Abstract
This chapter addresses the design of a DMPC based on both DDPG [1] and a distributed dynamical system partitioning [2,3,4]. To this end, the contributions presented in Chaps. 6 and 8 are combined to design a distributed optimization-based controller also considering a dynamical system partitioned. Depending on the current system states, some constraints are neglected in order to reduce the number of decision variables of the optimization problem behind the MPC controller design. Thus, the size of the information-sharing network is also reduced. The partitioning algorithm is performed to determine the appropriate set of sub-systems in function of the information-sharing network [2]. Finally, the DDPG approach computes all the optimal control inputs at each time instant [1], taking advantage of the population dynamics characteristics as studied in [5,6,7]. Notice that, due to the fact that the information-sharing network varies along the time, then the obtained optimal system partitioning is also different.
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Notes
- 1.
The physical partitioning can be obtained since there is a relationship between each node in the physical system and each node in the information-sharing network as it has been presented in Remark 8.3 at Sect. 8.1.3
References
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Barreiro-Gomez, J. (2019). Distributed System Partitioning and DMPC. In: The Role of Population Games in the Design of Optimization-Based Controllers. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-92204-1_9
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DOI: https://doi.org/10.1007/978-3-319-92204-1_9
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