In the iron flotation production process, production stages often undergo updates due to equipment upgrades, changes in raw materials, and other reasons. The operating condition prediction model established based on data from previous production stages may not meet the requirements of the new stage, resulting in a significant waste of collected datasets. Data-driven models established using small samples collected during the current stage may lack accuracy due to the limited sample size. This study proposes a method based on knowledge transfer to effectively leverage a large amount of outdated data. It allows for the rapid establishment of a new model that aligns with production requirements while minimizing the need for additional data collection. In previous tailings grade soft sensors, more emphasis was placed on quality parameters such as flotation froth features, often overlooking production process parameters. To enhance model accuracy, we introduce a multivariate collaborative modeling approach. The experimental results and industrial applications validate the effectiveness of this method.

The issue of sliding-mode control (SMC) design is studied for a class of nonlinear semi-Markovian switching systems (S-MSSs) via T–S fuzzy approach. Compared with previous literature, novel integral sliding-mode surfaces (ISMSs) are developed to depend on the designed controller gains and projection matrices. The aim of this work is to design an appropriate fuzzy SMC law under complex stochastic semi-Markovian switching process. For this purpose, the novel ISMSs are developed under the T–S fuzzy modeling framework. Then, based on the weak infinitesimal operator theory, sufficient conditions are given for stochastic stability criteria relying on the sojourn-time. Furthermore, an appropriate fuzzy SMC law is proposed to drive the state signals onto the predefined fuzzy manifold. Finally, an electric circuit model illustrates the effectiveness of the theoretical findings.

This article focuses on the problem of robust distributed fault estimation for nonlinear multiagent systems with actuator faults and sensor faults. The communication topology of the multiagent systems is assumed to be directed. A novel intermediate observer design method is proposed to estimate the system states, actuator faults, and sensor faults. For the observer constructed in one agent, the output estimation errors of itself and its neighbors are considered, simultaneously. The observer matching condition is not needed in the observer design process. Based on Schur decomposition, the observer parameter calculation method is presented in terms of solution to one linear matrix inequality, which is with the same order as it is for the single agent system. Thus, the calculated amount remains unchanged even when the number of agents increases, since the inequality dimension is independent of the agent number. At last, simulation results are provided to illustrate the effectiveness of the proposed technique.

This paper is devoted to the problem of distributed fault estimation for multiagent systems with nonlinear dynamic. For each agent, an augmented system can be obtained by treating the possible fault as a special system state. Utilizing the output of the neighbor agents, the nonlinear fault estimation observer can be constructed in one agent. The possible faults in both this agent and its neighbors can be estimated by the observer. By solving linear matrix inequalities, the parameter matrices of the observer can be computed. The region pole constraint is considered, which can improve the estimation performance. At last, both a numerical simulation and a network of four one-link flexible joint manipulator systems are considered to verify the proposed fault estimation approach.