Design and analysis of a fuzzy controller for a servo system

This paper presents design and simulation investigation of a fuzzy controller and a conventional PID controller for a servo system. First, a servo system is considered and its stability is discussed. Then, a PID controller that is tuned by the Ziegler-Nichols method is formulated for controlling the servo system. To improve the servo system's dynamic response parameters, a fuzzy controller is then proposed for controlling the system. A performance comparison between the fuzzy and the PID controllers are carried out. The results are presented and discussed.

Nonlinear identification of pneumatic servo-drive

Three nonlinear approaches to model the nonlinear pneumatic servo- drive are presented. The three nonlinear approaches are: (1) the multi input-single output (MISO) approach, which describes the single input-single output (SISO) nonlinear plant using a MISO linear representation which allows replacement of the nonlinear analysis by a linear one without approximation, and is studied in both time and frequency domains; (2) piecewise linearization, which systematically replaces, using artificial neural network, the nonlinear surface representing the plant in the hyper input-output space by a number of linear planes that are continuous over the boundaries between them; and (3) Adaptive Neuro-Fuzzy Inference System (ANFIS), in which the fuzzy rules are placed in a neural network structure, and which consequently utilizes neural networks learning rules to systematically tune the nonlinear fuzzy model. The superiority of these nonlinear models over the best model that can be developed using linear identification techniques is shown.