This article is concerned with modeling of a class of permanent-magnet-synchronous-motor-based drive systems with dead-zone input nonlinearity. The experimental testing method based on recursive least squares (RLS) is combined with the mechanism analysis method based on physical laws. With the structure of the analytical model first determined by the mechanism analysis method, an online RLS-based data-driven method is then exploited to obtain the unknown parameters of the analytical model. Since the unknown parameters are decoupled in the proposed method, all parameter estimates are available. An auxiliary model and a variable forgetting factor are constructed to ensure the high accuracy and the fast convergence of the proposed method, respectively. Besides, the asymptotic convergence property of the proposed method is analyzed, and the upper bound of the parameter estimation error is also presented. The effectiveness of the proposed method is verified by simulations and experiments on a self-designed computer numerical control engraving system.

This study investigates the robust recursive identification problem for Wiener non‐linear systems with non‐stationary disturbance and measurement white Gaussian noise. Non‐stationary disturbances cannot be eliminated by conventional statistical strategy. To overcome such problem, the non‐stationary disturbance signal is modelled as a dynamic parameter with a small change rate which is to be estimated step. Utilising the forgetting factor (FF) and the auxiliary model strategy, a discrete‐time recursive least‐squares algorithm is designed by augmenting the parameter vector and the information vector. To facilitate the steady asymptotic convergence of the time‐invariant system parameters and the dynamic tracking ability of the disturbance, two adaptive FFs are proposed to construct a new matrix FF scheme that is used to update the covariance matrix. Moreover, several asymptotic convergence conditions are also derived based on the stochastic statistical theory under persistent excitation condition. Finally, one illustrative example with non‐stationary disturbance is employed to show the effectiveness and advantages of the proposed algorithms.

Intelligent prediction of key output variables that are difficult to measure online in complex systems has important research significance. In this paper, by using the least absolute shrinkage and selection operator (LASSO) algorithm to analyze the principal elements of input variables, a temporal convolutional network fused with long short-term memory (TCN-LSTM) network and self-attention mechanism (SAM) is designed to realize dynamic modeling of multivariate feature sequences. For complex processes with multiple input variables, each variable has different effects on the output, so it is necessary to use the LASSO algorithm to perform regression analysis on the input and output data for selecting the principal component variables and reducing the redundancy and computation burden of the network. The TCN network is used to extract the features of the input variables efficiently. The long-term memory performance of time series is enhanced by applying an LSTM network. The multihead SAM is used to optimize the network, and the role of key features is enhanced by assigning weights with probability to further improve the accuracy of sequence prediction. Finally, by comparison with the existing network model, the offline data generated by the high and low converters in the synthetic ammonia industry is used to predict the CO content so as to verify the superiority and applicability of the proposed network model.

Hydrometallurgy technology can directly deal with low grade and complex materials, improve the comprehensive utilization rate of resources, and effectively adapt to the demand of low carbon and cleaner production. A series of cascade continuous stirred tank reactors are usually applied in the gold leaching industrial process. The equations of leaching process mechanism model are mainly composed of gold conservation, cyanide ion conservation, and kinetic reaction rate equations. The derivation of the theoretical model involves many unknown parameters and some ideal assumptions, which leads to difficulty and imprecision in establishing the accurate mechanism model of the leaching process. Imprecise mechanism models limit the application of model-based control algorithms in the leaching process. Due to the constraints and limitations of the input variables in the cascade leaching process, a novel model-free adaptive control algorithm based on compact form dynamic linearization with integration (ICFDL-MFAC) control factor is first constructed. The constraints between input variables is realized by setting the initial value of the input to the pseudo-gradient and the weight of the integral coefficient. The proposed pure data-driven ICFDLMFAC algorithm has anti-integral saturation ability and can achieve faster control rate and higher control precision. This control strategy can effectively improve the utilization efficiency of sodium cyanide and reduce environmental pollution. The consistent stability of the proposed control algorithm is also analyzed and proved. Compared with the existing model-free control algorithms, the merit and practicability of the control algorithm are verified by the practical leaching industrial process test. The proposed model-free control strategy has advantages of strong adaptive ability, robustness, and practicability. The MFAC algorithm can also be easily applied to control the multi-input multi-output of other industrial processes.