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In this paper, a new interaction prediction approach for hierarchical control of non-linear large-scale systems is presented. The proposed approach uses a new gradient-type coordination scheme which is robust with respect to the parameters' variation, and also has a good convergence rate. In classical coordination strategies, which can be divided into the gradient-type and substitution-type approaches, it is not possible to improve the robustness and the convergence rate at the same time, since by increasing one the other decreases. The proposed approach has the main advantages of the gradient-type algorithms in being independent of the parameter's variation and also the initial guess of the... 

In this paper, a new decomposition-coordination framework is presented for two-level optimal control of large-scale nonlinear systems. In the proposed approach, decomposition is performed by defining an interaction vector, while coordination is based on a new interaction prediction approach. In the first level, sub-problems are solved for nonlinear dynamics using a gradient method, while in the second level, the coordination is done using the gradient of coordination errors. This is in contrast to the conventional gradient-type coordination schemes, where they use the gradient of Lagrangian function. It is shown that the proposed decomposition-coordination framework considerably reduces the... 

In this paper, a new interaction prediction approach is presented for optimal control of nonlinear large-scale systems. The proposed approach uses a new gradient-type coordination scheme which has a larger convergence region with respect to the parameters' variation, and also has a good convergence rate. In this approach, the coordination vector is updated using the gradient of coordination error. This type of coordination considerably reduces the number of iterations. The robustness and the convergence rate of the proposed approach against the best classical interaction prediction approaches are shown through simulations of a benchmark problem. © 2008 IEEE  

In this paper, a novel computational algorithm is proposed for hierarchical optimal control of nonlinear systems. The hierarchical control uses a new coordination strategy based on the gradient of the coordination errors. This type of coordination extremely reduces the number of iterations required for obtaining the overall optimal solution. The performance and the convergence rate of the proposed approach, in compare to the classical gradient-type interaction prediction approach, is shown through simulations of a benchmark continuous stirred tank reactor (CSTR) problem. ©2008 IEEE