In the rotary kiln alumina production process, because of the complexity and variability of rotary kiln burning zone conditions, some important quality index related process parameters can not be detected continuously on-line.Detecting the different burning zone conditions on-line is a key factor for the whole process automation of alumina industry. The current method depends on flame observation by naked eye.In order to realize automated recognition of burning zone conditions, a method which learned experience and knowledge from naked eye observation was proposed to recognize burning zone conditions by utilizing the image processing technique and pattern classification method.At first, features were extracted from flame images of rotary kiln burning zone and were combined with some important process parameters to constitute a hybrid feature vector.Then a model with a binary tree based SVM (support vector machine) was constructed.At last, a flame image recognition system was developed.The system was successfully applied to a domestic alumina plant, and good economic benefit was realized.
A class of unknown nonlinear systems subject to uncertain actuator faults and external disturbances will be studied in this paper with the help of fuzzy approximation theory. Using backstepping technique, a novel adaptive fuzzy control approach is proposed to accommodate the uncertain actuator faults during operation and deal with the external disturbances though the systems cannot be linearized by feedback. The considered faults are modeled as both loss of effectiveness and lock-in-place （stuck at some unknown place）. It is proved that the proposed control scheme can guarantee all signals of the closed-loop system to be semi-globally uniformly ultimately bounded and the tracking error between the system output and the reference signal converge to a small neighborhood of zero, though the nonlinear functions of the controlled system as well as the actuator faults and the external disturbances are all unknown. Simulation results demonstrate the effectiveness of the control approach.
This paper studies the problems of H-infinity performance optimization and controller design for continuous-time NCSs with both sensor-to-controller and controller-to-actuator communication constraints （limited communication channels）. By taking the derivative character of network-induced delay into full consideration and defining new Lyapunov functions, linear matrix inequalities （LMIs）-based H-infinity performance optimization and controller design are presented for NCSs with limited communication channels. If there do not exist any constraints on the communication channels, the proposed design methods are also applicable. The merit of the proposed methods lies in their Jess conservativeness, which is achieved by avoiding the utilization of bounding inequalities for cross products of vectors. The simulation results illustrate the merit and effectiveness of the proposed H-infinity controller design for NCSs with limited communication channels.
