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Dissertations / Theses on the topic 'Stochastic robust control'
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1
Cheng, Qifeng. "Robust & stochastic model predictive control." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:89da4934-9de7-4142-958e-513065189518.
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In the thesis, two different model predictive control (MPC) strategies are investigated for linear systems with uncertainty in the presence of constraints: namely robust MPC and stochastic MPC. Firstly, a Youla Parameter is integrated into an efficient robust MPC algorithm. It is demonstrated that even in the constrained cases, the use of the Youla Parameter can desensitize the costs to the effect of uncertainty while not affecting the nominal performance, and hence it strengthens the robustness of the MPC strategy. Since the controller u = K x + c can offer many advantages and is used across the thesis, the work provides two solutions to the problem when the unconstrained nominal LQ-optimal feedback K cannot stabilise the whole class of system models. The work develops two stochastic tube approaches to account for probabilistic constraints. By using a semi closed-loop paradigm, the nominal and the error dynamics are analyzed separately, and this makes it possible to compute the tube scalings offline. First, ellipsoidal tubes are considered. The evolution for the tube scalings is simplified to be affine and using Markov Chain model, the probabilistic tube scalings can be calculated to tighten the constraints on the nominal. The online algorithm can be formulated into a quadratic programming (QP) problem and the MPC strategy is closed-loop stable. Following that, a direct way to compute the tube scalings is studied. It makes use of the information on the distribution of the uncertainty explicitly. The tubes do not take a particular shape but are defined implicitly by tightened constraints. This stochastic MPC strategy leads to a non-conservative performance in the sense that the probability of constraint violation can be as large as is allowed. It also ensures the recursive feasibility and closed-loop stability, and is extended to the output feedback case.
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Munoz, Carpintero Diego Alejandro. "Strategies in robust and stochastic model predictive control." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:2f6bce71-f91f-4d5a-998f-295eff5b089a.
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The presence of uncertainty in model predictive control (MPC) has been accounted for using two types of approaches: robust MPC (RMPC) and stochastic MPC (SMPC). Ideal RMPC and SMPC formulations consider closed-loop optimal control problems whose exact solution, via dynamic programming, is intractable for most systems. Much effort then has been devoted to find good compromises between the degree of optimality and computational tractability. This thesis expands on this effort and presents robust and stochastic MPC strategies with reduced online computational requirements where the conservativeness incurred is made as small as conveniently possible. Two RMPC strategies are proposed for linear systems under additive uncertainty. They are based on a recently proposed approach which uses a triangular prediction structure and a non-linear control policy. One strategy considers a transference of part of the computation of the control policy to an offline stage. The other strategy considers a modification of the prediction structure so that it has a striped structure and the disturbance compensation extends throughout an infinite horizon. An RMPC strategy for linear systems with additive and multiplicative uncertainty is also presented. It considers polytopic dynamics that are designed so as to maximize the volume of an invariant ellipsoid, and are used in a dual-mode prediction scheme where constraint satisfaction is ensured by an approach based on a variation of Farkas' Lemma. Finally, two SMPC strategies for linear systems with additive uncertainty are presented, which use an affine-in-the-disturbances control policy with a striped structure. One strategy considers an offline sequential design of the gains of the control policy, while these are variables in the online optimization in the other. Control theoretic properties, such as recursive feasibility and stability, are studied for all the proposed strategies. Numerical comparisons show that the proposed algorithms can provide a convenient compromise in terms of computational demands and control authority.
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Fleming, James. "Robust and stochastic MPC of uncertain-parameter systems." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:c19ff07c-0756-45f6-977b-9d54a5214310.
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Constraint handling is difficult in model predictive control (MPC) of linear differential inclusions (LDIs) and linear parameter varying (LPV) systems. The designer is faced with a choice of using conservative bounds that may give poor performance, or accurate ones that require heavy online computation. This thesis presents a framework to achieve a more flexible trade-off between these two extremes by using a state tube, a sequence of parametrised polyhedra that is guaranteed to contain the future state. To define controllers using a tube, one must ensure that the polyhedra are a sub-set of the region defined by constraints. Necessary and sufficient conditions for these subset relations follow from duality theory, and it is possible to apply these conditions to constrain predicted system states and inputs with only a little conservatism. This leads to a general method of MPC design for uncertain-parameter systems. The resulting controllers have strong theoretical properties, can be implemented using standard algorithms and outperform existing techniques. Crucially, the online optimisation used in the controller is a convex problem with a number of constraints and variables that increases only linearly with the length of the prediction horizon. This holds true for both LDI and LPV systems. For the latter it is possible to optimise over a class of gain-scheduled control policies to improve performance, with a similar linear increase in problem size. The framework extends to stochastic LDIs with chance constraints, for which there are efficient suboptimal methods using online sampling. Sample approximations of chance constraint-admissible sets are generally not positively invariant, which motivates the novel concept of âsample-admissible' sets with this property to ensure recursive feasibility when using sampling methods. The thesis concludes by introducing a simple, convex alternative to chance-constrained MPC that applies a robust bound to the time average of constraint violations in closed-loop.
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Evans, Martin A. "Multiplicative robust and stochastic MPC with application to wind turbine control." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:0ad9b878-00f3-4cfa-a683-148765e3ae39.
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A robust model predictive control algorithm is presented that explicitly handles multiplicative, or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty in the predicted future states and inputs is bounded by polytopes. The computational cost of running the controller is reduced by calculating matrices offline that provide a means to construct outer approximations to robust constraints to be applied online. The robust algorithm is extended to problems of uncertain models with an allowed probability of violation of constraints. The probabilistic degrees of satisfaction are approximated by one-step ahead sampling, with a greedy solution to the resulting mixed integer problem. An algorithm is given to enlarge a robustly invariant terminal set to exploit the probabilistic constraints. Exponential basis functions are used to create a Robust MPC algorithm for which the predictions are defined over the infinite horizon. The control degrees of freedom are weights that define the bounds on the state and input uncertainty when multiplied by the basis functions. The controller handles multiplicative and additive uncertainty. Robust MPC is applied to the problem of wind turbine control. Rotor speed and tower oscillations are controlled by a low sample rate robust predictive controller. The prediction model has multiplicative and additive uncertainty due to the uncertainty in short-term future wind speeds and in model linearisation. Robust MPC is compared to nominal MPC by means of a high-fidelity numerical simulation of a wind turbine under the two controllers in a wide range of simulated wind conditions.
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Hua, H. "Optimal and robust control for a class of nonlinear stochastic systems." Thesis, University of Liverpool, 2016. http://livrepository.liverpool.ac.uk/3001023/.
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This thesis focuses on theoretical research of optimal and robust control theory for a class of nonlinear stochastic systems. The nonlinearities that appear in the diffusion terms are of a square-root type. Under such systems the following problems are investigated: optimal stochastic control in both finite and infinite horizon; robust stabilization and robust H∞ control; H₂/H∞ control in both finite and infinite horizon; and risk-sensitive control. The importance of this work is that explicit optimal linear controls are obtained, which is a very rare case in the nonlinear system. This is regarded as an advantage because with explicit solutions, our work becomes easier to be applied into the real problems. Apart from the mathematical results obtained, we have also introduced some applications to finance.
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Kim, Dongwook Sawan Edwin M. "Application of stochastic control and robust stability of singularly perturbed unified systems." Diss., Click here for available full-text of this thesis, 2006. http://library.wichita.edu/digitallibrary/etd/2006/t026.pdf.
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Thesis (M.S.)--Wichita State University, Dept. of Electrical Engineering."August 2006." Title from PDF title page (viewed on October 2, 2006). Thesis adviser: Edwin M. Sawan. Includes bibliographic references (leaves 50-53).
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Song, Miao Ph D. Massachusetts Institute of Technology. "Applications of stochastic inventory control in market-making and robust supply chains." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62049.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 169-172).
This dissertation extends the classical inventory control model to address stochastic inventory control problems raised in market-making and robust supply chains. In the financial market, market-makers assume the role of a counterpart so that investors can trade any fixed amounts of assets at quoted bid or ask prices at any time. Market-makers benefit from the spread between the bid and ask prices. but they have to carry inventories of assets which expose them to potential losses when the market price moves in an undesirable direction. One approach to reduce the risk associated with price uncertainty is to actively trade with other Market-Makers at the price of losing potential spread gain. We propose a dynamic programming model to determine the optimal active trading quantity., which maximizes the Market-Maker's expected utility. For a single-asset model. We show that a threshold inventory control policy is optimal with respect to both an exponential utility criterion and a mean-variance tradeoff objective. Special properties such as symmetry and monotonicity of the threshold levels are also investigated. For a multiple-asset model. the mean-variance analysis suggests that there exists a connected no-trade region such that the Market-Maker does not need to actively trade with other market-makers if the inventory falls in the no-trade region. Outside the no-trade region. the optimal way to adjust inventory levels can be obtained from the boundaries of the no-trade region. These properties of the optimal policy lead to practically efficient algorithms to solve the problem. The dissertation also considers the stochastic inventory control model in robust supply chain systems. Traditional approaches in inventory control first estimate the demand distribution among a predefined family of distributions based on data fitting of historical demand observations, and then optimize the inventory control policy using the estimated distributions. which often leads to fragile solutions in case the preselected family of distributions was inadequate. In this work. we propose a minimax robust model that integrates data fitting and inventory optimization for the single item multi-period periodic review stochastic lot-sizing problem. Unlike the classical stochastic inventory models, where demand distribution is known, we assume that histograms are part of the input. The robust model generalizes Bayesian model, and it can be interpreted as minimizing history dependent risk measures. We prove that the optimal inventory control policies of the robust model share the same structure as the traditional stochastic dynamic programming counterpart. In particular., we analyze the robust models based on the chi-square goodness-of-fit test. If demand samples are obtained from a known distribution, the robust model converges to the stochastic model with true distribution under general conditions.
by Miao Song.
Ph.D.
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Yang, Lin. "Linear robust H-infinity stochastic control theory on the insurance premium-reserve processes." Thesis, University of Liverpool, 2015. http://livrepository.liverpool.ac.uk/2037227/.
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This thesis deals with the stability analysis of linear discrete-time premium-reserve (P-R) systems in a stochastic framework. Such systems are characterised by a mixture of the premium pricing process and the medium- and long- term stability in the accumulated reserve (surplus) policy, and they play a key role in the modern actuarial literature. Although the mathematical and practical analysis of P-R systems is well studied and motivated, their stability properties have not been studied thoughtfully and they are restricted in a deterministic framework. In Engineering, during the last three decades, many useful techniques are developed in linear robust control theory. This thesis is the first attempt to use some useful tools from linear robust control theory in order to analyze the stability of these classical insurance systems. Analytically, in this thesis, P-R systems are first formulated with structural properties such that time-varying delays, random disturbance and parameter uncertainties. Then as an extension of the previous literature, the results of stabilization and the robust H-infinity control of P-R systems are modelled in stochastic framework. Meanwhile, the risky investment impact on the P-R system stability condition is shown. In this approach, the potential effects from changes in insurer's investment strategy is discussed. Next we develop regime switching P-R systems to describe the abrupt structural changes in the economic fundamentals as well as the periodic switches in the parameters. The results for the regime switching P-R system are illustrated by means of two different approaches: markovian and arbitrary regime switching systems. Finally, we show how robust guaranteed cost control could be implemented to solve an optimal insurance problem. In each chapter, Linear Matrix Inequality (LMI) sufficient conditions are derived to solve the proposed sub-problems and numerical examples are given to illustrate the applicability of the theoretical findings.
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Paul, Anand Abraham. "Stochastic models in the analysis of project subcontracting and robustness to variability in project networks." Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p9992885.
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Azad, Saeed. "Combined Design and Control Optimization of Stochastic Dynamic Systems." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1602153122063302.
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Chohan, Neha. "Robust trajectory planning of autonomous vehicles at intersections with communication impairments." Thesis, Luleå tekniska universitet, Rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-75571.
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In this thesis, we consider the trajectory planning of an autonomous vehicle to cross an intersection within a given time interval. The vehicle communicates its sensordata to a central coordinator which then computes the trajectory for the given time horizon and sends it back to the vehicle. We consider a realistic scenario in which the communication links are unreliable, the evolution of the state has noise (e.g., due to the model simplification and environmental disturbances), and the observationis noisy (e.g., due to noisy sensing and/or delayed information). The intersection crossing is modeled as a chance constraint problem and the stochastic noise evolution is restricted by a terminal constraint. The communication impairments are modeled as packet drop probabilities and Kalman estimation techniques are used for predicting the states in the presence of state and observation noises. A robust sub-optimalsolution is obtained using convex optimization methods which ensures that the intersection is crossed by the vehicle in the given time interval with very low chance of failure.
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Lin, Jessica. "Robust Modelling of the Glucose-Insulin System for Tight Glycemic Control of Critical Care Patients." Thesis, University of Canterbury. Mechanical, 2007. http://hdl.handle.net/10092/1570.
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Hyperglycemia is prevalent in critical care, as patients experience stress-induced
hyperglycemia, even with no history of diabetes. Hyperglycemia has a significant
impact on patient mortality, outcome and health care cost. Tight regulation
can significantly reduce these negative outcomes, but achieving it remains clinically
elusive, particularly with regard to what constitutes tight control and what
protocols are optimal in terms of results and clinical effort.
Hyperglycemia in critical care is not largely benign, as once thought, and has
a deleterious effect on outcome. Recent studies have shown that tight glucose
regulation to average levels from 6.1–7.75 mmol/L can reduce mortality 17–45%,
while also significantly reducing other negative clinical outcomes. However, clinical
results are highly variable and there is little agreement on what levels of
performance can be achieved and how to achieve them.
A typical clinical solution is to use ad-hoc protocols based primarily on experience,
where large amounts of insulin, up to 50 U/hr, are titrated against
glucose measurements variably taken every 1–4 hours. When combined with the
unpredictable and sudden metabolic changes that characterise this aspect of critical
illness and/or clinical changes in nutritional support, this approach results
in highly variable blood glucose levels. The overall result is sustained periods
of hyper- or hypo- glycemia, characterised by oscillations between these states,
which can adversely affect clinical outcomes and mortality. The situation is exacerbated
by exogenous nutritional support regimes with high dextrose content.
Model-based predictive control can deliver patient specific and adaptive control,
ideal for such a highly dynamic problem. A simple, effective physiological
model is presented in this thesis, focusing strongly on clinical control feasibility.
This model has three compartments for glucose utilisation, interstitial insulin and its transport, and insulin kinetics in blood plasma. There are two patient
specific parameters, the endogenous glucose removal and insulin sensitivity. A
novel integral-based parameter identification enables fast and accurate real-time
model adaptation to individual patients and patient condition.
Three stages of control algorithm developments were trialed clinically in the
Christchurch Hospital Department of Intensive Care Medicine. These control
protocols are adaptive and patient specific. It is found that glycemic control utilising
both insulin and nutrition interventions is most effective. The third stage of
protocol development, SPRINT, achieved 61% of patient blood glucose measurements
within the 4–6.1 mmol/L desirable glycemic control range in 165 patients.
In addition, 89% were within the 4–7.75 mmol/L clinical acceptable range. These
values are percentages of the total number of measurements, of which 47% are
two-hourly, and the rest are hourly. These results showed unprecedented tight
glycemic control in the critical care, but still struggle with patient variability and
dynamics.
Two stochastic models of insulin sensitivity for the critically ill population
are derived and presented in this thesis. These models reveal the highly dynamic
variation in insulin sensitivity under critical illness. The stochastic models can deliver
probability intervals to support clinical control interventions. Hypoglycemia
can thus be further avoided with the probability interval guided intervention assessments.
This stochastic approach brings glycemic control to a more knowledge
and intelligible level.
In “virtual patient” simulation studies, 72% of glycemic levels were within
the 4–6.1 mmol/L desirable glycemic control range. The incidence level of hypoglycemia
was reduced to practically zero. These results suggest the clinical
advances the stochastic model can bring. In addition, the stochastic models reflect
the critical patients’ insulin sensitivity driven dynamics. Consequently, the
models can create virtual patients to simulated clinical conditions. Thus, protocol
developments can be optimised with guaranteed patient safety.
Finally, the work presented in this thesis can act as a starting point for many
other glycemic control problems in other environments. These areas include the
cardiac critical care and neonatal critical care that share the most similarities to
the environment studied in this thesis, to general diabetes where the population is growing exponentially world wide. Furthermore, the same pharmacodynamic
modelling and control concept can be applied to other human pharmacodynamic
control problems. In particular, stochastic modelling can bring added knowledge
to these control systems. Eventually, this added knowledge can lead clinical
developments from protocol simulations to better clinical decision making.
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Van, Langenhove Jan Willem. "Adaptive control of deterministic and stochastic approximation errors in simulations of compressible flow." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066357/document.
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La simulation de systèmes d'ingénierie non linéaire complexes tels que les écoulements de fluide compressibles peut être ciblée pour rendre plus efficace et précise l'approximation d'une quantité spécifique (scalaire) d'intérêt du système. En mettant de côté l'erreur de modélisation et l'incertitude paramétrique, on peut y parvenir en combinant des estimations d'erreurs axées sur des objectifs et des raffinements adaptatifs de maillage spatial anisotrope. A cette fin, un cadre élégant et efficace est celui de l'adaptation dite basé-métrique où une estimation d'erreur a priori est utilisée comme indicateur d’adaptation de maillage. Dans cette thèse on propose une nouvelle extension de cette approche au cas des approximations de système portant une composante stochastique. Dans ce cas, un problème d'optimisation est formulé et résolu pour un meilleur contrôle des sources d'erreurs. Ce problème est posé dans le cadre continu de l'espace de métrique riemannien. Des développements algorithmiques sont également proposés afin de déterminer les sources dominates d’erreur et effectuer l’adaptation dans les espaces physique ou des paramètres incertains. L’approche proposé est testée sur divers problèmes comprenant une entrée de scramjet supersonique soumise à des incertitudes paramétriques géométriques et opérationnelles. Il est démontré que cette approche est capable de bien capturé les singularités dans l’escape stochastique, tout en équilibrant le budget de calcul et les raffinements de maillage dans les deux espacesThe simulation of complex nonlinear engineering systems such as compressible fluid flows may be targeted to make more efficient and accurate the approximation of a specific (scalar) quantity of interest of the system. Putting aside modeling error and parametric uncertainty, this may be achieved by combining goal-oriented error estimates and adaptive anisotropic spatial mesh refinements. To this end, an elegant and efficient framework is the one of (Riemannian) metric-based adaptation where a goal-based a priori error estimation is used as indicator for adaptivity. This thesis proposes a novel extension of this approach to the case of aforementioned system approximations bearing a stochastic component. In this case, an optimisation problem leading to the best control of the distinct sources of errors is formulated in the continuous framework of the Riemannian metric space. Algorithmic developments are also presented in order to quantify and adaptively adjust the error components in the deterministic and stochastic approximation spaces. The capability of the proposed method is tested on various problems including a supersonic scramjet inlet subject to geometrical and operational parametric uncertainties. It is demonstrated to accurately capture discontinuous features of stochastic compressible flows impacting pressure-related quantities of interest, while balancing computational budget and refinements in both spaces
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Lin, Xiaohai [Verfasser]. "Robust and Stochastic Model Predictive Control of Linear Systems with Predictable Additive Disturbance : with Application to Multi-Objective Adaptive Cruise Control / Xiaohai Lin." Düren : Shaker, 2020. http://d-nb.info/121347261X/34.
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Zhu, Dinghuan. "Multi - Timescale Control of Energy Storage Enabling the Integration of Variable Generation." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/332.
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A two-level optimal coordination control approach for energy storage and conventional generation consisting of advanced frequency control and stochastic optimal dispatch is proposed to deal with the real power balancing control problem introduced by variable renewable energy sources (RESs) in power systems. In the proposed approach, the power and energy constraints on energy storage are taken into account in addition to the traditional power system operational constraints such as generator output limits and power network constraints. The advanced frequency control level which is based on the robust control theory and the decentralized static output feedback design is responsibl e for the system frequency stabilization and restoration, whereas the stochastic optimal dispatch level which is based on the concept of stochastic model predictive control (SMPC) determines the optimal dispatch of generation resources and energy storage under uncertainties introduced by RESs as well as demand. In the advanced frequency control level, low-order decentralized robust frequency controllers for energy storage and conventional generation are simultaneously designed based on a state-space structure-preserving model of the power system and the optimal controller gains are solved via an improved linear matrix inequality algorithm. In the stochastic optimal dispatch level, various optimization decomposition techniques including both primal and dual decompositions together with two different decomposition schemes (i.e. scenario-based decomposition and temporal-based decomposition) are extensively investigated in terms of convergence speed due to the resulting large-scale and computationally demanding SMPC optimization problem. A two-stage mixed decomposition method is conceived to achieve the maximum speedup of the SMPC optimization solution process. The underlying control design philosophy across the entire work is the so-called time-scale matching principle, i.e. the conventional generators are mainly responsible to balance the low frequency components of the power variations whereas the energy storage devices because of their fast response capability are employed to alleviate the relatively high frequency components. The performance of the proposed approach is tested and evaluated by numerical simulations on both the WECC 9-bus system and the IEEE New England 39-bus system.
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Ahmad, Ayaz. "On Resource Optimization and Robust CQI Reporting for Wireless Communication Systems." Phd thesis, Supélec, 2011. http://tel.archives-ouvertes.fr/tel-00771973.
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Adaptive resource allocation in wireless communication systems is crucial in order to support the diverse QoS needs of the services and optimize resource utilization. The design of resource allocation schemes should consider the service type for which it is intended. Moreover, due to feedback delay and channel estimation error, the Channel Quality Indicator (CQI) reported to the transmitter may not be a perfect measure of the channel quality and its use for resource allocation may severely degrade the systems performance. In this thesis, we study resource allocation and CQI reporting for wireless networks while taking the aforementioned factors into consideration. First, we consider resource allocation and adaptive modulation in uplink SC-FDMA systems. This is a combinatorial problem whose optimal solution is exponentially complex. We use canonical duality theory to derive a polynomial complexity resource allocation algorithm that provides a nearly optimal solution to the problem. Then, we focus on resource allocation for video streaming in wireless networks with time-varying interference. To this end, by using risk-sensitive control approach, we develop a cross-layer optimization framework that performs power control at the PHY/MAC layer and rate adaptation at the APPLICATION layer jointly and provides fairness among nodes. Finally, by using stochastic control and game theory, we design a robust best-M CQI reporting scheme for multi-carrier and multi-user systems which takes into account the impact of feedback delay and error in CQI computation. Performing resource allocation on the basis of the proposed CQI reporting can significantly improve the system performance.
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Liu, Jianzhe. "On Control and Optimization of DC Microgrids." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1512049527948171.
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Caceres, Zuniga Yusef Rafael. "Alocação de potencia em sistemas de comunicações sem fio : abordagens estocastica via o CVaR e robusta." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261030.
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Orientador: Michel Daoud YacoubTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
Made available in DSpace on 2018-08-10T01:21:53Z (GMT). No. of bitstreams: 1 CaceresZuniga_YusefRafael_D.pdf: 1196886 bytes, checksum: b589961266e398a3fd22bfd7b30719e4 (MD5) Previous issue date: 2007
Resumo: Nesta tese, estuda-se o problema da alocação de potência através de duas abordagens: estocástica e robusta, sendo os ganhos do canal, que descrevem o estado do sistema de comunicações sem fio, parcialmente observados pelo decisor. Na abordagem estocástica, considera-se que os ganhos do canal são variáveis aleatórias, que representam a variação rápida do sinal de rádio. Nesse contexto, reformula-se o índice de desempenho do sistema através do CVaR (Conditional. Value-at-Risk). Na abordagem robusta, considera-se que os ganhos do canal e o ruído pertencem a um determinado conjunto convexo. Em ambas as abordagens, a solução ótima é obtida em termos de um problema de otimização convexa. Adicionalmente, na abordagem estocástica, apresenta-se um algoritmo recursivo e distribuído, que converge para uma solução subótima, quando o ruído é nulo e a potência transmitida é limitada tanto superior como inferiormente. Também mostra-se que, em um sistema onde os ganhos do canal coincidem com o seu valor esperado, esse algoritmo converge para a soluçãã ótima quando a qualidade do enlace é muito maior que a mínima requerida
Abstract: This thesis deals with the power allocation problem under the stochastic and robust approaches, where the channel gains describe the wireless communication system state and are partially known by the controller. The stochastic approach considers the channel gains as random variables which represent the fast fading of the radio signal. Under these settings, the system performance index is reformulated using CVaR (Conditional Value-at-Risk). The robust approach considers that the channels gains and noise belong to a determined convex set. ln both approaches, the optimal solution is determined in terms of a convex optimization problem. Additionally, under the stochastic approach, a recursive and distributed algorithm is presented which converges to its suboptimal solution when noise is null and the transmitted power is upper and lower bounded. It is also show that this algorithm converges to its optimal solution when the link quality is much greater than the minimum required quality in a system where the channels gains match its expected value
Doutorado
Telecomunicações e Telemática
Doutor em Engenharia Elétrica
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Хоменко, Віктор Віталійович. "Багатокритеріальний синтез систем із анізотропійними регуляторами на основі стохастичної мультиагентної оптимізації." Thesis, Українська інженерно-педагогічна академія, 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/22705.
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Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13.03 – системи та процеси керування. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2016.
У роботі вирішена науково-практична задача підвищення точності керування багатомасовими електромеханічними системами з невизначеними параметрами на основі багатокритеріального підходу до стохастичних робастних методів. Розроблено метод багатокритеріального синтезу анізотропійних регуляторів для керування багатомасовими електромеханічними системами із параметричною невизначеністю. Багатокритеріальний синтез анізотропійних регуляторів зведено до рішення задачі векторного математичного нелінійного програмування, в якій обчислення векторної цільової функції і обмежень зводиться до ітеративного рішення системи з чотирьох пов'язаних рівнянь Ріккаті, рівняння Ляпунова та обчислення виразу спеціального виду. Удосконалено метод рішення багатокритеріальної задачі математичного програмування на основі багатороєвоі стохастичної мультиагентної оптимізації на основі Парето – оптимальних рішень. Досліджено динамічні характеристики синтезованих багатомасових електромеханічних систем з анізотропійними регуляторами, які синтезовані на основі багатокритеріального підходу. Застосування робастних регуляторів дозволило підвищити швидкодію системи за рахунок скорочення часу першого узгодження в 4,1 разів, зменшити помилку регулювання швидкості обертання при випадковій зміні моменту зовнішнього впливу більше ніж у 2 рази, системи до зміни параметрів об'єкту керування у порівнянні із системою з типовими регуляторами. Проведено експериментальні дослідження синтезованих систем із анізотропійними регуляторами на стенді стохастичної двомасової електромеханічної системи. Експериментально встановлено, що застосування анізотропійних регуляторів у стенді двомасової електромеханічної системи дозволило скоротити час першого узгодження регулювання швидкості у 2 рази, зменшити помилку регулювання швидкості обертання при випадковій зміні моменту зовнішнього впливу більше ніж у 2 рази у порівнянні із системою з типовими регуляторами.Thesis for scientific degree of candidate of engineering science on speciality 05.13.03 – control systems and processes. – National Technical University "Kharkiv Polytechnic Institute", Kharkiv, 2016. The thesis is devoted to solving scientific and practice task of increasing the accuracy of control multimass electromechanical systems with uncertain parameters on the basis of multiobjective approach to the synthesis of anisotropic regulators. A method of multiobjective synthesis of anisotropic regulators multimass electromechanical systems, which allows to meet the diverse requirements that apply to the work multimass electromechanical systems in various modes. Multiobjective synthesis of anisotropic regulators reduced to solution the vector nonlinear programming, in which the calculation of the objective function is algo-rithmic in nature, involving multiple solution of algebraic Riccati equations, Lyapunov equations and special expressions for calculating of anisotropy level. Improved method for solving multiobjective programming problem based on a stochastic multi-agent multi swarm optimization based on Pareto-optimal solutions. Investigated the dynamic characteristics of the synthesized multimass electro-mechanical systems with anisotropic regulators are synthesized from multi approach. The results of the comparisons of dynamic characteristics multimass electro-mechanical systems with synthesized anisotropic regulators and controller types. It is proved that the use of synthetic anisotropic regulators allowed to reduce time of the first coordination by 4.1 times, to reduce the error compensation of random external perturbation 2 times and to reduce the system sensitivity to changes of plant parameters compared to a system with standard controllers. Experimental on the stand two-mass electromechanical system found that the use of synthesized anisotropic regulators can reduce time of the first coordination by 2 times, reduce the error speed control for random change of the load torque is 2 times as compared with the system controller types.
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N'Goran, Arnold. "Contrôle optimal et gestion énergétique d'une station d'énergie autonome par optimisation robuste." Thesis, Université Paris sciences et lettres, 2020. http://www.theses.fr/2020UPSLM050.
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Le contrôle des micro-réseaux électriques repose sur un problème d’optimisation complexe quand il doit tenir compte de la production intermittente et imparfaitement prévisible des sources d’énergie renouvelables, et de la dynamique physique court-terme des solutions de stockage mises en place pour pallier à cette intermittence. Cette thèse veut apporter un éclairage sur la performance pratique comparée des méthodes d’optimisation pour le contrôle, avec la mise en œuvre de différentes stratégies, exactes ou approchées, analytiques ou numériques, déterministes, stochastiques ou robustesPower microgrid control involves solving a complex optimisation problem when it must deal with the intermittent, poorly forecasted production of renewable energy sources and with the short-term dynamics of the storage devices used to address intermittency issue. This thesis aims to shed light on the compared practical performance of optimization methods in control with the implementation of different strategies, exact or approximated, analytical or numerical, deterministic, stochastic or robust
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Хоменко, Віктор Віталійович. "Багатокритеріальний синтез систем із анізотропійними регуляторами на основі стохастичної мультиагентної оптимізації." Thesis, НТУ "ХПІ", 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/22704.
Full textAbstract:
Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13.03 – системи та процеси керування. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2016.
У роботі вирішена науково-практична задача підвищення точності керування багатомасовими електромеханічними системами з невизначеними параметрами на основі багатокритеріального підходу до стохастичних робастних методів. Розроблено метод багатокритеріального синтезу анізотропійних регуляторів для керування багатомасовими електромеханічними системами із параметричною невизначеністю. Багатокритеріальний синтез анізотропійних регуляторів зведено до рішення задачі векторного математичного нелінійного програмування, в якій обчислення векторної цільової функції і обмежень зводиться до ітеративного рішення системи з чотирьох пов'язаних рівнянь Ріккаті, рівняння Ляпунова та обчислення виразу спеціального виду. Удосконалено метод рішення багатокритеріальної задачі математичного програмування на основі багатороєвоі стохастичної мультиагентної оптимізації на основі Парето – оптимальних рішень. Досліджено динамічні характеристики синтезованих багатомасових електромеханічних систем з анізотропійними регуляторами, які синтезовані на основі багатокритеріального підходу. Застосування робастних регуляторів дозволило підвищити швидкодію системи за рахунок скорочення часу першого узгодження в 4,1 разів, зменшити помилку регулювання швидкості обертання при випадковій зміні моменту зовнішнього впливу більше ніж у 2 рази, системи до зміни параметрів об'єкту керування у порівнянні із системою з типовими регуляторами. Проведено експериментальні дослідження синтезованих систем із анізотропійними регуляторами на стенді стохастичної двомасової електромеханічної системи. Експериментально встановлено, що застосування анізотропійних регуляторів у стенді двомасової електромеханічної системи дозволило скоротити час першого узгодження регулювання швидкості у 2 рази, зменшити помилку регулювання швидкості обертання при випадковій зміні моменту зовнішнього впливу більше ніж у 2 рази у порівнянні із системою з типовими регуляторами.Thesis for scientific degree of candidate of engineering science on speciality 05.13.03 – control systems and processes. – National Technical University "Kharkiv Polytechnic Institute", Kharkiv, 2016. The thesis is devoted to solving scientific and practice task of increasing the accuracy of control multimass electromechanical systems with uncertain parameters on the basis of multiobjective approach to the synthesis of anisotropic regulators. A method of multiobjective synthesis of anisotropic regulators multimass electromechanical systems, which allows to meet the diverse requirements that apply to the work multimass electromechanical systems in various modes. Multiobjective synthesis of anisotropic regulators reduced to solution the vector nonlinear programming, in which the calculation of the objective function is algo-rithmic in nature, involving multiple solution of algebraic Riccati equations, Lyapunov equations and special expressions for calculating of anisotropy level. Improved method for solving multiobjective programming problem based on a stochastic multi-agent multi swarm optimization based on Pareto-optimal solutions. Investigated the dynamic characteristics of the synthesized multimass electro-mechanical systems with anisotropic regulators are synthesized from multi approach. The results of the comparisons of dynamic characteristics multimass electro-mechanical systems with synthesized anisotropic regulators and controller types. It is proved that the use of synthetic anisotropic regulators allowed to reduce time of the first coordination by 4.1 times, to reduce the error compensation of random external perturbation 2 times and to reduce the system sensitivity to changes of plant parameters compared to a system with standard controllers. Experimental on the stand two-mass electromechanical system found that the use of synthesized anisotropic regulators can reduce time of the first coordination by 2 times, reduce the error speed control for random change of the load torque is 2 times as compared with the system controller types.
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Horchler, Andrew de Salle. "Design of Stochastic Neural-inspired Dynamical Architectures: Coordination and Control of Hyper-redundant Robots." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1459442036.
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Karim, Yassine. "Caractérisation robuste de liaisons amortissantes avec dispositifs piezo-électriques pour la réduction de vibrations de structures." Phd thesis, Conservatoire national des arts et metiers - CNAM, 2013. http://tel.archives-ouvertes.fr/tel-00953330.
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L'étude présentée dans ce document a pour objet l'étude de différents modes de réduction de vibrations dans les structures avec liaisons. Le premier mode étudié se base sur la dissipation d'énergie apportée par la déformation d' éléments piézoélectriques connectés à un circuit électrique adapté. Le second mode proposé se base sur la propriété de la liaison boulonnée à changer les fréquences propres d'une structure en fonction du serrage appliqué. Cette propriété est utilisée avec plusieurs lois de contrôle du serrage afin d'éviter les plages de fréquences critiques. Ensuite une étude probabiliste est effectuée pour déterminer la robustesse de la réduction de vibrations par rapport à la variation de certains paramètres du modèle. Cette étude de robustesse est effectuée à travers des méthodes stochastiques non-intrusives, parmi lesquelles une méthode originale proposée. Elle permet une réduction de la taille du modèle stochastique à résoudre, ce qui réduit très considérablement le temps de calcul sans perte de qualité significative.
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Schneider, Antoine. "Contribution à l'identification et la commande de systèmes stochastiques discrets par des méthodes hiérarchisées : Application au modèle d'un convertisseur d'acier." Nancy 1, 1987. http://www.theses.fr/1987NAN10248.
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"Final report on robust stochastic adaptive control." Massachusetts Institute of Technology, Laboratory for Information and Decision Systems, 1988. http://hdl.handle.net/1721.1/2787.
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prepared by Lena Valavani, Michael Athans ; submitted to Office of Naval Research, Mathematical Sciences Division.Includes bibliographical references.
Supported by the Office of Naval Research under contract N00014-82-K-0582 NR606-003 MIT OSP no.92775
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Li, Xiang. "Probabilistic robust control system design by stochastic optimization." 2004. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-638/index.html.
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Wu, Hsuan-Liang, and 吳泫良. "Robust Switching Fuzzy Control for Nonlinear Time-Varying Stochastic Systems." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/12053113835723264597.
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碩士國立清華大學
電機工程學系
95
In this study, the problem of robust control for nonlinear stochastic systems subject to time-varying parameter is treated by a proposed switching fuzzy stabilization scheme via output feedback. Switching multiple-modeling approach has been used to deal with linear systems with time-varying parameters or structure. Conventionally, Takagi-Sugeno (TS) fuzzy modeling method is usually adopted to approximate the nonlinear time-invariant systems, but not suitable for nonlinear time-varying systems. Combining the switching multiple-modeling approach and TS fuzzy modeling method, a robust fuzzy observer-based controller, based on the switching TS fuzzy model, is proposed for the stabilization of the nonlinear time-varying stochastic systems. In order to mitigate the model approximation error and external disturbance in the systems, the proposed H2/Hinf switching fuzzy observer-based control achieves H2 suboptimal control and Hinf attenuation of external disturbance simultaneously. The conditions for the existence of the proposed H2/Hinf switching fuzzy observer-based controller are provided in terms of linear matrix inequalities (LMIs), allowing the use of standard convex optimization procedures to solve the proposed robust H2/Hinf output control design problem for nonlinear time-varying stochastic systems. Finally, numerical simulations are provided to illustrate the design procedure and to confirm the performance of the proposed robust H2/Hinf switching fuzzy observer-based controller for nonlinear time-varying stochastic systems.
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Taflanidis, Alexandros Angelos. "Stochastic System Design and Applications to Stochastically Robust Structural Control." Thesis, 2008. https://thesis.library.caltech.edu/4819/1/Thesis_Taflanidis.pdf.
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The knowledge about a planned system in engineering design applications is never complete. Often, a probabilistic quantification of the uncertainty arising from this missing information is warranted in order to efficiently incorporate our partial knowledge about the system and its environment into their respective models. In this framework, the design objective is typically related to the expected value of a system performance measure, such as reliability or expected life-cycle cost. This system design process is called stochastic system design and the associated design optimization problem stochastic optimization. In this thesis general stochastic system design problems are discussed. Application of this design approach to the specific field of structural control is considered for developing a robust-to-uncertainties nonlinear controller synthesis methodology.
Initially problems that involve relatively simple models are discussed. Analytical approximations, motivated by the simplicity of the models adopted, are discussed for evaluating the system performance and efficiently performing the stochastic optimization. Special focus is given in this setting on the design of control laws for linear structural systems with probabilistic model uncertainty, under stationary stochastic excitation. The analysis then shifts to complex systems, involving nonlinear models with high-dimensional uncertainties. To address this complexity in the model description stochastic simulation is suggested for evaluating the performance objectives. This simulation-based approach addresses adequately all important characteristics of the system but makes the associated design optimization challenging. A novel algorithm, called Stochastic Subset Optimization (SSO), is developed for efficiently exploring the sensitivity of the objective function to the design variables and iteratively identifying a subset of the original design space that has high plausibility of containing the optimal design variables. An efficient two-stage framework for the stochastic optimization is then discussed combining SSO with some other stochastic search algorithm. Topics related to the combination of the two different stages for overall enhanced efficiency of the optimization process are discussed.
Applications to general structural design problems as well as structural control problems are finally considered. The design objectives in these problems are the reliability of the system and the life-cycle cost. For the latter case, instead of approximating the damages from future earthquakes in terms of the reliability of the structure, as typically performed in past research efforts, an accurate methodology is presented for estimating this cost; this methodology uses the nonlinear response of the structure under a given excitation to estimate the damages in a detailed, component level.
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"Robust Stochastic Control and High-Dimensional Statistics with Applications in Finance." 2016. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1292652.
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Chang, Yu-Te, and 昌育德. "Robust Control Design for Nonlinear Stochastic Distributed Parameter Systems: Fuzzy Approach." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/71162235138072661526.
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博士國立清華大學
電機工程學系
98
在本論文中，我們探討非線性隨機分佈參數系統的隨機穩定化問題，和有外部擾動和量測雜訊影響下的非線性隨機分佈參數系統的強健性 $H_\infty$ 穩定化問題。我們更針對外部的擾動和量測雜訊是在空間位置分佈的情況下來探討其穩定化的控制器設計。模糊方法被廣泛的應用於非線性系統的近似。因此，我們利用模糊內插法，提出一個模糊隨機分佈參數系統來近似原本的非線性隨機分佈參數系統。然後使用半離散化的有限差分法，我們發展一個模糊隨機的狀態空間模型，來取代模糊隨機分佈參數系統。模糊隨機的狀態空間模型是被證明可以近似原本的非線性隨機分佈參數系統。因此，基於這個模型，一個強健模糊估測器結合穩定化控制器是被提出來控制非線性隨機分佈參數系統使其穩定。控制器使其系統穩定的條件是被證明只要符合一個矩陣不等式即可被保證。進一步地，強健性 $H_\infty$ 控制設計法則是被提出來消除外部干擾和量測雜訊對系統輸出的影響。因為控制器增益及估測器增益互相偶和的問題，所以設計條件是一個雙線性的矩陣不等式。為了有系統的解決設計的問題，我們簡化BMI的問題成LMI的問題，並使用 LMI 技巧來求解控制器增益和估測器增益。最後，為了呈現設計的性能及方法的實用性，我們給一個神經系統的例子來說明控制器設計的流程，並驗證設計方法的效能。
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Wang, Jung-Sheng, and 王仲盛. "FUZZY-MODEL-BASED ROBUST CONTROL OF STOCHASTIC UNCERTAIN LARGE-SCALE SYSTEMS." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/54213648213477987842.
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碩士大同大學
電機工程學系(所)
94
This thesis deals with the stabilization problem for the fuzzy stochastic uncertain large-scale system in which the system is composed of a number of Takagi-Sugeno fuzzy model subsystems with interconnections. We represent a nonlinear plant with a Takagi-Sugeno fuzzy model that provides an effective method to represent complex nonlinear systems by fuzzy sets and fuzzy reasoning. Based on the Lyapunov stability theorem and the theory of the steady state covariance control, two feasible and effective approaches to the robust control problem of the stochastic uncertain large-scale systems are developed in this thesis. First, according to the robustness property of variable structure control, a fuzzy sliding mode controller with an integral function is designed such that the reference model input and the plant error term disappear on the sliding mode. Next, by assigning a common positive definite covariance matrix, the fuzzy state feedback controller can be developed by solving the corresponding state feedback gains such that the robust stability of the T-S fuzzy stochastic uncertain large-scale systems can be guaranteed. Finally, a numerical example is given to demonstrate the validity of the proposed two controllers in this thesis.
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Kim, Dongwook. "Application of stochastic control and robust stability of singularly perturbed unified systems." Thesis, 2006. http://hdl.handle.net/10057/318.
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The unified approach and singular perturbation theories are two of the most highly recognized and rapidly developed areas in the control field recently. In this thesis, a stochastic optimal controller design using state feedback is examined for singularly perturbed unified systems with Gaussian noise. The difference between the costs of full order systems and those of reduced order systems is just on the order of epsilon. The robust of a singularly perturbed unified stochastic system is investigated by exploring stability bounds under system uncertainties. A new unified stochastic bound is imported for the investigation. Practical application illustrates the validation of the concepts.Thesis (M.S.)--Wichita State University, Dept. of Electrical Engineering.
"May 2006."
Includes bibliographic references (leaves 50-53)
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Lin, Ying-Po, and 林英博. "Scheduled Managing Policy for Nonlinear Stochastic Bio-inspired Control Systems via a Robust Impulsive Tracking Control Strategy." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/q3k92z.
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博士國立清華大學
電機工程學系
102
The dynamics of bio-inspired control systems mainly rely on the trophic-like interactions among the controlled objects and their predator-like or prey-like control agents. The amounts of the bio-inspired control agents like pesticide, fertilizer or drugs are difficult to be arbitrarily specified during the whole control process, and may be fewer and fewer because they are absorbed by controlled objects like pest, crop or pathogen, or decay with time. Therefore, for some managing purpose or model reference tracking, a proper scheduled managing policy by impulsively introducing bio-inspired control agents, such as periodic irrigation/feeding for crop/livestock or scheduled dosing/treatment for patients, will be necessary. The scheduled managing policy should ensure a robust reference tracking performance even under the interference from random intrinsic fluctuation and uncertain environmental disturbance. In this study, we propose a scheduled managing policy for nonlinear stochastic bio-inspired control systems via a robust impulsive tracking control strategy to attenuate the effects of random intrinsic fluctuations and uncertain environmental noises on desired reference tracking and impulsive control input. To simplify the design procedure without solving a complex Hamilton Jacobi integration inequality (HJII), we combine the global linearization approach with numerical approximation to deal with nonlinearity and integration in HJII so that an equivalent LMIs is proposed for solving this robust impulsive tracking control problem efficiently. Simulation results are also given to verify the efficiency of the impulsive tracking control design of nonlinear stochastic bio-inspired control systems.
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Lin, Xue Lin, and 林學麟. "Robust H infinity Adaptive Fuzzy Tracking Control for MIMO Nonlinear Stochastic Poisson Jump Diffusion Systems." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/rq253d.
Full textAbstract:
碩士國立清華大學
電機工程學系
104
Recently, stochastic Poisson jump diffusion system has attracted much attention in stochastic control. Poisson jump process has been used to model the random discontinuous jump behavior of the intrinsic discontinuous perturbation in stochastic system. Wiener process also called diffusion process represents the continuous random fluctuation to the system. In this study, an adaptive control is introduced for multi-input multi-output (MIMO) nonlinear stochastic Poisson jump diffusion system with continuous and discontinuous random fluctuations to achieve the control performance with a prescribed disturbance attenuation level. The system structure is of a strict-feedback form. Based on backstepping design technique and control theory, robust adaptive control law is constructed for MIMO nonlinear stochastic Poisson jump diffusion system to achieve performance with a prescribed attenuation level of external disturbance, fuzzy approximation error and the effect of continuous and discontinuous fluctuations. The proposed adaptive control law combines both merits of control and adaptive control scheme to sufficiently solve the robust adaptive tracking control problem for MIMO nonlinear stochastic system with continuous and discontinuous random fluctuations. In addition, the uniformly positive definite assumption of control coefficient matrix is relaxed for MIMO adaptive control as well. The simulation results are provided to show the effectiveness of the proposed robust adaptive control law.
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Lin, Yann-Horng, and 林彥宏. "Performance Constrained Robust Fuzzy Control for Takagi-Sugeno Fuzzy Model-Based Perturbed Stochastic Nonlinear Systems." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/25xz94.
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碩士國立臺灣海洋大學
輪機工程學系
106
Nowadays, the stability analysis and controller design of stochastic systems have become much more important since the stochastic behaviors are usually exist in practical systems. Because of the reason, a robust fuzzy controller design approach is proposed by covariance control theory, pole placement theory and robust control theory in this thesis. At first, nonlinear systems are expressed as a perturbed Takagi-Sugeno fuzzy model and the so-called parallel distributed compensation method is applied for the fuzzy controller design. Next, considering the stability analysis and the performance of the perturbed Takagi-Sugeno fuzzy model, corresponding Lyapunov stability conditions subject to multiple performance constraints, including state variance constraint, output variance constraint and pole placement constraint are developed. Then, the proposed fuzzy control problem can be effectively transferred into the linear matrix inequality problem and it can be solved by the convex optimal programming algorithm. At last, several nonlinear practical systems are selected to verify the applicability and effectivity of the proposed robust fuzzy controller design method.
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Wang, Chun-Ping, and 王鈞平. "Stochastic Robust Team Tracking Control of Multi-UAV Networked System under Wiener and Poisson Random Fluctuations." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/d4r8aa.
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Wu, Wen-Ben, and 吳文濱. "Robut multi-objective control for the stochastic large-scale system." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/91507421860365961462.
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博士國防大學中正理工學院
國防科學研究所
96
There are two purposes in this dissertation. The first one is to develop a multi-objectives performance state feedback controller for the stochastic large-scale systems (SLSSs). The addressed multi-objectives consist of (1) external disturbance attenuation constraints on the norm level, (2) individual state variance constraints on the upper bound limitation, and (3) pole-placement constraints on the disk region condition. The sufficient condition for satisfying each control objective, basing on the Lyapunov (-Krasovskii) stability theory, can be derived in terms of linear matrix inequalities (LMIs). Then, the multi-objectives performance controller can be constructed by using the convex optimization algorithm (COA) of a set of feasible LMIs. In addition, the discussed various SLSSs include (1) stochastic large-scale nominal systems (SLSNSs), (2) stochastic large-scale uncertain systems (SLSUSs), and (3) stochastic large-scale time-delay systems (SLSTDSs). For achieving the above-mentioned goal, we first investigate the problem of developing the multi-objectives performance suboptimal state feedback controllers for the SLSNS. Three different design methods are used, which are (1) decoupled, (2) centralized, and (3) decentralized approaches, respectively. According to whether the matching condition in the large-scale system exists or not, the above-mentioned three controller design approaches are addressed accordingly. A set of corresponding sufficient conditions of suboptimal state feedback controllers to satisfy the multi-objectives performance can be derived in terms of feasible LMIs for solving each proposed problem. We then focus on the problem of developing a multi-objectives performance robustly decentralized optimal state feedback controller for the SLSUS. The uncertainties are allowed to be unstructured but time-varying and norm-bounded. It is shown that the addressed problem can be solved by a set of sufficient conditions of multi-objectives robustness, which can be derived in terms of feasible LMIs for all admissible uncertainties. Finally, we consider the problem of developing a multi-objectives performance robustly decentralized optimal state feedback controller for the SLSTDS. The considered time-delay parameters appear in the interconnections between individual subsystems. Similarly, a set of sufficient conditions of robustly decentralized optimal state feedback controller for satisfying the multi-objective performance can be derived in terms of feasible LMIs to solve the addressed problem for all time delays. The second goal is to design a robust output feedback controller (RHOFC) for the stochastic large-scale uncertain time-delay system (SLSUTDS). The considered time-delay parameters and uncertainties are described as previously. The sufficient conditions of the desired RHOFC can be derived in terms of feasible LMIs for all admissible uncertainties and time delays. The effectiveness of all of the proposed methods is illustrated by different numerical examples.
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"Scalable Control Strategies and a Customizable Swarm Robotic Platform for Boundary Coverage and Collective Transport Tasks." Doctoral diss., 2017. http://hdl.handle.net/2286/R.I.44017.
Full textAbstract:
abstract: Swarms of low-cost, autonomous robots can potentially be used to collectively perform tasks over large domains and long time scales. The design of decentralized, scalable swarm control strategies will enable the development of robotic systems that can execute such tasks with a high degree of parallelism and redundancy, enabling effective operation even in the presence of unknown environmental factors and individual robot failures. Social insect colonies provide a rich source of inspiration for these types of control approaches, since they can perform complex collective tasks under a range of conditions. To validate swarm robotic control strategies, experimental testbeds with large numbers of robots are required; however, existing low-cost robots are specialized and can lack the necessary sensing, navigation, control, and manipulation capabilities.
To address these challenges, this thesis presents a formal approach to designing biologically-inspired swarm control strategies for spatially-confined coverage and payload transport tasks, as well as a novel low-cost, customizable robotic platform for testing swarm control approaches. Stochastic control strategies are developed that provably allocate a swarm of robots around the boundaries of multiple regions of interest or payloads to be transported. These strategies account for spatially-dependent effects on the robots' physical distribution and are largely robust to environmental variations. In addition, a control approach based on reinforcement learning is presented for collective payload towing that accommodates robots with heterogeneous maximum speeds. For both types of collective transport tasks, rigorous approaches are developed to identify and translate observed group retrieval behaviors in Novomessor cockerelli ants to swarm robotic control strategies. These strategies can replicate features of ant transport and inherit its properties of robustness to different environments and to varying team compositions. The approaches incorporate dynamical models of the swarm that are amenable to analysis and control techniques, and therefore provide theoretical guarantees on the system's performance. Implementation of these strategies on robotic swarms offers a way for biologists to test hypotheses about the individual-level mechanisms that drive collective behaviors. Finally, this thesis describes Pheeno, a new swarm robotic platform with a three degree-of-freedom manipulator arm, and describes its use in validating a variety of swarm control strategies.Dissertation/Thesis
Doctoral Dissertation Mechanical Engineering 2017
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Rens, Gavin B. "A belief-desire-intention architechture with a logic-based planner for agents in stochastic domains." Diss., 2010. http://hdl.handle.net/10500/3517.
Full textAbstract:
This dissertation investigates high-level decision making for agents that are both goal and utility
driven. We develop a partially observable Markov decision process (POMDP) planner which
is an extension of an agent programming language called DTGolog, itself an extension of the
Golog language. Golog is based on a logic for reasoning about action—the situation calculus.
A POMDP planner on its own cannot cope well with dynamically changing environments
and complicated goals. This is exactly a strength of the belief-desire-intention (BDI) model:
BDI theory has been developed to design agents that can select goals intelligently, dynamically
abandon and adopt new goals, and yet commit to intentions for achieving goals. The contribution
of this research is twofold: (1) developing a relational POMDP planner for cognitive
robotics, (2) specifying a preliminary BDI architecture that can deal with stochasticity in action
and perception, by employing the planner.Computing
M. Sc. (Computer Science)