Nash-based robust distributed model predictive control for large-scale systems
| Autor: | Nooshin Bigdeli, Mehdi Rahmani, Reza Aliakbarpour Shalmani |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Optimization problem Scale (ratio) Computer science Automatic frequency control Stability (learning theory) Feed forward 02 engineering and technology Kalman filter Upper and lower bounds Industrial and Manufacturing Engineering Computer Science Applications 020901 industrial engineering & automation Quadratic equation 020401 chemical engineering Control and Systems Engineering Control theory Modeling and Simulation 0204 chemical engineering |
| Zdroj: | Journal of Process Control. 88:43-53 |
| ISSN: | 0959-1524 |
| Popis: | In this paper, a new robust distributed model predictive control (RDMPC) is proposed for large-scale systems with polytopic uncertainties. The time-varying system is first decomposed into several interconnected subsystems. Interactions between subsystems are obtained by a distributed Kalman filter, in which unknown parameters of the system are estimated using local measurements and measurements of neighboring subsystems that are available via a network. Quadratic boundedness is used to guarantee the stability of the closed-loop system. In the MPC algorithm, an output feedback-interaction feedforward control input is computed by an LMI-based optimization problem that minimizes an upper bound on the worst case value of an infinite-horizon objective function. Then, an iterative Nash-based algorithm is presented to achieve the overall optimal solution of the whole system in partially distributed fashion. Finally, the proposed distributed MPC approach is applied to a load frequency control (LFC) problem of a multi-area power network to study the efficiency and applicability of the algorithm in comparison with the centralized, distributed and decentralized MPC schemes. |
| Databáze: | OpenAIRE |
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