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This work proposes an iterative identification algorithm for polynomial NARX models with several inputs.
To conform to these particular conditions, an iterative identification method was developed.
The identified models will then suitable for iterative identification-control design.
A new iterative identification method is proposed in which local and global information about the control design criterion is blended.
Iterative identification has been applied to arrive at feasible and realistic design solutions for this multivariable process control problem.
A new simple method is also introduced for adaptive and combined iterative identification and control for time-delay plants.
Convergence analysis of iterative identification-optimization schemes is a key issue in modeling for optimization of batch processes.
Studies on iterative identification and model based control design have shown the necessity for identifying models on the basis of closed-loop data.
They include difficulties associated with the MIT rule, bursting, the Rohr's counterexample and unplanned instability in iterative identification and control.
In this paper, a customized version of iterative identification and control algorithms is presented, oriented to the design of reduced-order controllers with increasing performance (in terms of bandwidth for reference tracking).
This observation has led to the concept of "iterative identification and control design" and, subsequently, to model-free iterative controller design, in which the controller parameters are iteratively tuned on the basis of successive experiments performed on the real plant, leading to better and better closed loop behaviour.
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