In 1992, the Louisiana black bear (Ursus americanus luteolus) was granted threatened status under the Endangered Species Act primarily because of extensive habitat loss and fragmentation. Currently, the Louisiana black bear is restricted to 3 relatively small, disjunct breeding subpopulations located in the Tensas River Basin of northeast Louisiana, the upper Atchafalaya River Basin (ARB) of south-central Louisiana, and coastal Louisiana. The 1995 Recovery Plan mandates research to determine the viability of the remaining subpopulations. I conducted a capture-mark-recapture study during 2007–2009 to estimate population parameters for the ARB bear subpopulation by collecting hair samples (n = 2,977) from 115 barbed-wire hair traps during 8 1-week periods each summer. DNA was extracted from those hair samples and microsatellite genotypes were used to identify individuals. I analyzed encounter histories using the Huggins full heterogeneity estimator in a robust design framework in Program MARK. I compared candidate models incorporating heterogeneity, behavior, and time effects on capture using information-theoretic methods. I directly estimated apparent survival, temporary emigration, probability of capture and recapture, and probability of belonging to 1 of 2 mixtures; population abundance was a derived parameter. Apparent survival was 0.91 (SE = 0.06) and did not vary by gender or year. There was some evidence of temporary emigration for males only (0.10, 95% CI = 0.001–0.900). I modeled capture probabilities with a 2-mixture distribution for both male and females. Overall mean weekly capture probability was 0.12 (SE = 0.03) and 0.25 (SE = 0.04) for males and females, respectively. Recapture rates indicated a positive behavioral response to capture. Model-averaged mean annual abundance was 56 (SE = 4.5, 95% CI = 49–68). I calculated population density using spatially-explicit maximum-likelihood methods; model-averaged density was 0.15 bears/km2 (SE = 0.03). My results updated previous abundance estimates for the ARB bear subpopulation and will be used in a population viability analysis to determine if recovery criteria for the Louisiana black bear have been met.

Modern engineering design tends to use computer simulations such as Finite Element Analysis (FEA) to replace physical experiments when evaluating a quality response, e.g., the stress level in a phone packaging process. The use of computer models has certain advantages over running physical experiments, such as being cost effective, easy to try out different design alternatives, and having greater impact on product design. However, due to the complexity of FEA codes, it could be computationally expensive to calculate the quality response function over a large number of combinations of design and environmental factors. Traditional experimental design and response surface methodology, which were developed for physical experiments with the presence of random errors, are not very effective in dealing with deterministic FEA simulation outputs. In this thesis, we will utilize a spatial statistical method (i.e., Kriging model) for analyzing deterministic computer simulation-based experiments. Subsequently, we will devise a sequential strategy, which allows us to explore the whole response surface in an efficient way. The overall number of computer experiments will be remarkably reduced compared with the traditional response surface methodology. The proposed methodology is illustrated using an electronic packaging example.

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This thesis contains new results on the problem of coordinating a group of vehicles. The main motivation driving this work is the development of control laws that steer individual members of a formation, such that desired group behavior emerges. Special attention is paid to analysis of coordination issues, in particular formation control of marine craft where robustness to unknown environmental forces is important. Coordinated control applications for marine craft include: underway replenishment, maintaining a formation for increased safety during travel and instrument resolution, and cooperative transportation. A review of formation control structures is given, together with a discussion of special issues that arise in coordination of independent vehicles. The main contributions of this thesis may be grouped into two categories: • Path-following designs for controlling a group of vehicles • Multi-body motivated formation modeling and control A previously developed path following design is used to control a group of vehicles by synchronizing the individual path parameters. The path following design is advantageous since the path parameter, i.e., that parameter which determines position along a path, is scalar; hence coordination is achieved with a little amount of real-time communication. The path following design is also extended to the output-feedback case for systems where only parts of the state vector are known. The path following scheme is exploited further in a passivity-based design for coordination where the structural properties render an extended selection of functions for synchronization available. Performance and robustness properties in different operational conditions can be enhanced with a careful selection of these functions. Two designs are presented; a cascaded interconnection where a consensus system provides synchronized path parameters as input to the individual path following systems renders time-varying formations possible and increases robustness to communication problems; a feedback interconnection which is more robust to vehicle failures. Both designs are extended to sampled-data designs where plant and controller dynamics are updated in continuous-time and path parameters are exchanged over a communication network where transmission occurs at discrete intervals. Bias estimation is included to provide integral action against slowlyvarying environmental forces and model uncertainties. A scheme for formation modeling and control, inspired by analytical mechanics of multi-body systems and Lagrangian multipliers, is proposed. In this approach to formation control, various formation behaviors are determined by imposing constraint functions on group members. Several examples illustrate these formation behaviors. The stabilization scheme presented is made more robust with respect to unknown time-varying disturbances. In addition, the scheme is extended towards adaptive estimation of unknown plant and parameters. Furthermore, it can be applied with no major modifications to the case of position control for a single vehicle. The formation control scheme is such that it may be used in combination with a set of position control laws for a single vessel, thus enabling the designer to choose from a large class of control laws available in the literature. The input-to-state stability (ISS) framework is utilised to investigate robustness to environmental and communication disturbances. A loop-transform, together with the ISS framework, yields an upper bound on the inter-vessel time delay below which formation stability is maintained.

This thesis studies a two-way relay network (TWRN), which consists of two single antenna source nodes and a multi-antenna relay node. The source nodes exchange information via the assistance of the relay node in the middle. The relay scheme in this TWRN is amplify-and-forward (AF) based analog network coding (ANC). A robust beamforming matrix optimization algorithm is presented here with the objective to minimize the transmit power at the relay node under given signal to interference and noise ratio (SINR) requirements of source nodes. This problem is first formulated as a non-convex optimization problem, and it is next relaxed to a semi-definite programming (SDP) problem by utilizing the S-procedure and rank-one relaxation. This robust beamforming optimization algorithm is further validated in a MATLAB-based orthogonal frequency-division multiplexing (OFDM) MIMO two-way relay simulation system. To better investigate the performance of this beamforming algorithm in practical systems, synchronization issues such as standard timing offset (STO) and carrier frequency offset (CFO) are considered in simulation. The transmission channel is modeled as a frequency selective fading channel, and the source nodes utilize training symbols to perform minimum mean-square error (MMSE) channel estimation. BER curves under perfect and imperfect synchronization are presented to show the performance of TWRN with ANC. It is shown that the outage probability of robust beamforming algorithm is tightly related to the SINR requirements at the source nodes, and the outage probability increases significantly when the SINR requirements are high.

Wireless communication links with multiple antennas at both the transmitter and the receiver sides, so-called multiple-input-multiple-output (MIMO)systems, are attracting much interest since they can significantly increase the capacity of band-limited wireless channels to meet the requirements of the future high data rate wireless communications. The treatment of channel state information (CSI) is critical in the design of MIMO systems. Accurate CSI at the transmitter is often not possible or may require high feedback rates, especially in multi-user scenarios. Herein, we consider the robust design of linear transceivers with imperfect CSI either at the transmitter or at both sides of the link. The framework considers the design problem where the imperfect CSI consists of a channel mean and an channel covariance matrix or, equivalently, a channel estimate and an estimation error covariance matrix. For single-user systems, the proposed robust transceiver designs are based on a general cost function of the average mean square errors. Under different CSI conditions, our robust designs exhibit a similar structure to the transceiver designs for perfect CSI, but with a different equivalent channel and/or noise covariance matrix. Utilizing majorization theory, the robust linear transceiver design can be readily solved by convex optimization approaches in practice. For multi-user systems, we consider both the communication link from the users to the access point (up-link) as well as the reverse link from the access point to the users (down-link). For the up-link channel, it is possible to optimally design robust linear transceivers minimizing the average sum mean square errors of all the data streams for the users. Our robust linear transceivers are designed either by reformulating the optimization problem as a semidefinite program or by extending the design of a single-user system in an iterative manner. Under certain channel conditions, we show that the up-link design problem can even be solved partly in a distributed fashion. For the down-link channel, a system with one receive antenna per user is considered. A robust system design is obtained by reducing the feedback load from all users to allow only a few selected users to feed back accurate CSI to the access point. We study the properties of four typical user selection algorithms in conjunction with beamforming that guarantee certain signal-to-interference-plus-noise ratio (SINR) requirements under transmit power minimization. Specifically, we show that norm-based user selection is asymptotically optimal in the number of transmitter antennas and close-to-optimal in the number of users. Rooted in the practical significance of this result, a simpler down-link system design with reduced feedback requirements is proposed. / QC 20100922

In this research, a novel machine learning method is designed and applied to Mine Countermeasure Missions. Similarly to some kernel methods, the proposed approach seeks to compute a linear model from another higher dimensional feature space. However, no kernel is used and the feature mapping is explicit. Computation can be done directly in the accessible feature space. In the proposed approach, the feature projection is implemented by constructing a large hidden layer, which differs from traditional belief that Multi-Layer Perceptron is usually funnel-shaped and the hidden layer is used as feature extractor. The proposed approach is a general method that can be applied to various problems. It is able to improve the performance of the neural network based methods and the learning speed of support vector machine. The classification speed of the proposed approach is also faster than that of kernel machines on the mine countermeasure mission task.

The design of optimal strictly positive real (SPR) compensators using numerical optimization is considered. The plants to be controlled are linear and nonlinear flexible manipulators. For the design of SISO and MIMO linear SPR controllers, the optimization objective function is defined by reformulating the H2-optimal control problem subject to the constraint that the controllers must be SPR. Various controller parameterizations using transfer functions/matrices and state-space equations are considered. Depending on the controller form, constraints are enforced (i) using simple inequalities guaranteeing SPRness, (ii) in the frequency domain, or (iii) by implementing the Kalman-Yakubovich- Popov lemma. The design of a gain-scheduled SPR controller using numerical optimization is also considered. Using a family of linear SPR controllers, the time dependent scheduling signals are parameterized, and the objective function of the optimizer seeks to find the form of the scheduling signals which minimizes the manipulator tip tracking error while minimizing the control effort.

Networked control systems (NCSs) are a kind of distributed control systems in which the data between control components are exchanged via communication networks. Because of the attractive advantages of NCSs such as reduced system wiring, low weight, and ease of system diagnosis and maintenance, the research on NCSs has received much attention in recent years. The first part (Chapter 2 - Chapter 4) of the thesis is devoted to designing new controllers for NCSs by incorporating the network-induced delays. The thesis also conducts research on filtering of multirate systems and identification of Hammerstein systems in the second part (Chapter 5 - Chapter 6).<br /><br /> Network-induced delays exist in both sensor-to-controller (S-C) and controller-to-actuator (C-A) links. A novel two-mode-dependent control scheme is proposed, in which the to-be-designed controller depends on both S-C and C-A delays. The resulting closed-loop system is a special jump linear system. Then, the conditions for stochastic stability are obtained in terms of a set of linear matrix inequalities (LMIs) with nonconvex constraints, which can be efficiently solved by a sequential LMI optimization algorithm. Further, the control synthesis problem for the NCSs is considered. The definitions of <em>H₂</em> and <em>H_∞</em> norms for the special system are first proposed. Also, the plant uncertainties are considered in the design. Finally, the robust mixed <em>H₂/H_&infin;</em> control problem is solved under the framework of LMIs. <br /><br /> To compensate for both S-C and C-A delays modeled by Markov chains, the generalized predictive control method is modified to choose certain predicted future control signal as the current control effort on the actuator node, whenever the control signal is delayed. Further, stability criteria in terms of LMIs are provided to check the system stability. The proposed method is also tested on an experimental hydraulic position control system. <br /><br /> Multirate systems exist in many practical applications where different sampling rates co-exist in the same system. The <em>l₂-l_&infin;</em> filtering problem for multirate systems is considered in the thesis. By using the lifting technique, the system is first transformed to a linear time-invariant one, and then the filter design is formulated as an optimization problem which can be solved by using LMI techniques. <br /><br /> Hammerstein model consists of a static nonlinear block followed in series by a linear dynamic system, which can find many applications in different areas. New switching sequences to handle the two-segment nonlinearities are proposed in this thesis. This leads to less parameters to be estimated and thus reduces the computational cost. Further, a stochastic gradient algorithm based on the idea of replacing the unmeasurable terms with their estimates is developed to identify the Hammerstein model with two-segment nonlinearities. <br /><br /> Finally, several open problems are listed as the future research directions.

This dissertation discusses an implementation of a design, control and motion planning for a novel extendable modular redundant robotic manipulator in space constraints, which robots may encounter for completing required tasks in small and constrained environment. The design intent is to facilitate the movement of the proposed robotic manipulator in constrained environments, such as rubble piles. The proposed robotic manipulator with multi Degree of Freedom (m-DOF) links is capable of elongating by 25% of its nominal length. In this context, a design optimization problem with multiple objectives is also considered. In order to identify the benefits of the proposed design strategy, the reachable workspace of the proposed manipulator is compared with that of the Jet Propulsion Laboratory (JPL) serpentine robot. The simulation results show that the proposed manipulator has a relatively efficient reachable workspace, needed in constrained environments. The singularity and manipulability of the designed manipulator are investigated. In this study, we investigate the number of links that produces the optimal design architecture of the proposed robotic manipulator. The total number of links decided by a design optimization can be useful distinction in practice. Also, we have considered a novel robust bio-inspired Sliding Mode Control (SMC) to achieve favorable tracking performance for a class of robotic manipulators with uncertainties. To eliminate the chattering problem of the conventional sliding mode control, we apply the Brain Emotional Learning Based Intelligent Control (BELBIC) to adaptively adjust the control input law in sliding mode control. The on-line computed parameters achieve favorable system robustness in process of parameter uncertainties and external disturbances. The simulation results demonstrate that our control strategy is effective in tracking high speed trajectories with less chattering, as compared to the conventional sliding mode control. The learning process of BLS is shown to enhance the performance of a new robust controller. Lastly, we consider the potential field methodology to generate a desired trajectory in small and constrained environments. Also, Obstacle Collision Avoidance (OCA) is applied to obtain an inverse kinematic solution of a redundant robotic manipulator.