Dissertations / Theses on the topic 'Control allocation design'

Thesis (Ph. D.)--University of California, San Diego, 2006.
Title from first page of PDF file (viewed May 18, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 127-131).

Control Allocation addresses the problem of the management of multiple, redundant control effectors. Generally speaking, control allocation is any method that is used to determine how the controls of a system should be positioned to achieve some desired effect. An infinite number of allocation methods exist, from the straight-forward direct allocation technique, to the daisy chaining approach, to the computationally simple generalized inverse method. Because different methods have advantages and disadvantages with respect to others, the determination of the "optimal" control allocation method is left to the system designer. The many tradeoffs that are addressed during control system design, of which control allocation is an integral part, dictate the need for a reliable, computer-based design tool. The Control Allocation Toolbox for MATLAB satisfies such a need by providing the designer with a means of testing/comparing the validity of certain allocation methods under prescribed conditions. The issues involved in the development and implementation of the Control Allocation Toolbox are discussed.
Master of Science

Abstract—This paper proposes a new approach for the optimal integrated control and real-time scheduling of control tasks. First, the problem of the optimal integrated control and nonpreemptive off-line scheduling of control tasks in the sense of the H2 performance criterion is addressed. It is shown that this problem may be decomposed into two sub-problems. The first sub-problem aims at finding the optimal non-preemptive off-line schedule, and may be solved using the branch and bound method. The second sub-problem uses the lifting technique to determine the optimal control gains, based on the solution of the first sub-problem. Second, an efficient on-line scheduling algorithm is proposed. This algorithm, called Reactive Pointer Placement (RPP) scheduling algorithm, uses the plant state information to dispatch the computational resources in a way that improves control performance. Control performance improvements as well as stability guarantees are formally proven. Finally, simulations as well as experimental results are presented in order to illustrate the effectiveness of the proposed approach.

With the promise to shape the future of industry, multi-agent robotic technologies have the potential to change many aspects of daily life. Over the coming decade, they are expected to impact transportation systems, military applications such as reconnaissance and surveillance, search-and-rescue operations, or space missions, as well as provide support to emergency first responders. Motivated by the latest developments in the field of robotics, this thesis contributes to the evolution of the future generation of multi-agent robotic systems as they become smarter, more accurate, and diversified in terms of applications. But in order to achieve these goals, the individual agents forming cooperative robotic systems need to be specialized in what they can accomplish, while ensuring accuracy and preserving the ability to perform diverse tasks. This thesis addresses the problem of task allocation in swarm robotics in the specific context where specialized capabilities of the individual agents are considered. Based on the assumption that each individual agent possesses specialized functional capabilities and that the expected tasks, which are distributed in the surrounding environment, impose specific requirements, the proposed task allocation mechanisms are formulated in two different spaces. First, a rudimentary form of the team members’ specialization is formulated as a cooperative control problem embedded in the agents’ dynamics control space. Second, an advanced formulation of agents’ specialization is defined to estimate the individual agents’ task allocation probabilities in a dedicated specialization space, which represents the core contribution of this thesis to the advancement and practice in the area of swarm robotics. The original task allocation process formulated in the specialization space evolves through four stages of development. First, a task features recognition stage is conceptually introduced to leverage the output of a sensing layer embedded in robotic agents to drive the proposed task allocation scheme. Second, a matching scheme is developed to best match each agent’s specialized capabilities with the corresponding detected tasks. At this stage, a general binary definition of agents’ specialization serves as the basis for task-agent association. Third, the task-agent matching scheme is expanded to an innovative probabilistic specialty-based task-agent allocation framework to generalize the concept and exploit the potential of agents’ specialization consideration. Fourth, the general framework is further refined with a modulated definition of the agents’ specialization based on their mechanical, physical structure, and embedded resources. The original framework is extended and a prioritization layer is also introduced to improve the system’s response to complex tasks that are characterized based on the recognition of multiple classes. Experimental validation of the proposed specialty-based task allocation approach is conducted in simulation and on real-world experiments, and the results are presented and discussed in light of potential applications to demonstrate the effectiveness and efficiency of the proposed framework.

Networked real-time systems (NRSs) are pervasive in the real world, and many of them work in an open environment with varying workload. Quality of service (QoS) of NRSs is closely related to the provision of the system resources for servingthe real-time tasks. To provide guarantees of QoS in NRSs, the system resources should be allocated to the real-time tasks in adaptation to the workload variations so that the desired system performance is obtained, referred to as QoS control.This thesis is concerned with the design of dynamic resource allocation schemes for QoS control in three typical NRSs. In the first part, we propose dynamic computing capacity planning schemes for processor utilization control in the distributed real-time systems, and for energy minimization with request response time guarantees in the server clusters. To handle the workload variations, we model the workload uncertainties as the parameters in the system models and use the system performance as online feedback to predict these parameters as precise as possible. Then the optimal computing capacities are provided to serve the real-time tasks in these systems online. Experimental/simulation results demonstrate effectiveness of the proposed schemes for QoS control in comparisons with the existing approaches. In the second part, we propose a dynamic network scheduling scheme for networked control systems (NCSs), typical NRSs with network bandwidth as a critical system resource. The proposed scheduling scheme can properly allocate the network bandwidth to the applications in NCSs so that the good real-time performance can be achieved.
Les systèmes temps-réel en réseau (NRSs) sont de plus en plus utilisés, et beaucoup d'entre eux fonctionnent dans un environnement ouvert aux charges variables. La Qualité de Service (QoS) des NRSs dépend des ressources systèmes pour répondre aux taches en temps-réel. Pour garantir la QoS, les ressources système doivent être allouées dynamiquement, en s'adaptant aux variations de charge, ceci dans le but d'atteindre les performances désirées.Cette thèse traite de la conceptions de méthodes d'allocation dynamique des ressources dans le but d'assurer la QoS dans le cas de 3 NRSs représentatifs. Nous commencerons pas proposer des méthodes de plannification de capacités pour le contrôle de l'utilisation du processeur dans les systèmes distribués, à coup énergétique minimal, avec temps de réponse garanti. Pour supporter les variations de charge, nous utilisons les performances du système pour prédire l'évolution de la charge à venir aussi précisement que possible. Ensuite, les ressources optimales sont libéréespour répondre aux besoins en temp-réel. Les resultats des expérimentations / simulations démontrent l'efficacité de ces méthodes sur le contrôle de la QoS, en comparaison à d'autres approches existantes. Dans un second temps, nous proposerons une méthodologie de séquençage réseau dynamique pour les systèmes controlés en réseau (NCSs), un NRS commun dépendant fortement de la bande passante du réseau. La méthodologie proposée peut correctement allouer la bande passante aux applications du NCS de sorte que de bonnesperformances soient atteintes.

This thesis describes the automatic flight control systems designed for a conventional and an over actuated unmanned air vehicle (UAV). A nonlinear simulation model including the flight mechanics equations together with the interpolated nonlinear aerodynamics, environmental effects, mass-inertia properties, thrust calculations and actuator dynamics is created
trim and linearization codes are developed. Automatic flight control system of the conventional UAV is designed by using both classical and robust control methods. Performances of the designs for full autonomous flight are tested through nonlinear simulations for different maneuvers in the presence of uncertainties and disturbances in the aircraft model. The fault tolerant control of an over actuated UAV is the main concern of the thesis. The flight control system is designed using classical control techniques. Two static control allocation methods are examined: Moore-Penrose pseudo inverse and blended inverse. For this purpose, an aircraft with three sets of ailerons is employed. It is shown that with redundant control surfaces, fault tolerant control is possible. Although both of the static control allocation methods are found to be quite successful to realize the maneuvers, the new blended inverse algorithm is shown to be more effective in controlling the aircraft when some of the control surfaces are lost. It is also demonstrated that, with redundant control surfaces it is possible to recover the aircraft during a maneuver even some of the control surfaces are damaged or got stuck at a particular deflection.

Thesis (Ph.D.)--RAND Graduate School, 2009.
"This document was submitted as a dissertation in June 2009 in partial fulfillment of the requirements of the doctoral degree in public policy analysis at the Pardee RAND Graduate School. The faculty committee that supervised and approved the dissertation consisted of Gregory F. Treverton (Chair), Lynn E. Davis, David E. Mosher, and Walter L. Perry. Professor Kathryn Blackmond Laskey (George Mason University) was the external reader. Financial support for this dissertation was provided by RAND's National Defense Research Institute"--Cover. Title from title screen (viewed on Aug. 24, 2009). Includes bibliographical references: p. 100-105.

Machine-to-Machine (M2M) communications is an emerging communication paradigm that provides ubiquitous connectivity between devices along with an ability to communicate autonomously without human intervention. M2M communications acts as an enabling technology for the practical realization of Internet-of-Things (IoT). However, M2M communications differs from conventional Human-to-Human (H2H) communications due to its unique features such as massive number of connected devices, small data transmissions, little or no mobility, requirements of high energy efficiency and reliability, etc. These features create various challenges for existing communication networks which are primarily optimized for H2H communications. Therefore, novel solutions are required to meet the key requirements of M2M communications. In addition, enhancements are required at different layers of the protocol stack to support co-existence of M2M devices and H2H users. The main objective of this research is to investigate the challenges of M2M communications in two broad types of M2M networks; capillary M2M and cellular M2M networks. The primary focus is on developing novel solutions, algorithms, and protocol enhancements for successfully enabling M2M communications. Since cognitive radio technology is very promising for M2M communications, special emphasis is on capillary M2M networks with cognitive radio based Physical layer. Besides, the focus is also on exploring new frontiers in M2M communications. This thesis covers different aspects of M2M communications. Considering the motivation for cognitive M2M and service requirements of M2M devices, two cognitive MAC protocols have been proposed. The first protocol is centralized in nature and utilizes a specialized frame structure for co-existence with the primary network as well as handling different Quality-of-Service (QoS) requirements of M2M devices. The second protocol is a distributed cognitive MAC protocol, which is specially designed to provide high energy efficiency and reliability for M2M devices operating in challenging wireless environments. Both protocols explicitly account for the peculiarities of cognitive radio environments. The protocols have been evaluated using analytical modeling and simulation studies. Recently IETF has standardized a specially designed routing protocol for capillary M2M networks, known as RPL (Routing for Low Power and Lossy Networks). RPL is emerging as the de facto routing protocol for many M2M applications including the smart grid. On the other hand, the application of cognitive radio for smart grid communication is under active investigation in the research community. Hence, it is important to investigate the applicability and adaptation of RPL in cognitive radio environments. In this regard, an enhanced RPL based routing protocol has been proposed for cognitive radio enabled smart grid networks. The enhanced protocol provides novel modifications to RPL for protecting the primary users along with meeting the utility requirements of the secondary network. An important challenge in LTE-based cellular networks with M2M communications is the uplink radio resource management as available resources are shared between M2M devices and H2H users, having different and often conflicting QoS requirements. Apart from this, energy efficiency requirements become critically important. Further, the specific constraints of Single Carrier Frequency Division Multiple Access (SC-FDMA) complicate the resource allocation problem. In this respect, an energy efficient resource allocation algorithm for the uplink of LTE networks with M2M/H2H co-existence under statistical QoS guarantees has been developed, that is based on canonical duality theory. The proposed algorithm outperforms classical algorithms in terms of energy efficiency while satisfying the QoS requirements of M2M devices and H2H users. A new frontier in M2M communications is the nano-M2M communications, which is envisioned to create the Internet-of-Nano-Things (IoNT). Molecular communication (MC) is a promising communication technique for nano-M2M communications. In literature, no model for error performance of MC exists. Therefore, an error performance model has been developed that explicitly accounts for noise and interference effects. Since relaying and network coding based solutions are gaining popularity for nano-M2M networks, the error performance of a network coded molecular nano-M2M network has been evaluated as well. Finally, the thesis is concluded based on the overall picture of the research conducted. In addition, some directions for future work are included as well.

In design and optimization of a complex system, there exist various methods for defining the relationship between the system as a whole, the subsystems and the individual components. Traditional methods provide requirements at the system level which lead to a set of design targets for each subsystem. Meeting these targets is sometimes a simple task or can be very difficult and expensive, but this is not captured in the design process and therefore unknown at the system level. This work compares Requirements Allocation (RA) with Distributed Value Driven Design (DVDD). A computational experiment is proposed as a means of evaluating RA and DVDD. A common preliminary design is determined by optimizing the utility of the system, and then a Subsystem of Interest (SOI) is chosen as the focal point of subsystem design. First the behavior of a designer using Requirements Allocation is modeled with an optimization problem where the distance to the design targets is minimized. Next, two formulations of DVDD objective functions are used to approximate the system-level value function. The first is a linear approximation and the second is a nonlinear approximation with higher fidelity around the preliminary design point. This computational experiment is applied to a series hybrid vehicle where the SOI is the electric motor. In this case study, RA proves to be more effective than DVDD on average. It is still possible that the use of objectives is superior to design targets. This work shows that, for this case study, a linear approximation as well as a slightly higher fidelity approximation are not well suited to find the design alternative with the highest expected utility.

This research effort develops a comprehensive computational framework to support the parametric optimal design of uncertain dynamical systems. Uncertainty comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it; not accounting for uncertainty may result in poor robustness, sub-optimal performance and higher manufacturing costs. Contemporary methods for the quantification of uncertainty in dynamical systems are computationally intensive which, so far, have made a robust design optimization methodology prohibitive. Some existing algorithms address uncertainty in sensors and actuators during an optimal design; however, a comprehensive design framework that can treat all kinds of uncertainty with diverse distribution characteristics in a unified way is currently unavailable. The computational framework uses Generalized Polynomial Chaos methodology to quantify the effects of various sources of uncertainty found in dynamical systems; a Least-Squares Collocation Method is used to solve the corresponding uncertain differential equations. This technique is significantly faster computationally than traditional sampling methods and makes the construction of a parametric optimal design framework for uncertain systems feasible. The novel framework allows to directly treat uncertainty in the parametric optimal design process. Specifically, the following design problems are addressed: motion planning of fully-actuated and under-actuated systems; multi-objective robust design optimization; and optimal uncertainty apportionment concurrently with robust design optimization. The framework advances the state-of-the-art and enables engineers to produce more robust and optimally performing designs at an optimal manufacturing cost.
Ph. D.

The main objective of this thesis is to design and evaluate reconfigurable flight control system against control surfaces faults in Unmanned Aerial Vehicle (UAV) and aircraft without modifying the baseline controller/control law by using control re-allocation technique. The faults are introduced in the form of partial loss and stuck at unknown positions of control surfaces on the UAV and aircraft. Four control reallocation algorithms with applications to UAV and fixed-wing aircraft were investigated, which include a pseudo-inverse, a fixed-point algorithm, a direct control allocation algorithm and a weighted least squares method. The thesis work is evaluated by a nonlinear UAV model ALTAV (Almost-Light-Than-Air-Vehicles), developed by Quanser Inc., and a nonlinear aircraft model ADMIRE (Aero-Data-Model-in-Research-Environment), developed by the Group of Aeronautical Research and Technology in Europe (GARTEUR). Different faults have been introduced in control surfaces with different commanded inputs. Gaussian noise was introduced in the ALTAV model. Different faults have been introduced in control surfaces with different command inputs. Comparisons were made under normal situation, fault conditions without control re-allocation, and with control reallocation. Simulation results show the satisfactory reconfigurable flight control system performance using control re-allocation methods for ALTAV UAV model and ADMIRE aircraft model.

碩士
國立暨南國際大學
通訊工程研究所
96
The IEEE 802.16d claims to support high bandwidth for the wireless metropolitan area network. However, the standard does not propose including the method for bandwidth management. Therefore, a feasible bandwidth allocation algorithm will be very important. The thesis proposes a Leaky Bucket based admission control algorithm and dynamic bandwidth algorithm for a WiMAX BS. For a new services flow, the admission control algorithm (CAC) will calculate the required bandwidth according to the Leaky Bucket parameters of the flow. Only if the total required bandwidth of all flows is smaller than the system capacity, the new flow can admitted. In the run time, the BS needs to allocate symbols to DL/UL subframes for every frame. In addition to the queue length, we also use the required bandwidth calculated by CAC as a parameter to allocate the bandwidth dynamically. The proposed algorithm are implemented in the NS-2 simulator and verified by simulation. Compared to the fixed allocation scheme, our algorithm can provide quality of service and uses the bandwidth efficiency.

A fundamental question in a sequential decision making setting under uncertainty is “how to allocate resources amongst competing entities so as to maximize the rewards accumulated in the long run?”. The resources allocated may be either abstract quantities such as time or concrete quantities such as manpower. The sequential decision making setting involves one or more agents interacting with an environment to procure rewards at every time instant and the goal is to find an optimal policy for choosing actions. Most of these problems involve multiple (infinite) stages and the objective function is usually a long-run performance objective. The problem is further complicated by the uncertainties in the sys-tem, for instance, the stochastic noise and partial observability in a single-agent setting or private information of the agents in a multi-agent setting. The dimensionality of the problem also plays an important role in the solution methodology adopted. Most of the real-world problems involve high-dimensional state and action spaces and an important design aspect of the solution is the choice of knowledge representation. The aim of this thesis is to answer important resource allocation related questions in different real-world application contexts and in the process contribute novel algorithms to the theory as well. The resource allocation algorithms considered include those from stochastic optimization, stochastic control and reinforcement learning. A number of new algorithms are developed as well. The application contexts selected encompass both single and multi-agent systems, abstract and concrete resources and contain high-dimensional state and control spaces. The empirical results from the various studies performed indicate that the algorithms presented here perform significantly better than those previously proposed in the literature. Further, the algorithms presented here are also shown to theoretically converge, hence guaranteeing optimal performance. We now briefly describe the various studies conducted here to investigate problems of resource allocation under uncertainties of different kinds: Vehicular Traffic Control The aim here is to optimize the ‘green time’ resource of the individual lanes in road networks that maximizes a certain long-term performance objective. We develop several reinforcement learning based algorithms for solving this problem. In the infinite horizon discounted Markov decision process setting, a Q-learning based traffic light control (TLC) algorithm that incorporates feature based representations and function approximation to handle large road networks is proposed, see Prashanth and Bhatnagar [2011b]. This TLC algorithm works with coarse information, obtained via graded thresholds, about the congestion level on the lanes of the road network. However, the graded threshold values used in the above Q-learning based TLC algorithm as well as several other graded threshold-based TLC algorithms that we propose, may not be optimal for all traffic conditions. We therefore also develop a new algorithm based on SPSA to tune the associated thresholds to the ‘optimal’ values (Prashanth and Bhatnagar [2012]). Our thresh-old tuning algorithm is online, incremental with proven convergence to the optimal values of thresholds. Further, we also study average cost traffic signal control and develop two novel reinforcement learning based TLC algorithms with function approximation (Prashanth and Bhatnagar [2011c]). Lastly, we also develop a feature adaptation method for ‘optimal’ feature selection (Bhatnagar et al. [2012a]). This algorithm adapts the features in a way as to converge to an optimal set of features, which can then be used in the algorithm. Service Systems The aim here is to optimize the ‘workforce’, the critical resource of any service system. However, adapting the staffing levels to the workloads in such systems is nontrivial as the queue stability and aggregate service level agreement (SLA) constraints have to be complied with. We formulate this problem as a constrained hidden Markov process with a (discrete) worker parameter and propose simultaneous perturbation based simulation optimization algorithms for this purpose. The algorithms include both first order as well as second order methods and incorporate SPSA based gradient estimates in the primal, with dual ascent for the Lagrange multipliers. All the algorithms that we propose are online, incremental and are easy to implement. Further, they involve a certain generalized smooth projection operator, which is essential to project the continuous-valued worker parameter updates obtained from the SASOC algorithms onto the discrete set. We validate our algorithms on five real-life service systems and compare their performance with a state-of-the-art optimization tool-kit OptQuest. Being ��times faster than OptQuest, our scheme is particularly suitable for adaptive labor staffing. Also, we observe that it guarantees convergence and finds better solutions than OptQuest in many cases. Wireless Sensor Networks The aim here is to allocate the ‘sleep time’ (resource) of the individual sensors in an intrusion detection application such that the energy consumption from the sensors is reduced, while keeping the tracking error to a minimum. We model this sleep–wake scheduling problem as a partially-observed Markov decision process (POMDP) and propose novel RL-based algorithms -with both long-run discounted and average cost objectives -for solving this problem. All our algorithms incorporate function approximation and feature-based representations to handle the curse of dimensionality. Further, the feature selection scheme used in each of the proposed algorithms intelligently manages the energy cost and tracking cost factors, which in turn, assists the search for the optimal sleeping policy. The results from the simulation experiments suggest that our proposed algorithms perform better than a recently proposed algorithm from Fuemmeler and Veeravalli [2008], Fuemmeler et al. [2011]. Mechanism Design The setting here is of multiple self-interested agents with limited capacities, attempting to maximize their individual utilities, which often comes at the expense of the group’s utility. The aim of the resource allocator here then is to efficiently allocate the resource (which is being contended for, by the agents) and also maximize the social welfare via the ‘right’ transfer of payments. In other words, the problem is to find an incentive compatible transfer scheme following a socially efficient allocation. We present two novel mechanisms with progressively realistic assumptions about agent types aimed at economic scenarios where agents have limited capacities. For the simplest case where agent types consist of a unit cost of production and a capacity that does not change with time, we provide an enhancement to the static mechanism of Dash et al. [2007] that effectively deters misreport of the capacity type element by an agent to receive an allocation beyond its capacity, which thereby damages other agents. Our model incorporates an agent’s preference to harm other agents through a additive factor in the utility function of an agent and the mechanism we propose achieves strategy proofness by means of a novel penalty scheme. Next, we consider a dynamic setting where agent types evolve and the individual agents here again have a preference to harm others via capacity misreports. We show via a counterexample that the dynamic pivot mechanism of Bergemann and Valimaki [2010] cannot be directly applied in our setting with capacity-limited alim¨agents. We propose an enhancement to the mechanism of Bergemann and V¨alim¨aki [2010] that ensures truth telling w.r.t. capacity type element through a variable penalty scheme (in the spirit of the static mechanism). We show that each of our mechanisms is ex-post incentive compatible, ex-post individually rational, and socially efficient

碩士
國立暨南國際大學
電機工程學系
96
The widely development of the WLAN technology makes the access of network more convenient. As new applications are created, quality of service (QoS) demand from users is getting important. The traditional IEEE 802.11 standard can not support QoS for real-time data and a new standard, IEEE 802.11e, is designed to solve the QoS issue. However, the call admission control (CAC) algorithm and the calculation of TXOP in IEEE 802.11e are only suitable for transmission CBR traffic. For VBR traffic, the loss rate will be high. The thesis first proposes CAC algorithm based on Gaussian approximation of total arrival traffic. Then a dynamic TXOP allocation algorithm is proposed to efficiently utilize the bandwidth. We will adjust the polling order according to current loss rate for every flow such that each flow has the same loss rate.