Abstract
Model-free adaptive control (MFAC) builds a virtual equivalent dynamic linearized model by using a dynamic linearization technique. The virtual equivalent dynamic linearized model contains some time-varying parameters, time-varying parameters usually include high nonlinearity implicitly, and the performance will degrade if the nonlinearity of these time-varying parameters is high. In this paper, first, a novel learning algorithm named error minimized regularized online sequential extreme learning machine (EMREOS-ELM) is investigated. Second, EMREOS-ELM is used to estimate those time-varying parameters, a model-free adaptive control method based on EMREOS-ELM is introduced for single-input single-output unknown discrete-time nonlinear systems, and the stability of the proposed algorithm is guaranteed by theoretical analysis. Finally, the proposed algorithm is compared with five other control algorithms for an unknown discrete-time nonlinear system, and simulation results show that the proposed algorithm can improve the performance of control systems.
Highlights
It is difficult to obtain an accurate mechanism model of a physical system when the production technologies and processes are very complex
After several years of development, some data-driven control techniques have been investigated, such as, proportional-integral derivative (PID) [2], fuzzy logic control [3], unfalsified control (UC) [4,5], model free adaptive control (MFAC) [6,7,8,9,10,11,12], iterative learning control (ILC) [13,14,15], iterative feedback tuning (IFT) [16,17], some control algorithms based on neural network [18,19,20,21,22,23] and so on
In order to analyse the stability of the MFAC algorithm based on REOS-Extreme learning machine (ELM) for unknown discrete-time nonlinear systems, an updating formula, which contains dead-zone characteristics, is introduced, and EMREOS-EM is investigated for the purpose of getting a more compact network structure and improving the performances of control systems
Summary
It is difficult to obtain an accurate mechanism model of a physical system when the production technologies and processes are very complex. The virtual equivalent dynamic linearized model contains some time-varying parameters. It is often not easy to obtain the resulting time-varying parameters, but they can be estimated by using historical data of control systems.
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