Hybrid simulation is used to obtain the dynamic response of a system whose components consist physical and numerical substructures. The coupling these substructures achieved by actuation systems, which are commanded in closed-loop control setting. To ensure high fidelity such hybrid simulations, performing them real-time necessary. However, poses challenges since inherent dynamics introduce tim...

In the research of renewable energy power generation, tubular grid-connected solid oxide fuel cells with apparent advantage in voltage regulation have been widely applied systems. Recently, a model predictive control has to consider nonlinear constraints cells, which cannot be considered by proportional-integral-derivative controller. Both and controller achieve only 80% efficiency. An adaptive...

With development of fast modern computers, it has become possible to extend model predictive control (MPC) method to automotive engine control systems, which is traditionally applied to plants with dynamics slow enough to allow computations between samples. In this paper MPC based on an adaptive neural network model is attempted for air fuel ratio (AFR), in which the model is adapted on-line to...

With the development of power switches and processor performance in recent years, control frequency inverters has been significantly improved. However, limited by technology price, sensor sampling large-scale industrial applications is much lower than inverter that can be realized. This mismatch limits improvement inverter. In this article, current voltage at non-sampling time are reconstructed...

We consider a discrete-time, linear time varying (LTV), multiple input, multiple output (MIMO) system with nu inputs and ny outputs. The system is known to be asymptotically stable, but the exact dynamics and the way they change over time are not known. We denote the vector of control inputs at time step t ∈ Z by u(t) = [u1(t), . . . , unu(t)] T , where ui(t) ∈ R, i = 1, . . . , nu are the indi...