Predictive Model-Based Control Chart for Monitoring Healthcare
Quality measurement and reporting systems are used in healthcare internationally. In Australia, the Australian Council on Healthcare Standards records and reports hundreds of clinical indicators (CIs) nationally across the healthcare system. These CIs are measures of performance in the clinical setting, and are used as a screening tool to help assess whether a standard of care is being met. Existing analysis and reporting of these CIs incorporate Bayesian methods to address sampling variation; however, such assessments are retrospective in nature, reporting upon the previous six or twelve months of data. The use of Bayesian methods within statistical process control for monitoring systems is an important pursuit to support more timely decision-making. Our research has developed and assessed a new graphical monitoring tool, similar to a control chart, based on the beta-binomial posterior predictive (BBPP) distribution to facilitate the real-time assessment of health care organizational performance via CIs. The BBPP charts have been compared with the traditional Bernoulli CUSUM (BC) chart by simulation. The more traditional “central” and “highest posterior density” (HPD) interval approaches were each considered to define the limits, and the multiple charts were compared via in-control and out-of-control average run lengths (ARLs), assuming that the parameter representing the underlying CI rate (proportion of cases with an event of interest) required estimation. Preliminary results have identified that the BBPP chart with HPD-based control limits provides better out-of-control run length performance than the central interval-based and BC charts. Further, the BC chart’s performance may be improved by using Bayesian parameter estimation of the underlying CI rate.
Improvement of Ride Comfort of Turning Electric Vehicle Using Optimal Speed Control
With the spread of EVs (electric Vehicles), the ride
comfort has been gaining a lot of attention. The influence of the lateral
acceleration is important for the improvement of ride comfort of EVs
as well as the longitudinal acceleration, especially upon turning of
the vehicle. Therefore, this paper proposes a practical optimal speed
control method to greatly improve the ride comfort in the vehicle
turning situation. For consturcting this method, effective criteria that
can appropriately evaluate deterioration of ride comfort is derived.
The method can reduce the influence of both the longitudinal and
the lateral speed changes for providing a confortable ride. From
several simulation results, we can see the fact that the method can
prevent aggravation of the ride comfort by suppressing the influence
of longitudinal speed change in the turning situation. Hence, the
effectiveness of the method is recognized.
Evolving Digital Circuits for Early Stage Breast Cancer Detection Using Cartesian Genetic Programming
Cartesian Genetic Programming (CGP) is explored to
design an optimal circuit capable of early stage breast cancer
detection. CGP is used to evolve simple multiplexer circuits for
detection of malignancy in the Fine Needle Aspiration (FNA) samples
of breast. The data set used is extracted from Wisconsins Breast
Cancer Database (WBCD). A range of experiments were performed,
each with different set of network parameters. The best evolved
network detected malignancy with an accuracy of 99.14%, which is
higher than that produced with most of the contemporary non-linear
techniques that are computational expensive than the proposed
system. The evolved network comprises of simple multiplexers
and can be implemented easily in hardware without any further
complications or inaccuracy, being the digital circuit.
Multi Antenna Systems for 5G Mobile Phones
With the increasing demand of bandwidth and data rate,
there is a dire need to implement antenna systems in mobile phones
which are able to fulfill user requirements. A monopole antenna
system with multi-antennas configurations is proposed considering
the feasibility and user demand. The multi-antenna structure is
referred to as multi-input multi-output (MIMO) antenna system. The
multi-antenna system comprises of 4 antennas operating below 6
GHz frequency bands for 4G/LTE and 4 antenna for 5G applications
at 28 GHz and the dimension of board is 120 × 70 × 0.8mm3.
The suggested designs is feasible with a structure of low-profile
planar-antenna and is adaptable to smart cell phones and handheld
devices. To the best of our knowledge, this is the first design
compared to the literature by having integrated antenna system
for two standards, i.e., 4G and 5G. All MIMO antenna systems
are simulated on commercially available software, which is high
frequency structures simulator (HFSS).
Optimization Approach to Estimate Hammerstein–Wiener Nonlinear Blocks in Presence of Noise and Disturbance
Hammerstein–Wiener model is a block-oriented model
where a linear dynamic system is surrounded by two static
nonlinearities at its input and output and could be used to model
various processes. This paper contains an optimization approach
method for analysing the problem of Hammerstein–Wiener systems
identification. The method relies on reformulate the identification
problem; solve it as constraint quadratic problem and analysing its
solutions. During the formulation of the problem, effects of adding
noise to both input and output signals of nonlinear blocks and
disturbance to linear block, in the emerged equations are discussed.
Additionally, the possible parametric form of matrix operations
to reduce the equation size is presented. To analyse the possible
solutions to the mentioned system of equations, a method to reduce
the difference between the number of equations and number of
unknown variables by formulate and importing existing knowledge
about nonlinear functions is presented. Obtained equations are applied
to an instance H–W system to validate the results and illustrate the
On Fault Diagnosis of Asynchronous Sequential Machines with Parallel Composition
Fault diagnosis of composite asynchronous sequential
machines with parallel composition is addressed in this paper. An
adversarial input can infiltrate one of two submachines comprising
the composite asynchronous machine, causing an unauthorized state
transition. The objective is to characterize the condition under
which the controller can diagnose any fault occurrence. Two control
configurations, state feedback and output feedback, are considered in
this paper. In the case of output feedback, the exact estimation of
the state is impossible since the current state is inaccessible and the
output feedback is given as the form of burst. A simple example is
provided to demonstrate the proposed methodology.
Slip Suppression Sliding Mode Control with Various Chattering Functions
This study presents performance analysis results of
SMC (Sliding mode control) with changing the chattering functions
applied to slip suppression problem of electric vehicles (EVs). In
SMC, chattering phenomenon always occurs through high frequency
switching of the control inputs. It is undesirable phenomenon and
degrade the control performance, since it causes the oscillations of the
control inputs. Several studies have been conducted on this problem
by introducing some general saturation function. However, study
about whether saturation function was really best and the performance
analysis when using the other functions, weren’t being done so much.
Therefore, in this paper, several candidate functions for SMC are
selected and control performance of candidate functions is analyzed.
In the analysis, evaluation function based on the trade-off between
slip suppression performance and chattering reduction performance
is proposed. The analyses are conducted in several numerical
simulations of slip suppression problem of EVs. Then, we can
see that there is no difference of employed candidate functions
in chattering reduction performance. On the other hand, in slip
suppression performance, the saturation function is excellent overall.
So, we conclude the saturation function is most suitable for slip
suppression sliding mode control.
Numerical Simulations on Feasibility of Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization
The random dither quantization method enables us
to achieve much better performance than the simple uniform
quantization method for the design of quantized control systems.
Motivated by this fact, the stochastic model predictive control
method in which a performance index is minimized subject to
probabilistic constraints imposed on the state variables of systems
has been proposed for linear feedback control systems with random
dither quantization. In other words, a method for solving optimal
control problems subject to probabilistic state constraints for linear
discrete-time control systems with random dither quantization has
been already established. To our best knowledge, however, the
feasibility of such a kind of optimal control problems has not
yet been studied. Our objective in this paper is to investigate the
feasibility of stochastic model predictive control problems for linear
discrete-time control systems with random dither quantization. To
this end, we provide the results of numerical simulations that verify
the feasibility of stochastic model predictive control problems for
linear discrete-time control systems with random dither quantization.
Location Detection of Vehicular Accident Using Global Navigation Satellite Systems/Inertial Measurement Units Navigator
Vehicle tracking and accident recognizing are considered by many industries like insurance and vehicle rental companies. The main goal of this paper is to detect the location of a car accident by combining different methods. The methods, which are considered in this paper, are Global Navigation Satellite Systems/Inertial Measurement Units (GNSS/IMU)-based navigation and vehicle accident detection algorithms. They are expressed by a set of raw measurements, which are obtained from a designed integrator black box using GNSS and inertial sensors. Another concern of this paper is the definition of accident detection algorithm based on its jerk to identify the position of that accident. In fact, the results convinced us that, even in GNSS blockage areas, the position of the accident could be detected by GNSS/INS integration with 50% improvement compared to GNSS stand alone.
Attitude Stabilization of Satellites Using Random Dither Quantization
Recently, the effectiveness of random dither
quantization method for linear feedback control systems has
been shown in several papers. However, the random dither
quantization method has not yet been applied to nonlinear feedback
control systems. The objective of this paper is to verify the
effectiveness of random dither quantization method for nonlinear
feedback control systems. For this purpose, we consider the attitude
stabilization problem of satellites using discrete-level actuators.
Namely, this paper provides a control method based on the random
dither quantization method for stabilizing the attitude of satellites
using discrete-level actuators.
Comparison of Inter Cell Interference Coordination Approaches
This work aims to compare various techniques used in order to mitigate Inter-Cell Interference (ICI) in Long Term Evolution (LTE) and LTE-Advanced systems. For that, we will evaluate the performance of each one. In mobile communication networks, systems are limited by ICI particularly caused by deployment of small cells in conventional cell’s implementation. Therefore, various mitigation techniques, named Inter-Cell Interference Coordination techniques (ICIC), enhanced Inter-Cell Interference Coordination (eICIC) techniques and Coordinated Multi-Point transmission and reception (CoMP) are proposed. This paper presents a comparative study of these strategies. It can be concluded that CoMP techniques can ameliorate SINR and capacity system compared to ICIC and eICIC. In fact, SINR value reaches 15 dB for a distance of 0.5 km between user equipment and servant base station if we use CoMP technology whereas it cannot exceed 12 dB and 9 dB for eICIC and ICIC approaches respectively as reflected in simulations.
Object-Oriented Multivariate Proportional-Integral-Derivative Control of Hydraulic Systems
This paper presents and discusses the application of the object-oriented modelling software SIMSCAPE to hydraulic systems, with particular reference to multivariable proportional-integral-derivative (PID) control. As a result, a particular modelling approach of a double cylinder-piston coupled system is proposed and motivated, and the SIMULINK based PID tuning tool has also been used to select the proper controller parameters. The paper demonstrates the usefulness of the object-oriented approach when both physical modelling and control are tackled.
Secure Text Steganography for Microsoft Word Document
Seamless modification of an entity for the purpose of hiding a message of significance inside its substance in a manner that the embedding remains oblivious to an observer is known as steganography. Together with today's pervasive registering frameworks, steganography has developed into a science that offers an assortment of strategies for stealth correspondence over the globe that must, however, need a critical appraisal from security breach standpoint. Microsoft Word is amongst the preferably used word processing software, which comes as a part of the Microsoft Office suite. With a user-friendly graphical interface, the richness of text editing, and formatting topographies, the documents produced through this software are also most suitable for stealth communication. This research aimed not only to epitomize the fundamental concepts of steganography but also to expound on the utilization of Microsoft Word document as a carrier for furtive message exchange. The exertion is to examine contemporary message hiding schemes from security aspect so as to present the explorative discoveries and suggest enhancements which may serve a wellspring of information to encourage such futuristic research endeavors.
Number of Parametrization of Discrete-Time Systems without Unit-Delay Element: Single-Input Single-Output Case
In this paper, we consider the parametrization of the
discrete-time systems without the unit-delay element within the
framework of the factorization approach. In the parametrization,
we investigate the number of required parameters. We consider
single-input single-output systems in this paper. By the investigation,
we find, on the discrete-time systems without the unit-delay element,
three cases that are (1) there exist plants which require only one
parameter and (2) two parameters, and (3) the number of parameters
is at most three.
Sampled-Data Model Predictive Tracking Control for Mobile Robot
In this paper, a sampled-data model predictive tracking
control method is presented for mobile robots which is modeled as
constrained continuous-time linear parameter varying (LPV) systems.
The presented sampled-data predictive controller is designed by linear
matrix inequality approach. Based on the input delay approach, a
controller design condition is derived by constructing a new Lyapunov
function. Finally, a numerical example is given to demonstrate the
effectiveness of the presented method.
Control Strategies for a Robot for Interaction with Children with Autism Spectrum Disorder
Socially assistive robotic has become increasingly active and it is present in therapies of people affected for several neurobehavioral conditions, such as Autism Spectrum Disorder (ASD). In fact, robots have played a significant role for positive interaction with children with ASD, by stimulating their social and cognitive skills. This work introduces a mobile socially-assistive robot, which was built for interaction with children with ASD, using non-linear control techniques for this interaction.
Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization
Recently, feedback control systems using random dither
quantizers have been proposed for linear discrete-time systems.
However, the constraints imposed on state and control variables
have not yet been taken into account for the design of feedback
control systems with random dither quantization. Model predictive
control is a kind of optimal feedback control in which control
performance over a finite future is optimized with a performance
index that has a moving initial and terminal time. An important
advantage of model predictive control is its ability to handle
constraints imposed on state and control variables. Based on the
model predictive control approach, the objective of this paper is to
present a control method that satisfies probabilistic state constraints
for linear discrete-time feedback control systems with random dither
quantization. In other words, this paper provides a method for
solving the optimal control problems subject to probabilistic state
constraints for linear discrete-time feedback control systems with
random dither quantization.
Neuron-Based Control Mechanisms for a Robotic Arm and Hand
A robotic arm and hand controlled by simulated
neurons is presented. The robot makes use of a biological neuron
simulator using a point neural model. The neurons and synapses are
organised to create a finite state automaton including neural inputs
from sensors, and outputs to effectors. The robot performs a simple
pick-and-place task. This work is a proof of concept study for a
longer term approach. It is hoped that further work will lead to
more effective and flexible robots. As another benefit, it is hoped that
further work will also lead to a better understanding of human and
other animal neural processing, particularly for physical motion. This
is a multidisciplinary approach combining cognitive neuroscience,
robotics, and psychology.