Chai Tianyou

Direct adaptive fuzzy control for unknown multivariable nonlinear systems with fuzzy logic

In this paper, a fuzzy, adaptive tracking control scheme for a class of unknown dynamical nonlinear systems is presented. Since both parametric uncertainties and unmodeled dynamics are present in the nonlinear system, we design different controllers, respectively. Based on Lyapunov theory, we discussed the stability of the closed loop system and the convergence of the tracking errors.

Hybrid Intelligent Optimal Control Method for Operation of Complex Industrial Processes

During the operation of the industrial process, the optimal control objective is to control the technique indices that represent the quality, the efficiency, and the consumption of the product processing into its targeted ranges. However, due to the difficulty of measuring the technique indices on-line of the complex industrial process, the dynamics between the techniques and the control loops with complex natures, such as strong nonlinearity, heavy coupling and difficulty of description by the accurate model, and its dynamics varying with the process conditions, such a control objective by far is difficult to achieve by the existing control methods, thus theonly way of manual control is adopted. However, the manual control cannot adjust the setting point according to the conditions of the operation process timely and exactly. Therefore, it is difficult to control the technique indices into its desired ranges and even cause fault work-condition. In this paper, A hybrid intelligent control method for process optimal operation is proposed, which controls the technique indices into the desired ranges by on-line adjusting the set-points of the control loops according to the operation condition, enabling the control system to track the adjusted set-points. The proposed method is comprised of a control loop presetting model, feedforward and feedback compensators, a prediction model of the technique indices and a fault work-condition diagnosis unit plus a fault-tolerance controller. An application case study is given to illustrate the method being applied to a roasting process with 22 shaft furnaces in one ore concentration plant, and the application results have proven the effectiveness of the proposed method.

Fuzzy indirect adaptive control for a class of decentralized nonlinear systems

A stable fuzzy indirect control scheme is presented for a class of interconnected nonlinear systems for which an explicit linear parametrization of the uncertainty is either unknown or impossible. In the control algorithm, fuzzy logic systems, are employed to approximate the unknown dynamics in each subsystems, the feedback and adaptation mechanisms for each subsystems depend only upon local measurements to provide asymptotic tracking of a reference trajectory. In addition, a fuzzy sliding mode controller is developed to compensate for the fuzzy approximating errors or neural network approximating, and to attenuate the interactions between subsystems. Global asymptotic stability is established in the Lyapunov sense, with the tracking errors converging to a neighbourhood of zero

Adaptive fuzzy sliding mode control for nonlinear systems

This paper proposes a kind of adaptive fuzzy control scheme for a nonlinear system. In the design, we employ fuzzy systems to approximate nonlinear functions, then combine the sliding mode principle and Lyapunov function to obtain a stable adaptive controller. Furthermore, we apply fuzzy inference control to attenuate the chattering phenomenon being inherent in the conventional sliding mode controller, and prove that the closed-loop system is stable with the tracking error converging to a neighbourhood of zero.

INTELLIGENT CONTROL METHOD and APPLICATION for BOF STEELMAKING PROCESS

Abstract This paper introduces a new intelligent control method for Basic Oxygen Furnace (BOF) steelmaking dynamic process, by combining Neural Network, Fuzzy Inference, Expert System with dynamic process control method of BOF steelmaking. The control system is composed of the preset model of the dynamic requirement for oxygen blowing and coolant adding, bath [C] and temperature prediction model, and judgment Expert System for blowing-stop. The control method of BOF steelmaking process has been successfully applied in some steelmaking plants and improves the bath Hit Ratio (HR) significantly. It shows that the method is effective.

Multivariable intelligent decoupling control and its applications

Many complex industrial processes are multivariable, strongly coupled, nonlinear and time-variant, and conventional decoupling based control techniques will not work. This paper discusses multivariable intelligent decoupling control algorithms, and develops the three-level multivariable intelligent decoupling technique which can be easily realized in distributed control systems. Finally, examples of successful applications of the multivariable intelligent decoupling control technique in complex industrial processes are given.Abstract: Many complex industrial processes are multivariable, strongly coupled, nonlinear and time-variant, control systems based on conventional decoupling techniques cannot work. This paper discussed multivariable intelligent decoupling control algorithms, and developed the three-leveled multivariable intelligent decoupling technique which can be easily realized in Distributed Control Systems (DCS). Finally, examples of successful applications of multivariable intelligent decoupling control technique in complex industrial processes are introduced.

Notes on multivariable fuzzy controller under Gödel's implication

In this paper, we obtain the upper bounds and lower bounds for a multivariable fuzzy logic controller under Godels implication without any constraint conditions, and gave a sufficient condition in which fuzzy outputs can reach their upper and lower bounds.

Fault diagnosis expert system using neural networks for roasting process

Abstract An intelligent fault diagnosis system integrating neural networks, expert system and case-based reasoning technology is established for the roasting process of shaft furnace in hematite ores processing of China. The structure, function, and realization methods of the proposed system are presented. The proposed system has been successfully applied to the roasting process of shaft furnace in the biggest hematite ores processing factory of China. The industrial application shows the effectiveness of proposed system for the fault diagnosis of the roasting process of shaft furnace and its potential future in the complex industrial process.

Notice of Retraction Nonlinear dynamic soft senser modeling of wastewater treatment effluent quality

Due to the lack of widely stable and reliable water quality parameters on-line instrumentation, it is difficult to implement closed-loop control of water quality and optimize the operation for wastewater treatment plant. In this paper, a nonlinear dynamic soft-sensing multi-model based on PLS is proposed to solve the problem of multi-variable, non-linear and time-varying uncertainty in wastewater treatment process, through selection of such auxiliary variables easily received as water flow and quality, the dissolved oxygen and oxygen aeration. The methodology integrates dynamic ARX with Fuzzy C-means identifies operating conditions of time-varying and uncertainty in the wastewater treatment process. NNPLS is used to establish a number of non-linear model in different operating conditions and the whole non-linear system. The proposed method is applied in soft-sensing of effluent quality component concentration in wastewater treatment plant. Simulation results indicate that the method which establishes a multi-variable model of water quality indicators is more precise than traditional linear PLS model.

H/sup /spl infin// tracking of unknown nonlinear systems using neural network

A stable neural adaptive control scheme, is proposed to achieve H/sup /spl infin// performance for a class of unknown nonlinear SISO systems with external disturbances. In the control design, the controller comprises a certainty equivalence control term and an H/sup /spl infin// compensating term. The neural network is used to approximate the unknown nonlinear functions for the design of the equivalence controller, and the Lyapunov method is used for the update of the parameters of the neural network, the stability of the proposed control algorithm can be guaranteed. The performance of the proposed method is demonstrated through the control of the inverted pendulum system.