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Multi-step optimal control of complex process: a genetic programming strategy and its application
Engineering Applications of Artificial Intelligence, 17 (5): 491--500 (2004)
Аннотация
In many industrial processes, especially chemistry and
metallurgy industry, the plant is slow for feedback and
data test because of complex and varying factors.
Considering the multi-objective feature and the complex
problem of production stability in optimal control,
this paper proposed an optimal control strategy based
on genetic programming (GP), used as a multi-step state
transferring procedure. The fitness function is
computed by multi-step comprehensive evaluation
algorithm, which provides a synthetic evaluation of
multi-objective in process state based on single
objective models. The punishment to process state
variance is also introduced for the balance between
optimal performance and stability of production. The
individuals in GP are constructed as a chain linked by
a few relation operators of time sequence for a
facilitated evolution in GP with compact individuals.
The optimal solution gained by evolution is a
multi-step command program of process control, which
not only ensures the optimisation tendency but also
avoids violent process variation by adjusting control
parameters step by step. An optimal control system for
operation direction is developed based on this strategy
for imperial smelting process in Shaoguan. The
simulation and application results showed its
effectiveness for production objects optimisation in
complex process control.
