Transactions of the Institute of Measurement and Control

Rao, Jinjun; Gong, Zhenbang; Luo, Jun; Jiang, Zhen; Xie, Shaorong; Liu, Wufa

doi: 10.1177/0142331207075608pmid: N/A

Robotic airships have numerous low-speed and low-altitude application potentials. Mission path following is one such application, which, however, presents an autonomy challenge. In this paper, a yawing controller, which is based on artificial neural network (ANN) and human operator skills, is proposed for mission path following of robotic airships. First, the path-following errors based on the operator’s point of view are discussed. Then, a data acquisition system is designed to collect the flight data under manual control, and the data are then processed and used for offline training and validation of a multilayer feed-forward neural network. Finally, the trained neural network is reconstructed in the flight control system for yawing control, and the experimental results confirm the effectiveness of this method. It is also shown that the ANN controller is robust even with wind disturbance.

Bavdaž, G.; Kocijan, J.

doi: 10.1177/0142331207070362pmid: N/A

The main goal of raw material mill blending control in the cement industry is to maintain the chemical composition of the raw meal near the reference cement modules for the kiln at a desired value with minimum variance despite variations in the raw material composition. Because of the continuous variations in the chemical composition of the raw materials, one cannot work with a fixed blending scheme. The lack of an on-line measurement system, for the raw material chemical composition, additionally complicates the construction of the control algorithms. In this paper, a new type of the Takagi - Sugeno (TS) fuzzy controller based on the incremental algorithm for cement raw material blending purposes is presented. The presented control algorithm was tested on the raw mill simulation model within a Matlab™- Simulink™environment. Parameters of the simulation model were set up based on the measurements from the actual raw mill. Several step tests were carried out to validate the model. The results of the control study indicate that the proposed algorithm can improve cement raw material blending.

Muñoz-Hernández, G. A.; Jones, D. I.

doi: 10.1177/0142331207071137pmid: N/A

In this paper, the performance of constrained Generalized Predictive Control (CGPC) is investigated on a comprehensive non-linear multivariable simulation of the Dinorwig pumped-storage hydroelectric power station. The responses of the system are compared with those of the classic PI controller, as currently implemented on the plant. The results show that CGPC offers significantly better performance over the whole operating envelope. Fixed-parameter CGPC produces a faster primary response and better steady-state accuracy when the station is operating with a single unit, while also preserving stability when the operating conditions change as multiple units come on-line. It can accommodate large amplitude ramp power changes in combination with feed-forward control. It is less sensitive than PI to changes in hydraulic head and gives better power-tracking when units operate in frequency-control mode. Finally, the computational time of the CGPC algorithm is investigated.

Efe, Mehmet Önder

doi: 10.1177/0142331207073487pmid: N/A

Control of systems governed by Partial Differential Equations (PDEs) is an interesting subject area, as the classical tools of control theory are not directly applicable and PDEs can display enormously rich behaviour spatiotemporally. This paper considers the boundary control of a 2D heat flow problem. A solution to the control problem is obtained after a suitable model reduction. The considered PDE system is subject to Dirichlet boundary conditions of generic type f(x)γ(t). The separation of these boundary excitations after Proper Orthogonal Decomposition yields an autonomous Ordinary Differential Equation (ODE) set in which the boundary excitations are implicit. The main contribution of this paper is to describe a mathematical treatment based on the numerical observations such that the implicit excitation terms explicitly appear in the ODE set. With such an ODE model, standard tools of feedback control theory can be applied. A measurement point has been chosen, and the desired behaviour is forced to emerge at the chosen point. A root locus technique is used to obtain the controller. It is seen that the results obtained are in good compliance with the theoretical claims.

Toscano, R.; Lyonnet, P.

doi: 10.1177/0142331207076371pmid: N/A

In this paper, we investigate the problem of robust synthesis of a static output feedback controller, with guaranteed quadratic cost, in the context of multiple parametric uncertainties. To solve this problem, a random optimization technique based on a bisection method is proposed. The principle is as follows: for a given initial stabilizing controller of the nominal system, the proposed approach iteratively generates a sequence of matrices with a decreasing quadratic cost. Using a bisection method, this procedure is stopped when the controller reaches the best possible nominal performance that satisfies a given guaranteed quadratic cost. A numerical example shows the practical applicability of the proposed method.