Kuo-Chi Chang

By Kuo-Chi Chang, Kai-Chun Chu, Yuh-Chung Lin and Jeng-Shyang Pan

Automatic control refers to the use of a control device to make the controlled object automatically run or keep the state unchanged without the participation of people. The guiding ideology of intelligent control is based on people’s way of thinking and ability to solve problems, in order to solve the current methods that require human intelligence. We already know that the complexity of the controlled object includes model uncertainty, high nonlinearity, distributed sensors/actuators, dynamic mutations, multiple time scales, complex information patterns, big data process, and strict characteristic indicators, etc. In addition, the complexity of the environment manifests itself in uncertainty and uncertainty of change. Based on this, various researches continue to suggest that the main methods of intelligent control can include expert control, fuzzy control, neural network control, hierarchical intelligent control, anthropomorphic intelligent control, integrated intelligent control, combined intelligent control, chaos control, wavelet theory, etc. However, it is difficult to want all the intelligent control methods in a chapter, so this chapter focuses on intelligent control based on fuzzy logic, intelligent control based on neural network, expert control and human-like intelligent control, and hierarchical intelligent control and learning control, and provide relevant and useful programming for readers to practice.

Part of the book: Automation and Control

By Kai-Chun Chu, Kuo-Chi Chang, Hsiao-Chuan Wang, Fu-Hsiang Chang, Yuh-Chung Lin and Tsui-Lien Hsu

Nowadays, the application technology and demand are growth; there have been millions of solutions for user communication in smart cities. However, the quality of the autonomy of handheld devices and the information exchange of applications are functions of requesting services or participating in communications. Therefore, it is very difficult and tedious to implement resource management and control in such an environment. This study here proposes distributed cyber-physical systems (CPS) for agent-based middleware framework (AMF) using to achieve technology, thereby improving the reliability of environmental communication in smart cities. The technical solution has the characteristics of avoiding the problem of data source interruption because of the proxy technology of the linear calculation model. The aforementioned agents are independent and autonomous of each other in terms of providing seamless resource sharing and response scheduling, and have nothing to do with communication time and request queries. In this study, the architecture mainly uses the best linear calculation model to classify overlapping agents, and then allocates non-overlapping resources, and finally analyzes the overall architecture operation performance by responding to processed queries, storage utilization and resource usage, pause time and response.

Part of the book: Smart Cities