Published November 2019 | Version v1
Journal article

An improved predictive control model for stochastic max-plus-linear systems

  • 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.2019.07.009

Additional 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.