Adaptive control systems

AbstractÐAn increasing number of real-time applications, related to multimedia and adaptive control systems, require greater flexibility than classical real-time theory usually permits. In this paper, we present a novel scheduling framework in which tasks are treated as springs with given elastic coefficients to better conform to the actual load conditions. Under this model, periodic tasks can intentionally change their execution rate to provide different quality of service and the other tasks can automatically adapt their periods to keep the system underloaded. The proposed model can also be used to handle overload conditions in a more flexible way and to provide a simple and efficient mechanism for controlling a system's performance as a function of the current load. Index TermsÐReal-time scheduling, overload management, rate adaptation. ae .

The exploration of unknown and dynamic space environments necessitates a paradigm shift in autonomous spacecraft navigation. Traditional methods are often insufficient, highlighting the critical need to emulate the remarkable adaptability, resilience, and energy efficiency observed in biological intelligence. This paper posits that integrating biologically inspired artificial intelligence (AI), such as neuromorphic computing, spiking neural networks (SNNs), and swarm intelligence, with the unparalleled precision of quantum sensor technologies offers a transformative solution for next-generation space autonomy. This article delves into the theoretical foundations, proposed architectures, and potential applications of such bio-inspired quantum AI systems. It examines the limitations of classical navigation, the unique advantages of bio-inspired AI and quantum sensors, and outlines how their synergistic integration can enable unprecedented navigational capabilities in challenging extraterrestrial settings. Furthermore, it addresses the critical challenges in simulation, real-time learning, power constraints, and the complex interfacing of disparate computational paradigms. The ultimate vision is to pave the way for truly self-learning, adaptive, and robust space probes capable of navigating and exploring the cosmos with minimal human intervention, fundamentally expanding humanity's reach and understanding of the universe.

This paper considers dynamic state estimation using blurry measurements from image sensors such as CCD(charge coupled device) or CMOS(complementary metal oxide semiconductor) arrays. Typically, the information obtained from these sensors is the time-averaged output measurement during the exposure time. The additional information available in the intensity distribution, termed blur, is disregarded as noise. This manuscript models the image sensor as an integrative intensity sensor and exploits its unique properties to extract additional (non-linear) output information through spatial moments of the intensity distribution. An extended Kalman filter is then designed to exploit this information for better state reconstruction. We illustrate this modeling and algorithm development in the context of state estimation for adaptive optics systems. Simulation results verify that using the spatial moments can lead to more fidelous state estimation.

Contents Conventions and Notation xv Preface xxi Chapter 1. Introduction Chapter 2. Dynamical Systems and Differential Equations 2.1 Introduction 2.2 Vector and Matrix Norms 2.3 Set Theory and Topology 2.4 Analysis in R n 2.5 Vector Spaces and Banach Spaces 2.6 Dynamical Systems, Flows, and Vector Fields 2.7 Nonlinear Differential Equations 2.8 Extendability of Solutions 2.9 Global Existence and Uniqueness of Solutions 2.10 Flows and Dynamical Systems 2.11 Time-Varying Nonlinear Dynamical Systems 2.12 Limit Points, Limit Sets, and Attractors 2.13 Periodic Orbits, Limit Cycles, and Poincare-Bendixson Theorems 2.14 Problems 2.15 Notes and References Chapter 3. Stability Theory for Nonlinear Dynamical. Systems 3.1 X CONTENTS 3.8 Problems 3.9 Notes and References Chapter 4. Advanced Stability Theory 4.1

The Sydney Coordinated Adaptive Traffic System (SCATS), an adaptive signal system designed to reduce congestion, has been installed on a heavily trafficked roadway in Portland, OR. In addition to traffic performance metrics, we are investigating how this system affects roadway emissions of air pollutants. A twenty-second reduction to maximum cycle length was proposed for the SCATS system to address pedestrian delay concerns. A two-week trial period with this reduced cycle length was implemented. Travel times and roadside air pollution concentrations were monitored throughout this study period and compared to before and after periods with the current maximum cycle length. Average travel times were found to be significantly higher during the reduced maximum cycle length, but with a mean difference of only 4-5 seconds for travel time. Assessment of travel time for this roadway suggests that a twenty second decrease in maximum cycle length to help shorten pedestrian delay can be made without significant consequences to travel time. Total traffic volumes were consistent for all four weeks of the study. Meteorological conditions were similar for the first two weeks comparing maximum cycle lengths. A shift in ambient temperature led the second two weeks of the cycle length comparison to have more similar meteorological conditions versus the first two weeks. Average NO and NO 2 concentrations were not significantly different for the first half of the study. However, NO and NO 2 concentrations were significantly higher during the reduced maximum cycle length for the second half of the study. When there was a significant difference based on t-test statistics, the measurements did show an increase in roadside concentrations during the shorter maximum cycle length. Preliminary results are unclear if changes to air quality (as assessed by NO and NO 2 concentrations) occur or not due to the reduced maximum cycle length. Results require further comparative analysis in which meteorology and traffic conditions are controlled for so that any difference in air quality due to maximum cycle length alone can be quantified.

In robot navigation and manipulation, accurately determining the camera's pose relative to the environment is crucial for effective task execution. In this paper, we systematically prove that this problem corresponds to the Perspective-3-Point (P3P) formulation, where exactly three known 3D points and their corresponding 2D image projections are used to estimate the pose of a stereo camera. In image-based visual servoing (IBVS) control, the system becomes overdetermined, as the six degrees of freedom (DoF) of the stereo camera must align with nine observed 2D features in the scene. When more constraints are imposed than available DoFs, global stability cannot be guaranteed, as the camera may become trapped in a local minimum far from the desired configuration during servoing. To address this issue, we propose a novel control strategy for accurately positioning a calibrated stereo camera. Our approach integrates a feedforward controller with a Youla parameterization-based feedback controller, ensuring robust servoing performance. Through simulations, we demonstrate that our method effectively avoids local minima and enables the camera to reach the desired pose accurately and efficiently.

In robot navigation and manipulation, accurately determining the camera's pose relative to the environment is crucial for effective task execution. In this paper, we systematically prove that this problem corresponds to the Perspective-3-Point (P3P) formulation, where exactly three known 3D points and their corresponding 2D image projections are used to estimate the pose of a stereo camera. In image-based visual servoing (IBVS) control, the system becomes overdetermined, as the 6 degrees of freedom (DoF) of the stereo camera must align with 9 observed 2D features in the scene. When more constraints are imposed than available DoFs, global stability cannot be guaranteed, as the camera may become trapped in a local minimum far from the desired configuration during servoing. To address this issue, we propose a novel control strategy for accurately positioning a calibrated stereo camera. Our approach integrates a feedforward controller with a Youla parameterization-based feedback controller, ensuring robust servoing performance. Through simulations, we demonstrate that our method effectively avoids local minima and enables the camera to reach the desired pose accurately and efficiently.

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Rooz for their help in implementing my algorithms in simulation. I thank them all for the time and effort they spent in advising and helping me. I would also like to thank Johnny Evers of AFRL/MNGN, Eglin Air Force Base, FL, for inspiring my research topic and for his comments and collaborative efforts during the conduct of this research. I would also like to acknowledge the CERT at Georgia Tech and AFOSR for funding this research. v I am grateful for the time spent with other colleagues at Georgia Tech, both past and present, including Dr.

Rooz for their help in implementing my algorithms in simulation. I thank them all for the time and effort they spent in advising and helping me. I would also like to thank Johnny Evers of AFRL/MNGN, Eglin Air Force Base, FL, for inspiring my research topic and for his comments and collaborative efforts during the conduct of this research. I would also like to acknowledge the CERT at Georgia Tech and AFOSR for funding this research. v I am grateful for the time spent with other colleagues at Georgia Tech, both past and present, including Dr.

Wireless power transfer (WPT) has emerged as a revolutionary technology in the field of power electronics, offering a convenient and contactless method for charging electronic devices. It eliminates the need for physical connectors and enhances device durability and user experience. However, conventional wireless charging systems typically operate with fixed power output and fail to account for variations in the power demand of the receiving device. This often results in energy inefficiency, overheating, and suboptimal charging performance, especially under dynamic load conditions. To address these challenges, this study presents an adaptive wireless charging system that intelligently adjusts its power transmission based on the real-time power consumption and charging efficiency of the load device. The proposed system utilizes inductive and resonant inductive coupling methods and integrates feedback-based adaptive control to optimize power transfer. A monitoring module continuously analyzes the power requirements of the device, and the transmitter dynamically adjusts its output to match the load conditions, reducing unnecessary power loss and improving energy utilization. The system is modeled and simulated using MATLAB/Simulink and LTspice environments, enabling detailed analysis of system behavior under various operating scenarios. Key parameters such as coupling efficiency, power loss, coil alignment, and load variations are considered to evaluate system performance. Simulation results demonstrate that the adaptive control strategy significantly improves overall efficiency and responsiveness of the wireless charging system. The dynamic power adjustment leads to reduced energy waste, stable voltage output, and faster charging under changing load conditions. This adaptive wireless charging solution has promising applications in mobile devices, electric vehicles, wearables, and Internet of Things (IoT) ecosystems, where energy efficiency and flexibility are critical. The outcomes of this research contribute to the development of smarter and more sustainable wireless power systems suitable for real-world applications.

Wireless power transfer (WPT) has emerged as a revolutionary technology in the field of power electronics, offering a convenient and contactless method for charging electronic devices. It eliminates the need for physical connectors and enhances device durability and user experience. However, conventional wireless charging systems typically operate with fixed power output and fail to account for variations in the power demand of the receiving device. This often results in energy inefficiency, overheating, and suboptimal charging performance, especially under dynamic load conditions. To address these challenges, this study presents an adaptive wireless charging system that intelligently adjusts its power transmission based on the real-time power consumption and charging efficiency of the load device. The proposed system utilizes inductive and resonant inductive coupling methods and integrates feedback-based adaptive control to optimize power transfer. A monitoring module continuously analyzes the power requirements of the device, and the transmitter dynamically adjusts its output to match the load conditions, reducing unnecessary power loss and improving energy utilization. The system is modeled and simulated using MATLAB/Simulink and LTspice environments, enabling detailed analysis of system behavior under various operating scenarios. Key parameters such as coupling efficiency, power loss, coil alignment, and load variations are considered to evaluate system performance. Simulation results demonstrate that the adaptive control strategy significantly improves overall efficiency and responsiveness of the wireless charging system. The dynamic power adjustment leads to reduced energy waste, stable voltage output, and faster charging under changing load conditions. This adaptive wireless charging solution has promising applications in mobile devices, electric vehicles, wearables, and Internet of Things (IoT) ecosystems, where energy efficiency and flexibility are critical. The outcomes of this research contribute to the development of smarter and more sustainable wireless power systems suitable for real-world applications.

Supporting quality of service (QoS) over the Internet is a very important issue and many mechanisms have already been devised or are under way towards achieving this goal. One of the most important approaches is the so‐called Differentiated Services (DiffServ) architecture, which provides a scalable mechanism for QoS support in a TCP/IP network. The main concept underlying DiffServ is the aggregation of traffic flows at an ingress (or egress) point of a network and the marking of the IP packets of each traffic flow according to several classification criteria. Diffserv is classified under two taxonomies: the absolute and the relative. In absolute DiffServ architecture, an admission control scheme is utilized to provide QoS as absolute bounds of specific QoS parameters. The relative DiffServ model offers also QoS guarantees per class but in reference to the guarantees given to the other classes defined. In this paper, relative proportional delay differentiation is achieved based on c...

A behavioral programming approach to adaptive autonomous control had been completed. An advanced hexapedal crab-like robot, Ariel, was built in order to investigate methods of robotic self-adaption. As a result of this program, Ariel is able to negotiate the harsh surfzone using fully autonomous distributed gait control. Advancements include the implementation of a high-level controller that recognizes impending unstable etrajectories in the phase space of the gait, and adapts accordingly. Separately, a primate-like head was developed for multi-layer visual control of bipedal and quadrapedal robot navigation. The software architecture incorporates cognitive motivations, selective attention, and a suite of reflexive visual routines, the real-time adaptive software architecture developed under this effort is generically applicable to and DoD embedded real-time system.

This technical note proposes a discrete-time adaptive controller for the control of sampled-data systems. The design is inspired from the Adaptive Posicast Controller (APC) which was designed for time-delay systems in continuous time. Due to the performance degradation caused by digital approximation of continuous laws, together with the problem of assuming time-delays as integer multiples of sampling intervals, the benefits of APC could not be fully realized. In this technical note, these approximations/assumptions are eliminated. In addition, a disturbance observer is incorporated into the controller design which minimizes the effect of disturbances on the system. Extension to the case of uncertain input time-delay is also presented. The proposed approach is verified in simulation studies.

This paper presents a methodology for finding optimal system parameters and optimal control parameters using a novel adaptive particle swarm optimization (APSO) algorithm. In the proposed APSO, every particle dynamically adjusts inertia weight according to feedback taken from particles' best memories. The main advantages of the proposed APSO are to achieve faster convergence speed and better solution accuracy with minimum incremental computational burden. In the beginning we attempt to utilize the proposed algorithm to identify the unknown system parameters the structure of which is assumed to be known previously. Next, according to the identified system, PID gains are optimally found by also using the proposed algorithm. Two simulated examples are finally given to demonstrate the effectiveness of the proposed algorithm. The comparison to PSO with linearly decreasing inertia weight (LDW-PSO) and genetic algorithm (GA) exhibits the APSObased system's superiority.

Production-oriented companies that aspire to the concept of Industry 4.0 primarily focus on the increasing flexibility and reconfigurability of the entire manufacturing system. By integrating a robotic material transport/handling system that features a high level of efficiency, flexibility, and intelligence, the entire manufacturing system reaps the benefits. With that in mind, the authors propose a deep learning-based visual servoing algorithm for the intelligent control of a wheeled mobile robot. By utilizing a visual servoing algorithm, mobile robotic systems can flexibly and efficiently adapt their trajectories to real-world conditions. Moreover, deep learning algorithms allow mobile robots to learn robust visual features that make visual servoing even more applicable. The authors utilize
state-of-the-art deep learning models to train the mobile robot to perform visual servoing even without distinct features that are necessary for such a system to function properly. Experimental evaluation with the own developed mobile robot RAICO – Robot with Artificial Intelligence based COgnition has shown the benefits of the proposed visual control algorithm.

Robots and autonomous systems have become a part of our everyday life, therefore guaranteeing their safety is crucial. Among the possible ways to do so, monitoring is widely used, but few methods exist to systematically generate safety rules to implement such monitors. Particularly, building safety monitors that do not constrain excessively the system's ability to perform its tasks is necessary as those systems operate with few human interventions. We propose in this paper a method to take into account the system's desired tasks in the specification of strategies for monitors and apply it to a case study. We show that we allow more strategies to be found and we facilitate the reasoning about the trade-off between safety and availability.

We develop decentralized controllers for a team of disk-shaped robots to converge to and circulate along the boundary of a desired two-dimensional geometric pattern specified by a smooth function with collision avoidance. The proposed feedback controllers rely solely on each robot's range and bearing sensors which allow them to obtain information about positions of neighbors within a given range. This is releveant for applications such as perimeter surveillance or containing hazardous regions where limited bandwidth must be preserved for situational awareness. The computational complexity of the decentralized controller for each agent is linear in the number of neighboring agents, making it scalable to robot swarms. We establish stability and convergence properties of the controllers and verify the feasibility of the method through computer simulations.

This paper "Harnessing Q-Learning for Real-Time Voltage Adjustment with ESP32: A Demonstration of Adaptive Control and Reinforcement Learning" provides an overview of the research conducted. It emphasizes the application of reinforcement learning, particularly Q-learning, in real-time control systems using the ESP32 microcontroller. The paper outlines the objectives of implementing a Q-learning algorithm to manage and stabilize voltage through a control system involving a servo and potentiometers. It highlights the significance of real-time adaptability and the practical implications of integrating AI with embedded systems. The authors aim to demonstrate the effectiveness of Q-learning in optimizing voltage control and provide insights into future research directions in this domain.

In this work, a novel event-triggered implementation of a tracking controller for a nonlinear strict-feedback system with uncertain dynamics is presented. Approximations of the unknown nonlinear system dynamics are used in the backstepping approach in order to generate the control input. Neural networks (NNs) with event-sampled inputs are used as the means for obtaining the approximations. The system state vector is assumed to be unknown and is estimated with a NN observer. Using the estimated state vector in the backstepping design approach, an event-sampled controller is introduced. With the proposed design, it is demonstrated that the closed-loop system exhibits input-tostate-like stability (ISS) when the feedback, which is continuously accessed by the controller, is injected with bounded measurement errors. Further, for the event triggered control execution, an event-trigger condition is derived using Lyapunov analysis and uniform ultimate bounded (UUB) regulation of the tracking errors, NN weights and the observer errors is demonstrated. Finally, results are also provided for the restricted case when all the states are assumed to be measurable. The effectiveness of the proposed controllers is demonstrated with simulation results.

In this paper, a novel event-triggered implementation of a tracking controller for an uncertain strict-feedback system is presented. Neural networks (NNs) are utilized in the backstepping approach to design a control input by approximating unknown dynamics of the strict-feedback nonlinear system with event-sampled inputs. The system state vector is assumed to be unknown and an NN observer is used to estimate the state vector. By using the estimated state vector and backstepping design approach, an event-sampled controller is introduced. As part of the controller design, first, input-to-state-like stability for a continuously sampled controller that has been injected with bounded measurement errors is demonstrated, and subsequently, an event-execution control law is derived, such that the measurement errors are guaranteed to remain bounded. Lyapunov theory is used to demonstrate that the tracking errors, the observer estimation errors, and the NN weight estimation errors for each NN ...

LOCATE is workspace layout design software that also serves as a testbed for developing and refining principles of adaptive aiding. This demonstration illustrates LOCATE's ability to determine user cognitive styles and provide help matched to those styles. Users are assessed along a Wholist-Analytic dimension and a Verbal-Imagery-Kinesthetic "trimension" and that information is stored in a User Model maintained by LOCATE. Help options provided to users for selecting alternative forms of help permit the system to track those selections and allow for system adaptation to the user's preferred style of help.

The enhancement of the electrical energy security of Navy bases within the continental U.S. has been studied using an approach based on modeling and simulation, with the intent to achieve real time control and energy management. A notional base was developed and used for this study. Its format is general enough to be able to be used as a template for each specific Navy installation. Several preliminary simulations using commercial software have been performed on the power system of this notional base addressing a variety of operating scenarios including islanded mode and the impact of wind and solar power sources. Typical results of steady state and transient operation are also reviewed. The results of these simulations indicate that existing software packages available today, with proper adaptations, can provide a well validated and consistent process for evaluating power system architectures and technologies and, therefore, can become a valuable tool for the implementation of the described plan for Navy bases. The study concludes giving the details of the roadmap to move forward in the process of energy security enhancement of U.S. Navy bases through modeling and simulation. Recently, several other microgrid/smart-grid initiatives have been started around the CONUS, mostly centered on university campuses around the Conutry. As an example, one can point to one of the most successful ones,

This paper explores a nonlinear, adaptive controller aimed at increasing the stability margin of a direct-current (DC), small-scale, electrical network containing an unknown constant power load. Due to its negative incremental impedance, this load reduces the effective damping of the network, which may lead to voltage oscillations and even to voltage collapse. To overcome this drawback we consider the incorporation of a controlled DC-DC power converter in parallel with the constant power load. The design of the control law for the converter is particularly challenging due to the existence of unmeasured states and unknown parameters. We propose a standard input-output linearization stage, to which a suitably tailored adaptive observer is added. The good performance of the controller is validated through experiments on a small-scale network.

We analyze the stability of systems stemming from direct model-reference adaptive control. Although the statements of stability themselves are well-established for many years now, we provide a direct stability analysis both for linear and nonlinear systems under conditions of persistency of excitation. Our proofs are short and constructive as we provide strict Lyapunov functions that have all the required properties as established by Barbashin/Krasovskii's seminal papers on uniform global asymptotic stability.

A real-time parameter estimator for the climate discrete-time dynamic models of a greenhouse located at the North of Portugal are presented. The experiments showed that the second-order models identified for the air temperature and humidity achieve a close agreement between simulated and experimental data. The real-time data acquisition and the recursive identification techniques implemented are used in the simulation and design of an adaptive climate controller to achieve set-point accuracy and minimisation of the energy inputs.

We develop decentralized controllers for a team of disk-shaped robots to converge to and circulate along the boundary of a desired two-dimensional geometric pattern specified by a smooth function with collision avoidance. The proposed feedback controllers rely solely on each robot's range and bearing sensors which allow them to obtain information about positions of neighbors within a given range. This is releveant for applications such as perimeter surveillance or containing hazardous regions where limited bandwidth must be preserved for situational awareness. The computational complexity of the decentralized controller for each agent is linear in the number of neighboring agents, making it scalable to robot swarms. We establish stability and convergence properties of the controllers and verify the feasibility of the method through computer simulations.

The objective of this paper is to present a methodology for the estimation of the impact of Advanced Driver Assistance Systems (ADAS) on network efficiency and the environment. The methodology proposes a set of traffic and emissions simulation models, both microscopic and macroscopic. Issues relevant to the models are discussed, and the inputs to the methodology are presented, along with its expected outputs. An indicative application of the methodology is performed, where the traffic impact of the Adaptive Cruise Control (ACC) system is tested in an urban network. The simulation results indicate that the impact of the introduction of Adaptive Cruise Control can be significant in the improvement of the traffic parameters for high system penetration levels and peak period traffic conditions.

_______________________ 2a. SECURITY CLASSIFICATION AUTHORITY 3 DISTRIBUTION, AVAILABILITY OF ;?E;)(D' 2b DECLASSIFICATION /DOWNGRADING SCHEDULE Approved for public release; distribution unlimited. 4 PERFORMING ORGANIZATION REPORT NUMBER(S) 5 MONITORING ORGANIZATION REPORT r.S2, NRL Memorandum Report 6454 6a. NAME OF PERFORMING ORGANIZATION jbOFCE SOL 7 a NAME OF MOINiTORING ORGAIN:ZA-ON (if applicable) Naval Research Laboratory C ode 6120 ______________________ 6c. ADDRESS (City, State, and Z2P Code) 7b ADDRESS (City, State and ZIP CodeP) Washington, DC 20375-5000 8a. NAME OF FUNDING ISPONSORIN'2 8b OFFICE SYMBOL 9 PROCUREMENT NSTRuMENT IDEN7.FCa CA ON% ORGANIZA7ION (if applicable) Office of Naval Technology I_______ _______________________ 8c. ADDRESS (City, State, and ZIP Code) 10 SOURCE OF FUNDING %UMBERS

can not be changed at runtime, direct trajectory generation computes the motion of the next step based on current state and events. This means that at any instance in time, the trajectory of a motion system can be altered with respect to certain desired kinematic or dynamic constraints. For industrial applications this makes manufacturing on near-repetitive or non-rigid structures (e.g. flexible displays) possible. When applied to a robotic manipulator, this enables obstacle avoidance to no longer be on path planning level, but on trajectory planning level, where kinematic or dynamic constraints can be taken into account. This results in a motion that is smoother than when obstacle avoidance with path planning is employed. For both application areas this direct trajectory generation method is implemented and shows high flexibility in constrained motion trajectory design. vi

An Adaptive Navigation System is considered for navigation of an autonomous guided vehicles (such as non-holonomic mobile robot) with kinematics constrains in an unknown environment. The on-line navigation system. based on integrated reactivecognitive parts. adaptively determines the required actions and/or controUers for the mobile robot. Each controller is adaptive itself. it use an Adaptive Neuro-Fuzzy Inference System (ANFIS). The ANFIS learns and generates the required knowledge for achieving desired goals from the mobile robot behaviour and its environment. T\W different phases i.e. the configuration phase and the real time implementation phase are used to train the controllers. Finally. the experimental results confirm that employed methodology works well for mobile robot navigation in an unknown environment. Copyright @J998IFAC

The use of composite adaptive l a ws for control of the ane class of nonlinear systems having unknown dynamics is proposed. These dynamics are approximated by Gaussian radial basis function neural networks whose parameters are updated by a composite law that is driven by both tracking and estimation errors. This is motivated by the need to improve the speed of convergence of the unknown parameters, hence resulting in better system performance. To ensure global stability despite the inevitable network approximation errors, the control law is augmented with a l o w gain sliding mode component and deadzone adaptation is used for the indirect part of the composite law. The stability of the system is analyzed and the eectiveness of the method is demonstrated by simulation.

Aerospace applications require a variety of sensing technologies to monitor conditions related to both space exploration and aeronautic aircraft operations. These sensing technologies have requirements beyond those of standard commercial applications often due to the flight conditions and harsh environments involved. This paper is a broad overview of efforts to produce Smart Sensor Platforms and Systems that can be tailored to measure multiple chemical species for aerospace applications. A Smart Sensor System as described here implies the use of sensors combined with electronic processing capability and other supporting technologies. Applications include leak detection, engine emissions monitoring, planetary exploration, fire detection, human health monitoring, and environmental monitoring. These efforts range from development of base sensor platforms to the evaluation of more mature systems in relevant environments. Examples will be given of microsensor platform technology, smart sensor systems, and harsh environment sensor systems. Although Smart Sensor Systems can have a significant impact on aerospace applications, extensive relevant demonstration testing is necessary for their long-term implementation. It is suggested that small, smart sensor system technologies are an enabling first step towards more intelligent vehicle systems.

This paper is concerned with analysing, both em pirically and theoretically, the m acroeconom ic consequences arising from term s-of-trade volatility for a resource producing and exporting e co n o m y , w ith e m p h asis p laced upon th e A u stra lia n experience. Using Australian quarterly data for the period 1975 to 1991, the existence of a long-term relationship betw een the term s of trade and nine key m acroeconom ic variables is em p irically verified. These findings form the basis of the th eo retical fram ew ork, w hich is sim ulated , using plausible param eter values, for term s-of-trade disturbances sim ilar to that experienced by Australia during the 1980s. C om parisons with actual developm ents are then conducted.

Pressure drop over the pump Pressure drop over the valve ij Limited sequence of N moves to be opt imised Vector of past inputs-M steps before present time Vector of past inputs between-2M and-M steps before present time Vector of past inputs w ith accumulated moves Sampling time

This paper develops a novel immersion and invariance (I&I)-based adaptive controller for nonlinear underactuated quadrotor systems subject to uncertain inertial parameters. The objective is to drive the quadrotor to accurately track a reference trajectory. The controller synthesis is based on a hierarchical framework. First, the solution to the trajectory tracking problem is transformed to stabilizing its corresponding error system asymptotically. Second, to avoid singularity in the command attitude extraction, a saturated backstepping control strategy with smooth hyperbolic tangent functions is developed to synthesize a command force in the position loop, where an I&I adaptive methodology is introduced to update the mass estimation. The I&I adaption guarantees the asymptotic convergence of the mass estimation to its actual value. Third, in the attitude loop, the projection algorithm is used to update the inertial tensor estimations, which enables the estimations to be maintained within a prescribed range. Then, a rotation matrix-based applied torque with an initial condition constraint is synthesized. It is proven that, with appropriate control parameters, the proposed controller guarantees the asymptotic stability of the error system and then the trajectory tracking of the quadrotor is achieved. Finally, numerical simulations are given to verify and highlight the performance of the proposed controller.

In this article, an image feature extraction method based on two-dimensional (2D) Mellin cepstrum is introduced. The concept of one-dimensional (1D) mel-cepstrum that is widely used in speech recognition is extended to two-dimensions using both the ordinary 2D Fourier transform and the Mellin transform. The resultant feature matrices are applied to two different classifiers such as common matrix approach and support vector machine to test the performance of the mel-cepstrumand Mellin-cepstrum-based features. The AR face image database, ORL database, Yale database and FRGC database are used in experimental studies, which indicate that recognition rates obtained by the 2D mel-cepstrum-based method are superior to that obtained using 2D principal component analysis, 2D Fourier-Mellin transform and ordinary image matrix-based face recognition in both classifiers. Experimental results indicate that 2D cepstral analysis can also be used in other image feature extraction problems.

High order repetitive control application to a CD-player system van den Eerenbeemt, J.F. Award date: 2003 Link to publication Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain High order repetitive control Application to a CD-player system J.F. van den Eerenbeemt DCT Report nr. 2003.64 7th July 2003 Master thesis Engineering Committee: prof. dr. ir. M. Steinbuch (Chairman and coach) dr.

The method of approach used in the present study 3 1 Tension-torsion experimental set up 3 2 Schematic diagram of the machine (front view) 3 3 Schematic diagram of the machine (side view) 3 4 The schematic diagram of a moog brushless drive system 3 5 Schematic diagram of a moog brushless servomotor 3 6 The characteristic curve of motor-1 3 7 The characteristic curve of motor-2 3 8 The flow chart of the operation principle of the controllers 3 9 Various connections of the controller and the power supply unit 3 10 Circuit diagram among the controllers and the motors 3 11 The output characteristic curve of the angle measuring transducer 3 12 The RDP Module 600 amplifier 48 3 13 The front panel for constant load test 3 14 The block diagram for constant load test 3 15 The front panel for constant axial displacement test 3 16 The block diagram for constant axial displacement test 3 17 The front panel for constant angular twist test 3 18 The block diagram for constant angular twist test 3 19 The front panel for constant torque test 3 20 The block diagram for constant torque test 3 21 Block diagram of device 1 AT-AO-6 3 22 AT-AO-6 DAQ device 1 3 23 Block diagram of device2 AT-MIO-I6E-IO 3 24 At-MIO-16E DAQ device2 3 25 Axial load calibration curve 3 26 Torque load cell calibration curve 3 27 LVDT calibration curve 3 28 Angle of twist calibration curve 3 29 Geometry of the specimen 3 30 The assembly drawing of the thin-walled tube 331 Specially designed head to fit with the thin-walled tube 3 32 Different types of strain gauges used 4 1 Axial stress versus axial strain 4 2 Uniaxial tensile load versus axial strain 4 3 Torque versus shear strain curve 4 4 Normalised axial load versus normalised torque curves (constant axial displacement) 86 4 5 Normalised shear strain versus normalised axial load and torque curves (F=YL) 88 4 6 Normalised shear strain versus normalised axial load and torque curves (F=75%YL)

The method of approach used in the present study 3 1 Tension-torsion experimental set up 3 2 Schematic diagram of the machine (front view) 3 3 Schematic diagram of the machine (side view) 3 4 The schematic diagram of a moog brushless drive system 3 5 Schematic diagram of a moog brushless servomotor 3 6 The characteristic curve of motor-1 3 7 The characteristic curve of motor-2 3 8 The flow chart of the operation principle of the controllers 3 9 Various connections of the controller and the power supply unit 3 10 Circuit diagram among the controllers and the motors 3 11 The output characteristic curve of the angle measuring transducer 3 12 The RDP Module 600 amplifier 48 3 13 The front panel for constant load test 3 14 The block diagram for constant load test 3 15 The front panel for constant axial displacement test 3 16 The block diagram for constant axial displacement test 3 17 The front panel for constant angular twist test 3 18 The block diagram for constant angular twist test 3 19 The front panel for constant torque test 3 20 The block diagram for constant torque test 3 21 Block diagram of device 1 AT-AO-6 3 22 AT-AO-6 DAQ device 1 3 23 Block diagram of device2 AT-MIO-I6E-IO 3 24 At-MIO-16E DAQ device2 3 25 Axial load calibration curve 3 26 Torque load cell calibration curve 3 27 LVDT calibration curve 3 28 Angle of twist calibration curve 3 29 Geometry of the specimen 3 30 The assembly drawing of the thin-walled tube 331 Specially designed head to fit with the thin-walled tube 3 32 Different types of strain gauges used 4 1 Axial stress versus axial strain 4 2 Uniaxial tensile load versus axial strain 4 3 Torque versus shear strain curve 4 4 Normalised axial load versus normalised torque curves (constant axial displacement) 86 4 5 Normalised shear strain versus normalised axial load and torque curves (F=YL) 88 4 6 Normalised shear strain versus normalised axial load and torque curves (F=75%YL)

I would like to thank Capt Greg Agnes, Ph.D. for his never ending patience in providing guidance toward establishing the correct methods and more complete expressions presented in this thesis, and his help in showing me actually how simple some of the solutions were to many of my over-complicated problem statements. His help in installing LaTEXthe software on which this thesis was written-and explaining JPEG-to-encapsulated postscript conversion was instrumental in completing this work. I am grateful for his efforts in developing the LaTEX slide-show style files used for the thesis presentation. Without it, preparing for the defense would have been a great deal more arduous. I also would like to thank the faculty of the Department of Aeronautics and Astronautics for their support, and for constantly reminding me (as well as the others) that this was supposed to be fun! I would like to thank Dr. Brad Liebst, our Department Head, for always sounding off during his lectures. Also, many thanks for keeping his office candy jar always filled to the brim. Thank you Lt. Col Little for deciding not to give homework assignments during the end-of-quarter "crunch-time", and Lt. Col Turcotte, for never ever leaving his course outlines to a chance. Finally, I extend my gratitude to the Air Force for selecting me for this master's program, and for continuing to send me to very exotic places.

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This paper is concerned with the implementation and experimental validation of a discrete-time model reference adaptive control strategy, known as Minimal Control Synthesis (MCS) algorithm. After discussing the proof of stability of the algorithm when applied to discretized models of continuous-time plants, the problem of controlling a highly nonlinear electro-mechanical device is taken as a representative case of study. It is shown that the discrete-time MCS is an effective strategy to solve the problem while guaranteeing robustness to unmodeled nonlinear dynamics over a wide range of test manoeuvres.