Soft Computing Methods for Control and Instrumentation

The development of soft computing methods has attracted considerable research interest over the past decade. They are applied to important fields such as control, signal processing, and system modeling. Although soft computing methods have shown great potential in these areas, they share some common shortcomings that hinder them from being used more widely. For example, neural networks, a component of soft computing, often suffer from a slow learning rate. This drawback renders neural networks less than suitable for time critical applications. Therefore, the objective of this thesis is to explore and investigate the soft computing theory so that new and enhanced methods can be put forward. The applications of soft computing in control and instrumentation are also studied to solve demanding real-world problems. In this work, the existing soft computing techniques have been enhanced, and applied to control and instrumentation areas. First, new soft computing methods are proposed. A Modified Elman Neural Network (MENN) is introduced to provide fast convergence speed. Based on Muller’s method, we propose a new reinforcement learning method, which can converge faster than the original algorithm. As a fusion of fuzzy logic and neural networks, a new fuzzy filter using the selforganizing map to fine tune the membership functions is studied. The new soft computing schemes presented in this thesis improve the performance of those earlier methods. Second, we study the MENN-based identification and control problems. A dynamical system identification scheme as well as a trajectory tracking configuration using the MENNs are discussed, respectively. Our MENN-based identification structure belongs to the ‘black box’ identification catalogue. It has the advantageous feature of not knowing the exact order of the system. The inverted pendulum is utilized here as a testbed for the MENN-based trajectory control scheme. It is shown that neural networks are very efficient in dealing with nonlinear system identification and control. In addition, they need little prior information of the plant to be identified or controlled. However, the existence of local minima, under-fitting, and over-fitting may reduce the identification and control accuracy. Third, the applications of soft computing methods in velocity and acceleration acquisition in motion control systems are discussed. The aforementioned fuzzy filter is applied to filter out the velocity noise in the feedback loop without introducing any harmful delay. This could lead to a better servo control performance. Moreover, we construct a neural network-based acceleration acquisition scheme to obtain clean and delayless acceleration signals. Our method has the advantage