Performance Improvement of Cascade Control Systems Using Nonlinear Techniques

• Main Authors: Lan Tien-Hung, 藍天鴻
• Other Authors: Chen Chyi-Tsong
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/92730067594294169700

碩士 === 逢甲大學 === 化學工程學系 === 88 === In this dissertation, we propose the use of an adaptive single neuron controller (SNC) to enhance the system performance of cascade control schemes. In series and parallel cascade control systems, the SNC which acts as a nonlinear filter is implemented after the primary controller. The filter is meant to damp the action of the primary control when it tends to amplify the variation of the manipulated variable. The nonlinear filter, besides having a stabilizing effect on the cascade structure, reduces the tuning difficulties. To facilitate the design of a cascade control system for nonlinear processes, we suggest to implement a modified, SNC as the primary control and a P control as the secondary controller. Based on a learning algorithm, the SNC is able to generate suitable set point for the inner loop adaptively by merely observing the primary output errors. Extensive simulation results show that the proposed nonlinear techniques greatly improves the system performance of either series and parallel cascade control systems, which significantly subdues the loop interaction and attenuate the maximum deviation. Due to its simple structure, efficient algorithm and good performance, we convince that the proposed nonlinear strategy is a promising approach to the performance enhancement of cascade control systems.