Robust periodic disturbance compensation via multirate control

Master'sMASTER OF ENGINEERIN

RealTime Implementation Of An Internal-Model-Principle Signal Identifier

This thesis presents a new means approach of tuning an adaptive internal model principle based signal identification algorithm whose computational costs are low enough to allow a realtime implementation. The algorithm allows an instantaneous Fourier decomposition of nonstationary signals that have a strongly predictable component. The algorithm is implemented as a feedback loop resulting in a closed loop system with a frequency response of a bandpass filter with notches at the frequencies of the Fourier decomposition. This is achieved through real time selection of the coefficients of the transfer functions in the feedback loop. Previous work showed how the dynamics of the algorithm could be chosen to be represented by a bandpass filter with notches. However this involved solving a large set of coupled linear equations. This thesis shows how the equations can be decoupled into pairs of linear equations which have explicit solutions. In other word, rules for explicitly solving for these parameters are given that only involve evaluating frequency responses at the frequencies of the instantaneous Fourier decomposition. Last but not the least, alternative approach for choosing suitable coefficients to eliminate the DC component of the signal under consideration has been proposed as well by replacing a frequency response of a bandpass filter with lowpass filter and adding a model of the constant signal to the feedback loop

Neuro-Controller Design by Using the Multifeedback Layer Neural Network and the Particle Swarm Optimization

In the present study, a novel neuro-controller is suggested for hard disk drive (HDD) systems in addition to nonlinear dynamic systems using the Multifeedback-Layer Neural Network (MFLNN) proposed in recent years. In neuro-controller design problems, since the derivative based train methods such as the back-propagation and Levenberg-Marquart (LM) methods necessitate the reference values of the neural network’s output or Jacobian of the dynamic system for the duration of the train, the connection weights of the MFLNN employed in the present work are updated using the Particle Swarm Optimization (PSO) algorithm that does not need such information. The PSO method is improved by some alterations to augment the performance of the standard PSO. First of all, this MFLNN-PSO controller is applied to different nonlinear dynamical systems. Afterwards, the proposed method is applied to a HDD as a real system. Simulation results demonstrate the effectiveness of the proposed controller on the control of dynamic and HDD systems

Experimental Approaches to the Composition of Interactive Video Game Music

This project explores experimental approaches and strategies to the composition of interactive music for the medium of video games. Whilst music in video games has not enjoyed the technological progress that other aspects of the software have received, budgets expand and incomes from releases grow. Music is now arguably less interactive than it was in the 1990’s, and whilst graphics occupy large amounts of resources and development time, audio does not garner the same attention. This portfolio develops strategies and audio engines, creating music using the techniques of aleatoric composition, real-time remixing of existing work, and generative synthesisers. The project created music for three ‘open-form’ games : an example of the racing genre (Kart Racing Pro); an arena-based first-person shooter (Counter-Strike : Source); and a real-time strategy title (0 A.D.). These games represent a cross-section of ‘sandbox’- type games on the market, as well as all being examples of games with open-ended or open-source code