control, is that intelligent control techniques are motivated by the functionality of intelligent biological systems, either in how they perform the control task or in how they provide an innovative solution to another problem that can be adapted to solve a control problem. This is not to say that systems that are not developed using intelligent systems and control techniques such as those listed above cannot be called"intelligent", traditionally, we have often called any system intelligent if it is designed to perform a task that has normally been performed by humans(e.g, we use the term A full discussion on defining intelligent control involves considering additional issues in psychology, human cognition, artificial intelligence, and control. The interested reader is referred to the articles listed at the end of this chapter for a more detailed exposition that considers these issues 4.6.2 Architecture and Characteristics Figure 4.9 shows a functional architecture for an intelligent autonomous controller with an interface to the process involving sensing(e.g, via conventional sensing technology, vision, touch, smell, etc. ) actuation(e. g, via hydraulics robotics, motors, etc. ) and an interface to humans(. g, a driver, pilot, crew, etc )and other systems The"execution level" has low-level numeric signal processing and control algorithms(.g, PID, optimal, adaptive, or intelligent control; parameter estimators, failure detection and identification(FDI)algorithms). The"coordination level"provides for tuning, scheduling, supervision, and redesign of the execution-level algorithms, crisis management planning and learning capabilities for the coordination of execution-level tasks, and higher-level symbolic decision making for FDI and control algorithm management. The"management level"provides for the supervision of lower-level functions and for managing the interface to the human(s)and other systems. In particular, the management level will interact with the users in generating goals for the controller and in assessing the capabilities of the system. The management level also monitors performance of the lower-level systems, plans activities at the highest level(and in cooperation with humans), and performs high-level learning about the user and the lower-level algorithms Humans and other subsystem Management el Coordination Process Figure 4.9 intelligent autonomous controllers Intelligent systems or intelligent controllers(e.g, fuzzy, neural, genetic, expert, and planning) can be employed appropriate in the implementation of various functions at the three levels of the intelligent autonomous controller. For example, adaptive fuzzy control may be used at the execution level for adaptation, genetic algorithms may be used in the coordination level to pick an optimal coordination strategy, and planning systems may be used at the management level PDF文件使用" pdffactory Pro"试用版本创建ww, fineprint,com,cncontrol, is that intelligent control techniques are motivated by the functionality of intelligent biological systems, either in how they perform the control task or in how they provide an innovative solution to another problem that can be adapted to solve a control problem. This is not to say that systems that are not developed using intelligent systems and control techniques such as those listed above cannot be called "intelligent"; traditionally, we have often called any system intelligent if it is designed to perform a task that has normally been performed by humans (e.g., we use the term "intelligent" vehicle and highway systems). A full discussion on defining intelligent control involves considering additional issues in psychology, human cognition, artificial intelligence, and control. The interested reader is referred to the articles listed at the end of this chapter for a more detailed exposition that considers these issues. 4.6.2 Architecture and Characteristics Figure 4.9 shows a functional architecture for an intelligent autonomous controller with an interface to the process involving sensing (e.g., via conventional sensing technology, vision, touch, smell, etc.), actuation (e.g., via hydraulics, robotics, motors, etc.), and an interface to humans (e.g., a driver, pilot, crew, etc.) and other systems. The "execution level" has low-level numeric signal processing and control algorithms (e.g., PID, optimal, adaptive, or intelligent control; parameter estimators, failure detection and identification (FDI) algorithms). The "coordination level" provides for tuning, scheduling, supervision, and redesign of the execution-level algorithms, crisis management, planning and learning capabilities for the coordination of execution-level tasks, and higher-level symbolic decision making for FDI and control algorithm management. The "management level" provides for the supervision of lower-level functions and for managing the interface to the human(s) and other systems. In particular, the management level will interact with the users in generating goals for the controller and in assessing the capabilities of the system. The management level also monitors performance of the lower-level systems, plans activities at the highest level (and in cooperation with humans), and performs high-level learning about the user and the lower-level algorithms. Figure 4.9 intelligent autonomous controllers. Intelligent systems or intelligent controllers (e.g., fuzzy, neural, genetic, expert, and planning) can be employed as appropriate in the implementation of various functions at the three levels of the intelligent autonomous controller. For example, adaptive fuzzy control may be used at the execution level for adaptation, genetic algorithms may be used in the coordination level to pick an optimal coordination strategy, and planning systems may be used at the management level PDF 文件使用 "pdfFactory Pro" 试用版本创建 www.fineprint.com.cn