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 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 means of two approaches: fault accommodation and control re-design. The most significant control algorithms have been applied to a case study based on a hydraulic-press. The developed model has been embedded under a real-time Hardware in the Loop validation platform, which allows testing the FTC algorithms and analyze how the system will respond when a fault arises in similar conditions as a machine will have on factory. AFTCs have 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.
Setting Uncertainty Conditions Using Singular Values for Repetitive Control in State Feedback
A repetitive controller designed to accommodate periodic disturbances via state feedback is discussed. Periodic disturbances can be represented by a time delay model in a positive feedback loop acting on system output. A direct use of the small gain theorem solves the periodic disturbances problem via 1) isolating the delay model, 2) finding the overall system representation around the delay model and 3) designing a feedback controller that assures overall system stability and tracking error convergence. This paper addresses uncertainty conditions for the repetitive controller designed in state feedback in either past error feedforward or current error feedback using singular values. The uncertainty investigation is based on the overall system found and the stability condition associated with it; depending on the scheme used, to set an upper/lower limit weighting parameter. This creates a region that should not be exceeded in selecting the weighting parameter which in turns assures performance improvement against system uncertainty. Repetitive control problem can be described in lifted form. This allows the usage of singular values principle in setting the range for the weighting parameter selection. The Simulation results obtained show a tracking error convergence against dynamic system perturbation if the weighting parameter chosen is within the range obtained. Simulation results also show the advantage of weighting parameter usage compared to the case where it is omitted.
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.
Analyzing the Practicality of Drawing Inferences in Automation of Commonsense Reasoning
Commonsense reasoning is the simulation of human ability to make decisions during the situations that we encounter every day. It has been several decades since the introduction of this subfield of artificial intelligence, but it has barely made some significant progress. The modern computing aids also have remained impotent in this regard due to the absence of a strong methodology towards commonsense reasoning development. Among several accountable reasons for the lack of progress, drawing inference out of commonsense knowledge-base stands out. This review paper emphasizes on a detailed analysis of representation of reasoning uncertainties and feasible prospects of programming aids for drawing inferences. Also, the difficulties in deducing and systematizing commonsense reasoning and the substantial progress made in reasoning that influences the study have been discussed. Additionally, the paper discusses the possible impacts of an effective inference technique in commonsense reasoning.
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.
A Relative Entropy Regularization Approach for Fuzzy C-Means Clustering Problem
Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.
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.
Using Structured Analysis and Design Technique Method for Unmanned Aerial Vehicle Components
Introduction: Scientific developments and techniques for the systemic approach generate several names to the systemic approach: systems analysis, systems analysis, structural analysis. The main purpose of these reflections is to find a multi-disciplinary approach which organizes knowledge, creates universal language design and controls complex sets. In fact, system analysis is structured sequentially by steps: the observation of the system by various observers in various aspects, the analysis of interactions and regulatory chains, the modeling that takes into account the evolution of the system, the simulation and the real tests in order to obtain the consensus. Thus the system approach allows two types of analysis according to the structure and the function of the system. The purpose of this paper is to present an application of system analysis of Unmanned Aerial Vehicle (UAV) components in order to represent the architecture of this system. Method: There are various analysis methods which are proposed, in the literature, in to carry out actions of global analysis and different points of view as SADT method (Structured Analysis and Design Technique), Petri Network. The methodology adopted in order to contribute to the system analysis of an Unmanned Aerial Vehicle has been proposed in this paper and it is based on the use of SADT. In fact, we present a functional analysis based on the SADT method of UAV components Body, power supply and platform, computing, sensors, actuators, software, loop principles, flight controls and communications). Results: In this part, we present the application of SADT method for the functional analysis of the UAV components. This SADT model will be composed exclusively of actigrams. It starts with the main function ‘To analysis of the UAV components’. Then, this function is broken into sub-functions and this process is developed until the last decomposition level has been reached (levels A1, A2, A3 and A4). Recall that SADT techniques are semi-formal; however, for the same subject, different correct models can be built without having to know with certitude which model is the good or, at least, the best. In fact, this kind of model allows users a sufficient freedom in its construction and so the subjective factor introduces a supplementary dimension for its validation. That is why the validation step on the whole necessitates the confrontation of different points of views. Conclusion: In this paper, we presented an application of system analysis of Unmanned Aerial Vehicle components. In fact, this application of system analysis is based on SADT method (Structured Analysis Design Technique). This functional analysis proved the useful use of SADT method and its ability of describing complex dynamic systems.
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.
Virtual Conciliation in Colombia: Evaluation of Maturity Level within the Framework of E-Government
The Colombian government has defined an e-government strategy to take advantage of Information Technologies (IT) in order to contribute to the building of a more efficient, transparent and participative State that provides better services to citizens and businesses. In this regard, the Justice sector is one of the government sectors where IT has generated more expectation considering that the country has a judicial processes backlog. This situation has led to the search for alternative forms of access to justice that speed up the process while providing a low cost for citizens. To this end, the Colombian government has authorized the use of Alternative Dispute Resolution methods (ADR), a remedy where disputes can be resolved more quickly compared to judicial processes while facilitating greater communication between the parties, without recourse to judicial authority. One of these methods is conciliation, which includes a special modality that takes advantage of IT for the development of itself known as virtual conciliation. With this option the conciliation is supported by information systems, applications or platforms and communications are provided through it. This paper evaluates the level of maturity in how the service of virtual conciliation is under the framework of this strategy. This evaluation is carried out taking into account Shahkooh's 5-phase model for e-government. As a result, it is evident that in the context of conciliation, maturity does not reach the necessary level in the model so that it can be considered as virtual conciliation. Therefore, it is necessary to define strategies to maximize the potential of IT in this context.
All Solution-Processed Organic Light Emitting Diode with Low Melting Point Alloy Encapsulation
Organic Light Emitting Diodes (OLEDs) are being developed rapidly as next-generation displays due to their self-luminous and flexible characteristics. OLEDs are highly susceptible to moisture and oxygen due to their structural properties. Thus, requiring a high level of encapsulation technology. Recently, encapsulation technology such as Thin Film Encapsulation (TFE) has been developed for OLED, but it is not perfect to prevent moisture permeation on the side. In this study, we propose OLED encapsulation method using Low melting Point Alloy (LMPA). The LMPA line was designed in square box shape on the outer edge of the device and was formed by screen printing method. To determine if LMPA has an effect on OLED, we fabricated solution processed OLEDs with a square-shaped LMPA line and evaluate the I-V-L characteristics of the OLEDs. Also, the resistance characteristic of the LMPA line was observed by repeatedly bending the LMPA line. It is expected that LMPA encapsulation will have a great advantage in shortening the process time and cost reduction.
Process for Analyzing Information Security Risks Associated with the Incorporation of Online Dispute Resolution Systems in the Context of Conciliation in Colombia
The innumerable possibilities offered by the use of Information Technology (IT) in the development of different socio-economic activities has made a change in the social paradigm and the emergence of the so-called information and knowledge society. The Colombian government, aware of this reality, has been promoting the use of IT as part of the E-government strategy adopted in the country. However, it is well known that the use of IT implies the existence of certain threats that put the security of information in the digital environment at risk. One of the priorities of the Colombian government is to improve access to alternative justice through IT, in particular, access to Alternative Dispute Resolution (ADR): conciliation, arbitration and friendly composition; by means of which it is sought that the citizens directly resolve their differences. To this end, a trend has been identified in the use of Online Dispute Resolution (ODR) systems, which extend the benefits of ADR to the digital environment through the use of IT. This article presents a process for the analysis of information security risks associated with the incorporation of ODR systems in the context of conciliation in Colombia, based on four fundamental stages identified in the literature: (I) Identification of assets, (II) Identification of threats and vulnerabilities (III) Estimation of the impact and 4) Estimation of risk levels. The methodological design adopted for this research was the grounded theory, since it involves interactions that are applied to a specific context and from the perspective of diverse participants. As a result of this investigation, the activities to be followed are defined to carry out an analysis of information security risks, in the context of the conciliation in Colombia supported by ODR systems, thus contributing to the estimation of the risks to make possible its subsequent treatment.
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).
Estimating Occupancy in Residential Context Using Bayesian Networks for Energy Management
A general approach is proposed to determine occupant behavior (occupancy and activity) in residential buildings and to use these estimates for improved energy management. Occupant behaviour is modelled with a Bayesian Network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO₂ concentration. Two case studies are presented which show the real world applicability of estimating occupant behaviour in this way. Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort standards. The efficiency gains are strongly correlated with occupant behaviour and accuracy of the occupancy estimates.
A Combined High Gain-Higher Order Sliding Mode Controller for a Class of Uncertain Nonlinear Systems
The use of standard sliding mode controller, usually, leads to the appearing of an undesirable chattering phenomenon affecting the control signal. Such problem can be overcome using a higher-order sliding mode controller (HOSMC) which preserves the main properties of the standard sliding mode and deliberately increases the control smoothness. In this paper, we propose a new HOSMC for a class of uncertain multi-input multi-output nonlinear systems. Based on high gain and integral sliding mode paradigms, the established control scheme removes theoretically the chattering phenomenon and provides the stability of the control system. Numerical simulations are developed to show the effectiveness of the proposed controller when applied to solve a control problem of two water levels into a quadruple-tank process.
Multivariate Statistical Process Monitoring of Base Metal Flotation Plant Using Dissimilarity Scale-Based Singular Spectrum Analysis
A multivariate statistical process monitoring methodology using dissimilarity scale-based singular spectrum analysis (SSA) is proposed for the detection and diagnosis of process faults in the base metal flotation plant. Process faults are detected based on the multi-level decomposition of process signals by SSA using the dissimilarity structure of the process data and the subsequent monitoring of the multiscale signals using the unified monitoring index which combines T² with SPE. Contribution plots are used to identify the root causes of the process faults. The overall results indicated that the proposed technique outperformed the conventional multivariate techniques in the detection and diagnosis of the process faults in the flotation plant.
Genetic Algorithm and Multi-Parametric Programming Based Cascade Control System for Unmanned Aerial Vehicles
This paper considers the problem of cascade control system for unmanned aerial vehicles (UAVs). Due to the complicated modelling technique of UAV, it is necessary to separate them into two subsystems. The proposed cascade control structure is a hierarchical scheme including a robust control for inner subsystem based on H infinity theory and trajectory generator using genetic algorithm (GA), outer loop control law based on multi-parametric programming (MPP) technique to overcome the disadvantage of a big amount of calculations. Simulation results are presented to show that the equivalent path has been found and obtained by proposed cascade control scheme.
Maximum Initial Input Allowed to Iterative Learning Control Set-up Using Singular Values
Iterative Learning Control (ILC) known to be a controlling tool to overcome periodic disturbances for repetitive systems. This technique is required to let the error signal tends to zero as the number of operation increases. The learning process that lies within this context is strongly dependent on the initial input which if selected properly tends to let the learning process be more effective compared to the case where a system starts from blind. ILC uses previous recorded execution data to update the following execution/trial input such that a reference trajectory is followed to a high accuracy. Error convergence in ILC is generally highly dependent on the input applied to a plant for trial $1$, thus a good choice of initial starting input signal would make learning faster and as a consequence the error tends to zero faster as well. In the work presented within, an upper limit based on the Singular Values Principle (SV) is derived for the initial input signal applied at trial $1$ such that the system follow the reference in less number of trials without responding aggressively or exceeding the working envelope where a system is required to move within in a robot arm, for example. Simulation results presented illustrate the theory introduced within this paper.
Robust Model Predictive Controller for Uncertain Nonlinear Wheeled Inverted Pendulum Systems: A Tube-Based Approach
This work presents the problem of tube-based robust model predictive controller for a class of continuous-time systems in the presence of input disturbances. The main objective is to point out the state trajectory of closed system being maintained inside a sequence of tubes. An estimation of attraction region of the closed system is pointed out based on input state stability (ISS) theory and linearized model in each time interval. The theoretical analysis and simulation results demonstrate the performance of the proposed algorithm for a wheeled inverted pendulum system.
Adaptive Optimal Controller for Uncertain Inverted Pendulum System: A Dynamic Programming Approach for Continuous Time System
In this paper, we investigate the adaptive optimal control law for continuous-time systems with input disturbances and unknown parameters. This paper extends previous works to obtain the robust control law of uncertain systems. Through theoretical analysis, an adaptive dynamic programming (ADP) based optimal control is proposed to stabilize the closed-loop system and ensure the convergence properties of proposed iterative algorithm. Moreover, the global asymptotic stability (GAS) for closed system is also analyzed. The theoretical analysis for continuous-time systems and simulation results demonstrate the performance of the proposed algorithm for an inverted pendulum system.
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
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.
Application of Artificial Neural Networks to Adaptive Speed Control under ARDUINO
Nowadays, adaptive control schemes are being used when model based control schemes are applied in presence of uncertainty and model mismatches. Artificial neural networks have been employed both in modelling and control of non-linear dynamic systems with unknown dynamics. In fact, these are powerful tools to solve this control problem when only input-output operational data are available. A neural network controller under SIMULINK together with the ARDUINO hardware platform has been used to perform real-time speed control of a computer case fan. Comparison of performance with a PID controller has also been presented in order to show the efficacy of neural control under different command signals tracking and also when disturbance signals are present in the speed control loops.
Stability Analysis of Hossack Suspension Systems in High Performance Motorcycles
A motorcycle's front end links the front wheel to the motorcycle's chassis and has two main functions: the front wheel suspension and the vehicle steering. Up to this date, several suspension systems have been developed in order to achieve the best possible front end behavior, being the telescopic fork the most common one and already subjected to several years of study in terms of its kinematics, dynamics, stability and control. A motorcycle telescopic fork suspension model consists of a couple of outer tubes which contain the suspension components (coil springs and dampers) internally and two inner tubes which slide into the outer ones allowing the suspension travel. The outer tubes are attached to the frame through two triple trees which connect the front end to the main frame through the steering bearings and allow the front wheel to turn about the steering axis. This system keeps the front wheel's displacement in a straight line parallel to the steering axis. However, there exist alternative suspension designs that allow different trajectories of the front wheel with the suspension travel. In this contribution, the authors investigate an alternative front suspension system (Hossack suspension) and its influence on the motorcycle nonlinear dynamics to identify and reduce stability risks that a new suspension systems may introduce in the motorcycle dynamics. Based on an existing high-fidelity motorcycle mathematical model, the front end geometry is modified to accommodate a Hossack suspension system. It is characterized by a double wishbone design that varies the front end geometry on certain maneuverings and, consequently, the machine's behavior/response. It consists of a double wishbone structure directly attached to the chassis. In here, the kinematics of this system and its impact on the motorcycle performance/stability are analyzed and compared to the well known telescopic fork suspension system. The framework of this research is the mathematical modelling and numerical simulation. Full stability analyses are performed in order to understand how the motorcycle dynamics may be affected by the newly introduced front end design. This study is carried out by a combination of nonlinear dynamical simulation and root-loci methods. A modal analysis is performed in order to get a deeper understanding of the different modes of oscillation and how the Hossack suspension system affects them. The results show that different kinematic designs of a double wishbone suspension systems do not modify the general motorcycle's stability. The normal modes properties remain unaffected by the new geometrical configurations. However, these normal modes differ from one suspension system to the other. It is seen that the normal modes behaviour depends on various important dynamic parameters, such as the front frame flexibility, the steering damping coefficient and the centre of mass location.
Design and Implementation of the Embedded Control System for the Electrical Motor Based Cargo Vehicle
With an increased demand in the land cargo industry, it is predicted that the freight trade will rise to a record $1.1 trillion in revenue and volume in the following years to come. This increase is mainly driven by the e-commerce model ever so popular in the consumer market. Many innovative ideas have stemmed from this demand and change in lifestyle likes of which include e-bike cargo and drones. Rural and urban areas are facing air quality challenges to keep pollution levels in city centre to a minimum. For this purpose, this paper presents the design and implementation of a non-linear PID control system, employing a micro-controller and low cost sensing technique, for controlling an electrical motor based cargo vehicle with various loads, to follow a leading vehicle (bike). Within using this system, the cargo vehicle will have no load influence on the bike rider on different gradient conditions, such as hill climbing. The system is being integrated with a microcontroller to continuously measure several parameters such as relative displacement between bike and the cargo vehicle and gradient of the road, and process these measurements to create a portable controller capable of controlling the performance of electrical vehicle without the need of a PC. As a result, in the case of carrying 180kg of parcel weight, the cargo vehicle can maintain a reasonable spacing over a short length of sensor travel between the bike and itself.
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.
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.
Using Optimal Control Method to Investigate the Stability and Transparency of a Nonlinear Teleoperation System with Time Varying Delay
In this paper, a new structure for teleoperation systems with time varying delay has been modeled and proposed. A random time varying the delay of up to 150 msec is simulated in teleoperation channel of both masters to slave and vice versa. The system stability and transparency have been investigated, comparing the result of a PID controller and an optimal controller on each master and slave sub-systems separately. The controllers have been designed in slave subsystem for reducing position errors between master and slave, and another controller has been designed in the master subsystem to establish stability, transparency and force tracking. Results have been compared together. The results showed PID controller is appropriate in position tracking, but force response oscillates in contact with the environment. We showed the optimal control established position tracking properly. Also, force tracking is achieved in this controller appropriately.
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.