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With the increases in the levels of automation and computerization, supervisory control systems are becoming increasingly common in commercial and military applications. A supervisory control system consists of one or more human operators interacting with highly automated components such as those seen in satellite ground control, flexible manufacturing systems, or nuclear power plants. Humans typically perform cognitively intense tasks such as monitoring, planning, real-time control, and troubleshooting, and are ultimately responsible for the safe and efficient operation of the overall system. Although developments on supervisory control have led to useful applications in interface design and automation, there is a scarcity of research that empirically evaluates human decision making in supervisory control through emulation of task performance using knowledge-based systems. In the context of dynamic planning involving simulated search and rescue missions using ground based autonomous robots and uninhabited aerial vehicles, we developed a knowledge-based system that mimics supervisory control performance. This paper describes the application domain, the details of the simulation model, and the implementation and assessment of a knowledge-based system that mimics human supervisory control performance.

In order to realize the supervisory control of production process, RFID-technology is introduced and a RFID-based application solution which is oriented to the process sequence of production manufacturing is proposed. Under this circumstance, borrowing the idea of axiomatic design, a mapping from part process field to monitor field is elaborately constructed and it bridges the wide gap between the virtual world and physical world. Combined with the part manufacturing process sequence, a real-time supervisory control model of production procedure logistic flow is designed. This model can be used in RFID middleware to explain and interpret the RFID real-time data so as to provide meaningful and valuable data for decision making.

In this paper, we extend the wavelet networks for identification and H_∞ control of a class of nonlinear dynamical systems. The technique of feedback linearization, supervisory control and H_∞ control are used to design an adaptive control law and also the parameter adaptation laws of the wavelet network are developed using a Lyapunov-based design. By some theorems, it will be proved that even in the presence of modeling errors, named network error, the stability of the overall closed-loop system and convergence of the network parameters and the boundedness of the state errors are guaranteed. The applicability of the proposed method is illustrated on a nonlinear plant by computer simulation.

A new direct adaptive type-2 fuzzy controller for a nonlinear dynamical system is developed in this paper. The parameters of the membership functions characterizing the linguistic terms in the type-2 fuzzy IF–THEN rules change according to some adaptive law for the purpose of controlling a plant to track a reference trajectory. A supervisory controller is appended to the type-2 fuzzy controller to force the state to be within the constraint set. Stability of this adaptive scheme is established using Lyapunov stability tools, where we guarantee the global stability of the resulting closed-loop system, in the sense that all signals involved are uniformly bounded. The simulation results for a Duffing forced-oscillation system show better performances, i.e. tracking error and control effort can be made smaller.

This chapter presents a new practical control system that can apply conventional PID controllers to nonlinear field by using fuzzy reasoning. The proposed system is a hierarchical one consisting of two components: (a) a Fuzzy-PID controller, and (b) a supervisor for setting the control target of this controller. The fuzzy controller in the Fuzzy-PID controller compensates the output error of the conventional PID controller. The supervisor calculates the control target by fuzzy reasoning. This hierarchical control system is applied to the temperature control in a petroleum plant. The parameters in fuzzy controller are tuned on-line in the actual plant and the system can control the temperature effectively in the transient state, such as feed property changing or operation mode changing, as well as in the steady state