Information in the nonstationary case
Neural Computation
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
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Networked control systems (NCS) could be utilised in several industrial applications. However, the variable time delays introduced by the network impair the NCS performance, resulting even in the instability of the controlled process. To mitigate the delay problems, the advantage is taken from model-based, adaptive controllers. This calls for an efficient approach for on-line analysis of measurements applied to update the controller state in NCS. The paper introduces a new adaptive Model Predictive Controller (MPC) capable of compensating for variations in measurement and actuating delays. Weighting factors for delayed measurements and actuators are adjusted based on normalised version of mutual information that is calculated using a procedure described in the paper. The method is superior compared with other, more usual, metrics.