In nonlinear industrial processes, some product qualities or key variables are usually difficult to measure online automatically due to the lack of sensors. In this work, a novel data-driven soft sensor technology based on multilayer perceptron neural network (MLP) with a double least absolute shrinkage and selection operator (dLASSO) approach, named dLASSO-MLP, is developed with a two-step procedure. Firstly, an MLP model is constructed through the process data set. Secondly, a double LASSO algorithm is integrated into the model to solve two redundancy problems, i.e., the input variables redundancy and model structure redundancy. The proposed method not only selects input variables that are the most sensitive to the model, but also simplifies the MLP structure by deleting redundant hidden nodes to avoid the model overfitting. In addition, the method is validated by data from simulation examples as well as an industrial application. Compared to the other neural network methods, the proposed method requires fewer neurons and presents better prediction performance.

In the industrial process, it is helpful to take the previous fault diagnosis results into consideration during the current determination of faulty variables. In this paper, an unsupervised data-driven fault diagnosis method based on normalized relative reconstruction-based contribution (RBC) and the minimum risk Bayesian (MRB) decision theory is presented. Normalized relative RBC is used to represent the characteristic of the observation of the samples, and beta distribution is adopted to approximate the probability density distribution of the variable being faulty or normal. On the basis of the adjustable loss function, the conditional risk is obtained. The fault diagnosis method based on MRB decision is proposed, which will reduce the influence of smearing effect, improve the diagnosis rate, handle faults with a small magnitude, and identify multiple process faults. Numerical simulation examples and the Tennessee Eastman process are given to show the effectiveness and superiority of the proposed method.

This study investigates the distributed containment control for second‐order multi‐agent systems with multiple stationary or dynamic leaders based on only position information. Based on stability and algebraic graph theories, some necessary and sufficient conditions are obtained. Firstly, when the leaders are stationary, a necessary and sufficient condition is given to guarantee that all followers will ultimately converge to the stationary convex hull spanned by the stationary leaders for arbitrary initial states. The containment control protocol is designed based on a distributed filter, which is used for estimating the neighbours’ velocities of each follower. Secondly, when each leader is dynamic with constant velocity, using sampled current and outdated position data, a necessary and sufficient condition is obtained to drive the followers into the dynamic convex hull spanned by the dynamic leaders. Finally, some numerical simulations are presented to illustrate the proposed theories.

High precision register controls are indispensable in roll-to-roll (R2R) printing systems for mass manufacturing. In R2R printing systems, each gravure cylinder is driven by an individual motor and guiding rolls distributed between two adjacent gravure cylinders are driven by the tension of their wrapped web. In the speed-up phase, tension fluctuations caused by torque balance of guiding rolls generate register errors in each printing unit. Therefore, it is a challenging issue to design a control method to reduce the register errors. In this paper, a mechanical model of R2R printing systems in the speed-up phase is developed based on the principle of mass conservation and torque balance. A model-based feed-forward proportion-derivative controller is designed to reduce the register errors caused by the tension fluctuations. The validity of the proposed method is demonstrated by simulation and experiments carried out on an industrial rotogravure printing press. A comparison with other control methods especially a well-tuned proportion differential control which is widely adopted by printing presses shows that the absolute maximum register error is drastically reduced and the average register error is greatly decreased by the proposed method. The results verify the effectiveness and feasibility of the proposed control method.

In this paper, the torus-event-based fault detection and isolation (FDI) problem is investigated for a class of time-varying multirate systems. An ellipsoidal constraint is first adopted to describe the fault in a more practical pattern, and a novel torus-event-triggering scheme is proposed to improve the unilateral triggering mechanism. The aim is to design the torus-event-based fault detection filter and fault isolation estimators such that both the prescribed variance constraint on the estimation error and the desired H _∞ performance on the disturbance are guaranteed over the finite horizon. Especially, the residual evaluation function is employed to detect the fault, and the residual matching function is developed to isolate the fault. Furthermore, three optimization problems are provided to seek separately the minimal parameters on the H _∞ performance level, the upper bound of the estimation error variance, and the triggering torus. Finally, two simulation examples are utilized to show the effectiveness of the FDI scheme proposed in this paper.