Filter-based iterative learning control for linear large-scale industrial processes

In the procedure of the steady-state hierarchical optimization with feedback for large-scale industrial processes, a sequence of set-point changes with different magnitudes is carried out on the optimization layer. To improve the dynamic performance of transient response driven by the set-point changes, a filter-based iterative learning control strategy is proposed. In the proposed updating law, a local-symmetric-integral operator is adopted for eliminating the measurement noise of output information,a set of desired trajectories are specified according to the set-point changes sequence, the current control input is iteratively achieved by utilizing smoothed output error to modify its control input at previous iteration, to which the amplified coefficients related to the different magnitudes of set-point changes are introduced. The convergence of the algorithm is conducted by incorporating frequency-domain technique into time-domain analysis. Numerical simulation demonstrates the effectiveness of the proposed strategy,

Robust distributed controller design of rotational single-link smart materials beam

A dynamic modelling and controller design were presented for a single-link smart materials beam, a flexible beam bonded with piezoelectric actuators and sensors for better control performance. Taking into account bounded disturbances, a robust distributed controller was constructed based on the system model, which was described by a set of partial differential equations (PDEs) and boundary conditions (BCs) . Subsequently, a finite dimensional controller was further developed, and it was proven that this controller can stabilize the finite dimensional model with arbitrary number of flexible modes. Keywords Dynamic modelling - Robust distributed controller - Flexible beam - Smart material

Electricity Price Forecasting Based on AOSVR and Outlier Detection

Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, mis paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market

STEADY-STATE HIERARCHICAL INTELLIGENT CONTROL OF LARGE-SCALE INDUSTRIAL PROCESSES

This paper considers the fourth stage of development of hierarchical control ofindustrial processes to the intelligent control and optimization stage, and reviews what theauthor and his Group have been investigating for the past decade in the on-line steadystate hierarchical intelligent control of large-scale industrial processes (LSIP). This papergives a definition of intelligent control of large-scale systems first, and then reviews the useof neural networks for identification and optimization, the use of expert systems to solvesome kinds of hierarchical multi-objective optimization problems by an intelligent decisionunit (ID), the use of fuzzy logic control, and the use of iterative learning control. Severalimplementation examples are introduced. This paper reviews other main achievements ofthe Group also. Finally this paper gives a perspective of future development.