This course covers a number of traditional nonlinear analysis techniques, such as phase plane and describing function. Lyapunov stability analysis is also covered, along with a number of modern nonlinear control design techniques, such as feedback linearisation and sliding mode control. This course covers the area of stochastic modelling and analysis. In particular, attention is given to the use of the DFT in the analysis of noisy daya and the incorporaion of constraints into optimum signal processing techniques. The propagation of errors in input signals and internal system parameters through linear and nonlinear systems is also examined.
Analysis includes convergence and stability properties of the algorithms.
In the DSP context, a number of structures are considered, such as prediction, noise cancellation and beamforming, while the control section considers adaptive PID and pole-placement forms. This course examines the fundamentals of robust control theory and its engineering motivation. Several advanced techniques for analysing and designing control systems are investigated. This course examines a number of ANN architectures and presents some techniques for training them. Several application areas, such as time series modelling and pattern recognition are considered and an attempt is made to create an awareness of the possible pitfalls of ANN use.