O problema do desenvolvimento do \"Software\" para a pilotagem automática de embarcações é tratado através da aplicação de conceitos de controle estocástico. O movimento da embarcação é descrito de forma aproximada através do modelo clássico das derivadas hidrodinâmicas. A partir do Princípio da Separação desenvolve-se um procedimento sequencial em que os problemas de estimação do estado e de controle são tratados concomitantemente com as identificação de efeitos não modelados da dinâmica adotada para o piloto automático. A estimação de estados realiza-se através do filtro estendido de Kalman, que opera sobre informações de posição e velocidade da embarcação. O controlador, de natureza sequencial, atua sobre a embarcação em tempo discreto, sendo o leme o único elemento de controle. O piloto automático proposto foi testado através de simulação digital e alguns resultados obtidos são apresentados e discutidos. / The software development problem for the ship automatic steering is considered through the application of stochastic control concepts. Ship motion is described in an approximate way by the classical hydrodynamic derivatives model. A sequential procedure based on the Separation Principle is developed, being state estimation and control problems handled simultaneously with the identification of unmodeled effects of automatic pilot ship dynamics. State estimates are provided by the extended Kalman filter by using ship position and velocity measurements. The rudder is the only control element for the sequential, discrete-time controller. Digital simulation is employed for testing of the proposed automatic pilot, and some results are presented and discussed.

During the work of this research project we were interested in mathematical techniques that give us an insight to the following questions: How do we understand the trading decisions made by a manager of a hedge fund and what influences these decisions? In what way does an illiquid market affect these decisions and the performance of the fund? And how does the payment scheme affect the investor's decisions? Based on existing work on hedge fund management, we start with a fund that can be modelled with one risky investment and one riskless investment. Next, subject to the hedge fund special reward scheme we maximise the expected utility of wealth of the manager, by controlling the percentage invested in the risky investment, namely the portfolio. We use stochastic control techniques to derive a partial differential equation (PDE) and numerically obtain its corresponding viscosity solution, which provides a weak notion of solutions to these PDEs. This is then taken to a liquidity constrained scenario, to compare the behaviour of the two scenarios. Using the same approach as before we notice that due to the liquidity restriction we cannot use a simple model to combine the risky and riskless investments as a total amount, and hence the PDE is one order higher than before. We then model an investor who is investing in the hedge fund subject to the manager's optimal portfolio decisions, with similar mathematical tools as before. Comparisons between the investor's expected utility of wealth and the utility of having the money invested in the risk-free investment suggests that, in some cases, the investor is paying more to the manager than the return he is receiving for having invested in the hedge fund, compared to a risk-free investment. For that reason we propose a strategic game where the manager's action is to allocate the money between the two assets and the investor's action is to add money to the fund when he expects profit. The result is that the investor profits from the option to reinvest in the fund, although in some extreme cases the actions of the manager make the investor receive a negative value for having the option.

不同評估績效的長短顯著地影響基金的經營策略，相較於強調穩健經營的退休基金而言，此因素是否亦影響退休基金的運作，本研究嘗試應用隨機控制理論，將投資績效的時間因素納入決策考量，以隨機微分方程式描述退休基金資產和應計負債的動態隨機行為，以多期基金規劃的觀點，探討時間因素與最適策略之關連性。本研究應用Brennan、Schwartz與Lagnado（1997）的結果至負債導向的退休基金管理，建構多期資產負債管理模型，退休基金持有資產將分類為風險性的股票投資組合、長期債券和短期票券，並考量投資標的短期利率與長期利率之隨機性質，將基金提撥與資產配置視為可調節因子，給定風險評估測度，於不設定投資限制下計算各期最適投資比例及基金提撥；本研究並以私人退休金個案進行模擬分析，結果顯示此基金未來10年之最適提撥率介於4.2﹪與5.1﹪，就不同評估期限而言，5年評估期之提撥率於初期高於10年評估期，基金比率η=0.75之提撥率低於η=1；5年評估期之基金交易行為較10年期明顯劇烈，基金比率較低時，其交易變化程度較小，不同評估年限與基金比率將同時影響退休基金之最適提撥與投資策略。 / Distinct time horizons in measuring investment perfomance significantly influence the financial planning for the money managers. In this study, we explore this issue concerning the pension fund management that has focused on the asset and liability management to meet its future obligations. A stochastic control model is formulated in a continuous-time framework to obtain the closed form solution for optimal strategy. The time variation in expected returns introduced in Brennan, Schwartz and Lagnado（1997）is adopted in obtaining the optimal strategy using plausible future plan’s normal costs and accrued liabilities under distinct time horizons. Based on the proposed performance measurement, the optimal funding schedule and portfolio selections are determined dynamically without trading restrictions. A private pension scheme is selected and analyzed for numerical illustration. It shows that the optimal contribution rates are between 4.2﹪and 5.1﹪for this specific case. Comparing the funding schedules for distinct time horizons, we find that the contribution rates under 5-year period are higher than those under 10-year period in the beginning. The contribution rates given funding ratio at 75﹪are lower than those given at 100﹪. While the optimal trading behaviors of the pension fund managers for 5-year period are significant volatile than those for 10-year period. Their optimal trading behaviors have exhibited a reduced volatility under the lower funding ratios. The case study indicates that the distinct time horizon and the funding ratio play crucial roles in decision-making process for pension fund management.

Networked embedded control systems are present almost everywhere. A recent trendis to introduce radio communication in these systems to increase mobility and flex-ibility. Network nodes, such as the sensors, are often simple devices with limitedcomputing and transmission power and low storage capacity, so an important prob-lem concerns how to optimize the use of resources to provide sustained overall sys-tem performance. The approach to this problem taken in the thesis is to analyzeand design the communication and control application layers in an integrated man-ner. We focus in particular on cross-layer design techniques for closed-loop controlover non-ideal communication channels, motivated by future control systems withvery low-rate and highly quantized sensor communication over noisy links. Severalfundamental problems in the design of source–channel coding and optimal controlfor these systems are discussed.The thesis consists of three parts. The first and main part is devoted to the jointdesign of the coding and control for linear plants, whose state feedback is trans-mitted over a finite-rate noisy channel. The system performance is measured by afinite-horizon linear quadratic cost. We discuss equivalence and separation proper-ties of the system, and conclude that although certainty equivalence does not holdin general it can still be utilized, under certain conditions, to simplify the overalldesign by separating the estimation and the control problems. An iterative opti-mization algorithm for training the encoder–controller pairs, taking channel errorsinto account in the quantizer design, is proposed. Monte Carlo simulations demon-strate promising improvements in performance compared to traditional approaches.In the second part of the thesis, we study the rate allocation problem for statefeedback control of a linear plant over a noisy channel. Optimizing a time-varyingcommunication rate, subject to a maximum average-rate constraint, can be viewedas a method to overcome the limited bandwidth and energy resources and to achievebetter overall performance. The basic idea is to allow the sensor and the controllerto communicate with a higher data rate when it is required. One general obstacle ofoptimal rate allocation is that it often leads to a non-convex and non-linear problem.We deal with this challenge by using high-rate theory and Lagrange duality. It isshown that the proposed method gives a good performance compared to some otherrate allocation schemes.In the third part, encoder–controller design for Gaussian channels is addressed.Optimizing for the Gaussian channel increases the controller complexity substan-tially because the channel output alphabet is now infinite. We show that an efficientcontroller can be implemented using Hadamard techniques. Thereafter, we proposea practical controller that makes use of both soft and hard channel outputs. / QC 20100623

The presence of vibration is an important problem in many engineering applications. Various passive techniques have traditionally been used in order to reduce waves and vibrations, and their harmful effects. Passive techniques are, however, difficult to apply in the low frequency region. In addition, the use of passive techniques often involve adding mass to the system, which is undesirable in many applications. As an alternative, active techniques can be used to manipulate system dynamics and to control the propagation of waves and vibrations. This thesis deals with modeling, estimation and active control of systems that have resonant dynamics. The systems are exposed to stochastic disturbances. Some of them excite the system and generate vibrational responses and other corrupt measured signals. Feedback control of a beam with attached piezoelectrical elements is studied. A detailed modeling approach is described and system identification techniques are employed for model order reduction. Disturbance attenuation of a non-measured variable shows to be difficult. This issue is further analyzed and the problems are shown to depend on fundamental design limitations. Feedforward control of traveling waves is also considered. A device with properties analogous to those of an electrical diode is introduced. An `ideal´ feedforward controller based on the mechanical properties of the system is derived. It has, however, poor noise rejection properties and it therefore needs to be modified. A number of feedforward controllers that treat the measurement noise in a statistically sound way are derived. Separation of overlapping traveling waves is another topic under investigation. This operation also is sensitive to measurement noise. The problem is thoroughly analyzed and Kalman filtering techniques are employed to derive wave estimators with high statistical performance. Finally, a nonlinear regression problem with close connections to unbalance estimation of rotating machinery is treated. Different estimation techniques are derived and analyzed with respect to their statistical accuracy. The estimators are evaluated using the example of separator balancing.

Many optimal stochastic control problems in finance can be formulated in the form of Hamilton-Jacobi-Bellman (HJB) partial differential equations (PDEs). In this thesis, a general framework for solutions of HJB PDEs in finance is developed, with application to asset allocation. The numerical scheme has the following properties: it is unconditionally stable; convergence to the viscosity solution is guaranteed; there are no restrictions on the underlying stochastic process; it can be easily extended to include features as needed such as uncertain volatility and transaction costs; and central differencing is used as much as possible so that use of a locally second order method is maximized. In this thesis, continuous time mean variance type strategies for dynamic asset allocation problems are studied. Three mean variance type strategies: pre-commitment mean variance, time-consistent mean variance, and mean quadratic variation, are investigated. The numerical method can handle various constraints on the control policy. The following cases are studied: allowing bankruptcy (unconstrained case), no bankruptcy, and bounded control. In some special cases where analytic solutions are available, the numerical results agree with the analytic solutions. These three mean variance type strategies are compared. For the allowing bankruptcy case, analytic solutions exist for all strategies. However, when additional constraints are applied to the control policy, analytic solutions do not exist for all strategies. After realistic constraints are applied, the efficient frontiers for all three strategies are very similar. However, the investment policies are quite different. These results show that, in deciding which objective function is appropriate for a given economic problem, it is not sufficient to simply examine the efficient frontiers. Instead, the actual investment policies need to be studied in order to determine if a particular strategy is applicable to specific investment problem.

Many optimal stochastic control problems in finance can be formulated in the form of Hamilton-Jacobi-Bellman (HJB) partial differential equations (PDEs). In this thesis, a general framework for solutions of HJB PDEs in finance is developed, with application to asset allocation. The numerical scheme has the following properties: it is unconditionally stable; convergence to the viscosity solution is guaranteed; there are no restrictions on the underlying stochastic process; it can be easily extended to include features as needed such as uncertain volatility and transaction costs; and central differencing is used as much as possible so that use of a locally second order method is maximized. In this thesis, continuous time mean variance type strategies for dynamic asset allocation problems are studied. Three mean variance type strategies: pre-commitment mean variance, time-consistent mean variance, and mean quadratic variation, are investigated. The numerical method can handle various constraints on the control policy. The following cases are studied: allowing bankruptcy (unconstrained case), no bankruptcy, and bounded control. In some special cases where analytic solutions are available, the numerical results agree with the analytic solutions. These three mean variance type strategies are compared. For the allowing bankruptcy case, analytic solutions exist for all strategies. However, when additional constraints are applied to the control policy, analytic solutions do not exist for all strategies. After realistic constraints are applied, the efficient frontiers for all three strategies are very similar. However, the investment policies are quite different. These results show that, in deciding which objective function is appropriate for a given economic problem, it is not sufficient to simply examine the efficient frontiers. Instead, the actual investment policies need to be studied in order to determine if a particular strategy is applicable to specific investment problem.

Guaranteed Minimum Withdrawal Benefits(GMWB) have become popular riders on variable annuities. The pricing of a GMWB contract was originally formulated as a singular stochastic control problem which results in a Hamilton Jacobi Bellman (HJB) Variational Inequality (VI). A penalty method method can then be used to solve the HJB VI. We present a rigorous proof of convergence of the penalty method to the viscosity solution of the HJB VI assuming the underlying asset follows a Geometric Brownian Motion. A direct control method is an alternative formulation for the HJB VI. We also extend the HJB VI to the case of where the underlying asset follows a Poisson jump diffusion. The HJB VI is normally solved numerically by an implicit method, which gives rise to highly nonlinear discretized algebraic equations. The classic policy iteration approach works well for the Geometric Brownian Motion case. However it is not efficient in some circumstances such as when the underlying asset follows a Poisson jump diffusion process. We develop a combined fixed point policy iteration scheme which significantly increases the efficiency of solving the discretized equations. Sufficient conditions to ensure the convergence of the combined fixed point policy iteration scheme are derived both for the penalty method and direct control method. The GMWB formulated as a singular control problem has a special structure which results in a block matrix fixed point policy iteration converging about one order of magnitude faster than a full matrix fixed point policy iteration. Sufficient conditions for convergence of the block matrix fixed point policy iteration are derived. Estimates for bounds on the penalty parameter (penalty method) and scaling parameter (direct control method) are obtained so that convergence of the iteration can be expected in the presence of round-off error.

<p>Networked embedded control systems are present almost everywhere. A recent trend is to introduce wireless sensor networks in these systems, to take advantage of the added mobility and flexibility offered by wireless solutions. In such networks, the sensor observations are typically quantized and transmitted over noisy links. Concerning the problem of closed-loop control over such non-ideal communication channels, relatively few works have appeared so far. This thesis contributes to this field, by studying some fundamentally important problems in the design of joint source--channel coding and optimal control.</p><p>The main part of the thesis is devoted to joint design of the coding and control for scalar linear plants, whose state feedbacks are transmitted over binary symmetric channels. The performance is measured by a finite-horizon linear quadratic cost function. The certainty equivalence property of the studied systems is utilized, since it simplifies the overall design by separating the estimation and the control problems. An iterative optimization algorithm for training the encoder--decoder pairs, taking channel errors into account in the quantizer design, is proposed. Monte Carlo simulations demonstrate promising improvements in performance compared to traditional approaches.</p><p>Event-triggered control strategies are a promising solution to the problem of efficient utilization of communication resources. The basic idea is to let each control loop communicate only when necessary. Event-triggered and quantized control are combined for plants affected by rarely occurring disturbances. Numerical experiments show that it is possible to achieve good control performance with limited control actuation and sensor communication.</p>

Stochastic control problems that arise in sequential decision making applications typically assume that information used for decision-making is obtained according to a predetermined sampling schedule. In many real applications however, there is a high sampling cost associated with collecting such data. It is therefore of equal importance to determine when information should be collected as it is to decide how this information should be utilized for optimal decision-making. This type of joint optimization has been a long-standing problem in the operations research literature, and very few results regarding the structure of the optimal sampling and control policy have been published. In this thesis, the joint optimization of sampling and control is studied in the context of maintenance optimization. New theoretical results characterizing the structure of the optimal policy are established, which have practical interpretation and give new insight into the value of condition-based maintenance programs in life-cycle asset management. Applications in other areas such as healthcare decision-making and statistical process control are discussed. Statistical parameter estimation results are also developed with illustrative real-world numerical examples.