Design and analysis of hull configurations for a low-cost, autonomous underwater robot as an enabling technology for system of system applications

The objective of this research is to model and analyze candidate hull configurations for a low-cost, modular, autonomous underwater robot. As the computational power and speed of microprocessors continue to progress, we are seeing a growth in the research, development, and the utilization of underwater robots. The number of applications is broadening in the R&D and science communities, especially in the area of multiple, collaborative robots. These underwater collaborative robots represent an instantiation of a System of Systems (SoS). While each new researcher explores a unique application, control method, etc. a new underwater robot vehicle is designed, developed, and deployed. This sometimes leads to one-off designs that are costly. One limit to the wide-scale utilization of underwater robotics is the cost of development. Another limit is the ability to modify the configuration for new applications and evolving requirements. Consequently, we are exploring autonomous underwater vehicle (AUV) hull designs towards the goal of modularity, vehicle dexterity, and minimizing the cost. In our analysis, we have employed 3D solid modeling tools and finite element methods. In this paper we present our initial results and discuss ongoing work.

Common structural basis for central nervous system drug design

The main theme of this thesis is that there is a common structural basis for drugs acting on the central nervous system (CNS), and that this concept may be used to design new CNS-active drugs which have greater specificity and hence less side-effects. To develop these ideas, the biological basis of how drugs modify CMS neurotransmission is described, and illustrated using dopaminergic pathways. An account is then given of the use of physicochemical concepts in contemporary drug design. The complete conformational analysis of several antipsychotic drugs is used to illustrate some of these techniques in the development of a model for antipsychotic drug action. After reviewing current structure-activity studies in several classes of CNS drugs (antipsychotics, anti-depressants, stimulants, hal1ucinogens, anticonvulsants and analgesics), a hypothesis for a common structural basis of CNS drug action is proposed- This is based on a topographical comparison of the X-ray structures of eight representative CNS-active drugs, and consists of three parts: 1.there is a common structural basis for the activity of many different CNS-active drug classes; 2. an aromatic ring and a nitrogen atom are the primary binding groups whose topographical arrangement is fundamental to the activity of these drug classes; 3. the nature and placement of secondary binding determines different classes of CNS drug activity. A four-Point model for this common structural basis is then defined using 14- CNS-active drug structures that include the original eight used in proposing the hypothesis. The coordinates of this model are: R1 (0. 3.5, 0), R2 (0, -3.5, O), N (4.8. -0.3, 1.4), and R3 (6.3, 1.3, 0), where R1 and R2 represent the point locations of a hydrophobic interaction of the common aromatic ring with a receptor, and R3 locates the receptor point for a hydrogen bond involving the common nitrogen, N. Extended structures were used to define the receptor points R1, R2 and R3, and the complete conformational space of each of the 14 molecules was considered. It is then shoun that the model may be used to predict whether a given structure is likely to show CNS activity: a search over 1,000 entries in the current Merck Index shows a high probability (82%) of CNS activity in compounds fitting the structural model. Analysis of CNS neurotransmitters and neuropeptides shows that these fit the common model well. Based on the available evidence supporting chemical evolution, protein evolution, and the evolution of neurotransmitter functions, it is surmised that the aromatic ring/nitrogen atom pharmacophore proposed in the common model supports the idea of the evolution of CNS receptors and their neurotransmitters, possibly from an aromatic amine or acety1cho1ine acting as a primaeval communicating molecule. The third point in the hypothesis trilogy is then addressed. The extensive conformation-activity analyses that have resulted in well-defined models for five separate CNS drug classes are used to map out the locations of secondary binding groups relative to the common model for anti-psychotics, antidepressants, analgesics, anticholinergics, and anticonvulsants. With this information, and knowledge derived from receptor-binding data, it is postulated that drugs having specified activity could be designed. In order to generate novel structures having a high probability of CNS-activity, a process of drug design is described in which known CNS structures are superimposed topographically using the common model as a template. Atoms regarded as superfluous may be selectively deleted and the required secondary binding groups added in predicted locations to give novel structures. It is concluded that this process provides the basis for the rational design of new lead compounds which could further be optimized for potent and specific CNS activity.

Terminal sliding mode control strategy design for second-order nonlinear system

This study mainly focuses on the terminal sliding mode control (TSMC) strategy design, including an adaptive terminal sliding mode control (ATSMC) and an exact-estimator-based terminal sliding mode control (ETSMC) for second-order nonlinear dynamical systems. In the ATSMC system, an adaptive bound estimation for the lump uncertainty is proposed to ensure the system stability. On the other hand, an exact estimator is designed for exact estimating system uncertainties to solve the trouble of chattering phenomena caused by a sign function in ATSMC law in despite of the utilization of a fixed value or an adaptive tuning algorithm for the lumped uncertainty bound. The effectiveness of the proposed control schemes can be verified in numerical simulations.

A system decomposition approach to the design of functional observers

This paper reports a system decomposition that allows the construction of a minimum-order functional observer using a state observer design approach. The system decomposition translates the functional observer design problem to that of a state observer for a smaller decomposed subsystem. Functional observability indices are introduced, and a closed-form expression for the minimum order required for a functional observer is derived in terms of those functional observability indices. © 2014 Taylor & Francis.

Design of a virtual reality training system for micro-robotic cell injection

This paper discusses the design of a virtual reality (VR) training system for micro-robotic cell injection. A brief explanation of cell injection and the challenges associated with the procedure are first presented. This is followed by discussion of the skills required by the bio-operator to achieve successful injection, such as accuracy, trajectory and applied force. The design of the VR system which includes the visual display, input controllers, mapping strategies, haptic guidance and output data is then discussed. Initial evaluation of the VR system is presented including analysis and discussion based on conducted user evaluations. Finally, given the findings of the initial evaluation, this paper concludes that an effective haptically-enabled virtual cell injection training system is feasible, and recommendations for improvement and future work are given.

Robust adaptive backstepping excitation controller design for simple power system models with external disturbances

This paper presents a nonlinear robust adaptive excitation controller design for a simple power system model where a synchronous generator is connected to an infinite bus. The proposed controller is designed to obtain the adaption laws for estimating critical parameters of synchronous generators which are considered as unknown while providing the robustness against the bounded external disturbances. The convergence of different physical quantities of a single machine infinite bus (SMIB) system, with the proposed control scheme, is ensured through the negative definiteness of the derivative of Lyapunov functions. The effects of external disturbances are considered during formulation of Lyapunov function and thus, the proposed excitation controller can ensure the stability of the SMIB system under the variation of critical parameters as well as external disturbances including noises. Finally, the performance of the proposed scheme is investigated with the inclusion of external disturbances in the SMIB system and its superiority is demonstrated through the comparison with an existing robust adaptive excitation controller. Simulation results show that the proposed scheme provides faster responses of physical quantities than the existing controller.

A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting. ELM has become a popular learning algorithm for single hidden layer feed-forward neural networks (SLFN). From the functional equivalence between the SLFN and fuzzy inference system, a hybrid of fuzzy-ELM has gained attention of the researchers. This paper extends the concept of fuzzy-ELM to an IT2FLS based on ELM (IT2FELM). In the proposed design the antecedent membership function parameters of the IT2FLS are generated randomly, whereas the consequent part parameters are determined analytically by the Moore-Penrose pseudo inverse. The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. Effectiveness of the proposed design of IT2FLS is demonstrated with the application of forecasting nonlinear and chaotic data sets. Nonlinear data of electricity load from the Australian National Electricity Market for the Victoria region and from the Ontario Electricity Market are considered here. The proposed model is also applied to forecast Mackey-glass chaotic time series data. Comparative analysis of the proposed model is conducted with some traditional models such as neural networks (NN) and adaptive neuro fuzzy inference system (ANFIS). In order to verify the structure of the proposed design of IT2FLS an alternate design of IT2FLS based on Kalman filter (KF) is also utilized for the comparison purposes.

Design of a control system using linear matrix inequalities for the active vibration control of a plate

The study of algorithms for active vibration control in smart structures is an area of interest, mainly due to the demand for better performance of mechanical systems, such as aircraft and aerospace structures. Smart structures, formed using actuators and sensors, can improve the dynamic performance with the application of several kinds of controllers. This article describes the application of a technique based on linear matrix inequalities (LMI) to design an active control system. The positioning of the actuators, the design of a robust state feedback controller and the design of an observer are all achieved using LMI. The following are considered in the controller design: limited actuator input, bounded output (energy) and robustness to parametric uncertainties. Active vibration control of a flat plate is chosen as an application example. The model is identified using experimental data by an eigensystem realization algorithm (ERA) and the placement of the two piezoelectric actuators and single sensor is determined using a finite element model (FEM) and an optimization procedure. A robust controller for active damping is designed using an LMI framework, and a reduced model with observation and control spillover effects is implemented using a computer. The simulation results demonstrate the efficacy of the approach, and show that the control system increases the damping in some of the modes.

Control Design Applied to a Micro Electro Mechanical System: MEMS Comb drive

This paper presents the linear optimal control technique for reducing the chaotic movement of the micro-electro-mechanical Comb Drive system to a small periodic orbit. We analyze the non-linear dynamics in a micro-electro-mechanical Comb Drive and demonstrated that this model has a chaotic behavior. Chaos control problems consist of attempts to stabilize a chaotic system to an equilibrium point, a periodic orbit, or more general, about a given reference trajectory. This technique is applied in analyzes the nonlinear dynamics in an MEMS Comb drive. The simulation results show the identification by linear optimal control is very effective.

Marginal and Internal Discrepancies Related to Margin Design of Ceramic Crowns Fabricated by a CAD/CAM System

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)