An improved predictive control model for stochastic max-plus-linear systems
Creators
- 1. College of Science, North China University of Science and Technology, Tangshan 063210 (China)
- 2. College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050024 (China)
Description
In order to improve the robustness and stability of the model predictive control system, this paper research the problem by combination of stochastic predictive control and max-plus theory. Based on the analysis of the stochastic predictive control model, the maximum plus stochastic predictive control model is constructed, which is improved by the max-plus algebraic theory. The superiority of the maximum plus stochastic predictive control model is verified by simulation and experiment. The max-plus algebra is an algorithm which is suitable for noise processing of input signal, which can stabilize the input of the control system. The disadvantage of stochastic predictive control model is that the input signal is subjected to random disturbance in the external environment, max-plus algebraic theory can better compensate for the defect. The simulation results show that the stochastic predictive control model has significant advantages in accuracy, stability and robustness.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.chaos.2019.07.009Additional details
Identifiers
- DOI
- 10.1016/j.chaos.2019.07.009;
- PII
- S0960077919302619;
Publishing Information
- Journal Title
- Chaos, Solitons and Fractals
- Journal Volume
- 128
- Journal Page Range
- p. 210-218
- ISSN
- 0960-0779
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 54120608
- Subject category
- S97: MATHEMATICAL METHODS AND COMPUTING;
- Descriptors DEI
- ALGEBRA; ALGORITHMS; COMPUTERIZED SIMULATION; CONTROL SYSTEMS; NOISE; RANDOMNESS; SIGNALS; STOCHASTIC PROCESSES
- Descriptors DEC
- MATHEMATICAL LOGIC; MATHEMATICS; SIMULATION
Optional Information
- Copyright
- Copyright (c) 2019 Elsevier Ltd. All rights reserved.