DUAL FUNCTIONALITY ARTIFICIAL NEURAL NETWORK TECHNIQUE FOR WELLS TURBINE BASED WAVE ENERGY CONVERSION SYSTEM

Wave energy is of particular interest amongst new market-penetrating renewable energy resources. Although energy extraction from wave motion is still in its infancy, recent studies predict rapid development. Several studies have investigated various wave energy harvesting configurations. The oscillating water column (OWC) is the most common type. The OWC is based on a Wells turbine system coupled to a doubly fed induction generator (DFIG) grid connected via a back-to-back converter, which is inherently advantageous, enabling this arrangement to dominate the wave energy conversion field. Like the majority of renewable energy sources, wave energy power must be carefully tracked in order to maximize efficiency, due to its non-linear relation with the differential pressure and the turbine speed. Several maximum power point techniques (MPPT) have been presented in the literature that varies in implementation complexity, tracking convergence, and fast tracking methods. Despite several advantages offered by the OWC-based wave energy conversion system (WECS), the commonly installed Wells turbine suffers a critical phenomenon: stalling. During this operating condition, the turbine power is dramatically decreased causing a severe system disturbance, especially when grid integration is required. Various stalling avoidance techniques have been presented in the literature, including air flow control implementation and rotor speed control, in addition to variable-speed strategies. The air flow control-based techniques offer limited performance compared to rotor speed control. This thesis develops a dual functionality enhanced-performance technique that avoids stalling phenomenon and maximizes the extracted power. A grid connected WECS Wells turbine coupled to DFIG, is mathematically modelled within. A robust decoupled active-reactive power control strategy is subsequently presented for grid connection purposes. A novel artificial neural network-based stalling avoidance and maximum power point tracking (MPPT) technique is proposed. The presented technique facilitates a simplified training procedure, adequate stalling avoidance, minimal grid power oscillations, fast convergence and wide turbine speed range operation. Rigorous simulation results using Matlab/Simulink software package, comparing the developed and classical techniques, are utilized to verify the presented technique effectiveness and superiority under operating conditions

Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives

Vertebrates are able to quickly adapt to new environments in a very robust, seemingly effortless way. To explain both this adaptivity and robustness, a very promising perspective in neurosciences is the modular approach to movement generation: Movements results from combinations of a finite set of stable motor primitives organized at the spinal level. In this article we apply this concept of modular generation of movements to the control of robots with a high number of degrees of freedom, an issue that is challenging notably because planning complex, multidimensional trajectories in time-varying environments is a laborious and costly process. We thus propose to decrease the complexity of the planning phase through the use of a combination of discrete and rhythmic motor primitives, leading to the decoupling of the planning phase (i.e. the choice of behavior) and the actual trajectory generation. Such implementation eases the control of, and the switch between, different behaviors by reducing the dimensionality of the high-level commands. Moreover, since the motor primitives are generated by dynamical systems, the trajectories can be smoothly modulated, either by high-level commands to change the current behavior or by sensory feedback information to adapt to environmental constraints. In order to show the generality of our approach, we apply the framework to interactive drumming and infant crawling in a humanoid robot. These experiments illustrate the simplicity of the control architecture in terms of planning, the integration of different types of feedback (vision and contact) and the capacity of autonomously switching between different behaviors (crawling and simple reaching

Inverse Dynamics and Control for Nuclear Power Plants

A new nonlinear control technique was developed by reformulating one of the “inverse Problems” techniques in mathematics, namely the reconstruction problem. The theory identifies an important concept called inverse dynamics which is always a known property for systems already developed or designed. Accordingly, the paradigm is called “reconstructive inverse dynamics” (RID) control. The standard state-space representation of dynamic systems constitutes a sufficient foundation to derive an algebraic RID control law that provides solutions in one step computation. The existence of an inverse solution is guaranteed for a limited dynamic space. Outside the guaranteed range, existence depends on the nature of the system under consideration. Derivations include adaptive features to minimize the effects of modeling errors and measurement degradation on control performance. A comparative study is included to illustrate the relationship between the RID control and optimal control strategies. A set of performance factors were used to investigate the robustness against various uncertainties and the suitability for digital implementation in large scale-systems. All of the illustrations are based on computer simulations using nonlinear models. The simulation results indicate a significant improvement in robust control strategies. The control strategy can be implemented on-line by exploiting its algebraic design property. Three applications to nuclear reactor systems are presented. The objective is to investigate the merit of the RID control technique to improve nuclear reactor operations and increase plant availability. The first two applications include xenon induced power oscillations and feed water control in conventional light water reactors. The third application consists of an automatic control system design for the startup of the Experimental Breeder Reactor-II (EBR-II). The nonlinear dynamic models used in this analysis were previously validated against available plant data. The simulation results show that the RID technique has the potential to improve reactor control strategies significantly. Some of the observations include accurate xenon control, and rapid feed water maneuvers in pressurized water reactors, and successful automated startup of the EBR-II. The scope of the inverse dynamics approach is extended to incorporate artificial intelligence methods within a systematic strategy design procedure. Since the RID control law includes the dynamics of the system, its implementation may influence plant component and measurement design. The inverse dynamics concept is further studied in conjunction with artificial neural networks and expert systems to develop practical control tools

Aerospace Medicine and Biology: A continuing bibliography with indexes

This bibliography lists 417 reports, articles and other documents introduced into the NASA scientific and technical information system in March 1985