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Fuzzy adaptive-predictive decentralized control of discrete time interconnected systems

Tzafestas S., Kyriannakis E., Apostolikas G.

Systems Science

2002

Vol. 28, nr 1

5-24

Proposes an approach for the design of discrete-time decentralized control systems with m-step delay sharing information pattern, employing model-based predictive control (MBPC) combined with fuzzy prediction for the interconnections among the subsystems. A state space model is used at each control station to predict the corresponding subsystem output over a long-range time period. The interaction trajectories are considered to be non-linear functions of the states of the subsystems. For all cases the interconnections and the necessary predictions for them are estimated by an appropriate adaptive fuzzy identifier based on the generation of linguistic IF-THEN rules and the on-line construction of a common fuzzy rule base. Representative computer simulation results are provided and compared for nontrivial example systems.

Model-based predictive control of large-scale systems using a neural estimator

Tzafestas S., Kyriannakis E., Anthopoulos Y.

Applied Mathematics and Computer Science

1998

Vol. 8, no 3

585-598

An approach to the design of discrete-time decentralized controI systems based on model-based predictive controI (MBPC) and neural estimation is proposed. The class of interconnected large-scale systems (LSS) is considered, and a model is used at each controI station to predict the corresponding subsystem output over a long time period. In the case of subsystems with m-step delay information patterns the non-locally available interaction trajectories are estimated by a multi-layer neural network trained on-line with a modified backpropagationtype algortithm. Representative computer simulation results are provided and compared for a set of illustrative examples. The proposed controI scheme shows better performance than the other schemes, and also covers the important case where the subsystems' interactions are nonlinear.