Due to ever-increasing competition in the market, manufactured products are required to be of high quality at minimum cost, and, as a consequence, it is a trend that complex production systems with human operators in the loop are needed to be fully automated.
As is well known, manufacturing lines with human operator(s) in-the-loop are considered quite complex processes, and successful examples of automation are not easily found for such processes in which human expertise and skill are crucial. Various adjustment and/or assembly processes in the Color TV plant can be considered typical examples.
Real manufacturing environment is charcaterized as a system with complexity, uncertainty, and nonlinearity. In this case, for real-time control of manufacturing systems, algorithms that are computationally simple and less demanding, though not optimal, are often sought. For this, a consistent and efficient automation strategy should be established.
Considering the complexity of the system operation and the uncertainties in its characteristics, we propose to combine some of the features of heuristic rule-based systems and some of the advantages of a model-based approach to construct a multistage architecture in order to take advantage of the strengths of both while avoiding their weakness. In line with this proposal, this paper describes a hybrid-control approach for an automatic adjustment system of Integrated Tube Components (ITC).
In this paper, a hierarchical framework is presented for automation of Color Picture Tubes(CPT's) adjustment process. The design of the control system is based on a hybrid combination of heuristic knowledge from experts and approximated analytic models of the mechanisms for purity and convergence control of CPT. The adjustment system in practice is usually complex and operates under uncertainty and nonlinearity. To resolve difficulties due to system complexity the control object is hierarchically decomposed in planning stage into subsystems using the expert knowledge about task grouping and scheduling. And model-based search using Jacobian matrix is adopted to overcome the uncertainty factors and nonlinearity in each subsystem. The developed algorithms are tested in real automated environment with visual sensor feedback. Experimental results show that the proposed hierarchical control strategy can be adopted in industrial environments.
