Distributed model predictive control for consensus of nonlinear systems via parametric sensitivity.

To handle the nonlinear consensus problem, a distributed model predictive control (DMPC) scheme is developed via parametric sensitivity. A two-stage input computation strategy is adopted for enhancing optimization efficiency. In the background stage, each agent first establishes its next-step optimization problem based on communication topology, and then performs distributed optimization to calculate the future inputs.

Deep Subdomain Learning Adaptation Network: A Sensor Fault-Tolerant Soft Sensor for Industrial Processes.

Sensor faults are non-negligible issues for soft sensor modeling. However, existing deep learning-based soft sensors are fragile and sensitive when considering sensor faults. To improve the robustness against sensor faults, this article proposes a deep subdomain learning adaptation network (DSLAN) to develop a sensor fault-tolerant soft sensor, which is capable of handling both sensor degradation and sensor failure simultaneously.

Effect of platelet-derived growth factor (PDGF-BB) and bone morphogenic protein 2 (BMP-2) transfection of rBMSCs compounded with platelet-rich plasma on adipogenic differentiation.

The aim of this study was to inhibit adipogenic differentiation by transfecting two growth factors, platelet-derived growth factor (PDGF-BB) and bone morphogenic protein 2 (BMP-2), into modified rat bone marrow mesenchymal stem cells (rBMSCs) and then compounded with platelet-rich plasma (PRP). To achieve rBMSCs, the osteoporosis model of rats was established, and then the rBMSCs from the rats were isolated and identified. Co-transfection of rBMSCs with PDGF-BB-GFP and BMP-2 and detection of PDGF-BB/BMP-2 expression in transfected BMSCs was assessed by qRT-PCR and western blot, respectively.

Input and state estimation for linear systems with a rank-deficient direct feedthrough matrix.

The problem of joint input and state estimation for linear stochastic systems with a rank-deficient direct feedthrough matrix is discussed in this paper. Results from previous studies only solve the state estimation problem; globally optimal estimation of the unknown input is not provided. Based on linear minimum-variance unbiased estimation, a five-step recursive filter with global optimality is proposed to estimate both the unknown input and the state.

A two-time scale decentralized model predictive controller based on input and output model.

A decentralized model predictive controller applicable for some systems which exhibit different dynamic characteristics in different channels was presented in this paper. These systems can be regarded as combinations of a fast model and a slow model, the response speeds of which are in two-time scale. Because most practical models used for control are obtained in the form of transfer function matrix by plant tests, a singular perturbation method was firstly used to separate the original transfer function matrix into two models in two-time scale.