This paper considers security control under the network environment for system with deception attacks, polytopic uncertainty and persistent bounded disturbance. Since the state is immeasurable, and the physical constraint is considered, the output feedback robust model predictive control (MPC) is utilized. The previous results on the output feedback robust MPC are extended to address the deception attacks. The mean-square quadratic boundedness (MSQB) is defined in order to characterize the closed-loop stability. An optimization problem is proposed, which can be solved by linear matrix inequality techniques. By a simple refreshment of the state estimation error set (EES), the optimization problem is shown to be recursively feasible, and the augmented state converges to the neighborhood of equilibrium point. The effectiveness of the proposed theoretical approach is demonstrated by a numerical simulation example.

Reconstructing visual stimulus representation is a significant task in neural decoding. Until now, most studies have considered functional magnetic resonance imaging (fMRI) as the signal source. However, fMRI-based image reconstruction methods are challenging to apply widely due to the complexity and high cost of acquisition equipment. Taking into account the advantages of the low cost and easy portability of electroencephalogram (EEG) acquisition equipment, we propose a novel image reconstruction method based on EEG signals in this article. First, to meet the high recognizability of visual stimulus images in a fast-switching manner, we construct a visual stimuli image dataset and obtain the corresponding EEG dataset through EEG signals collection experiment. Second, we introduce the deep visual representation model (DVRM), comprising a primary encoder and a subordinate decoder, to reconstruct visual stimuli representation. The encoder is designed based on residual-in-residual dense blocks to learn the distribution characteristics between EEG signals and visual stimulus images. Meanwhile, the decoder is designed using a deep neural network to reconstruct the visual stimulus representation from the learned deep visual representation. The DVRM can accommodate the deep and multiview visual features of the human natural state, resulting in more precise reconstructed images. Finally, we evaluate the DVRM based on the quality of the generated images using our EEG dataset. The results demonstrate that the DVRM exhibits an excellent performance in learning deep visual representation from EEG signals, generating reconstructed representation of images that are realistic and highly resemble the original images.

This article considers dynamic output feedback model predictive control for quasi-linear parameter varying model with norm-bounded disturbance. The infinite horizon control moves are parameterized as the parameter-dependent dynamic output feedback (PDDOF) laws. In contrast to the traditional single-step approach where one PDDOF law is utilized, in this article a periodic approach where a sequence of PDDOF laws, which are invoked periodically in the future predictions, are applied. The regions of attraction for the periodic approach are considerably larger, and the control performance improved, as compared with the single-step approach. The recursive feasibility, and convergence of augmented state and output/input, are guaranteed. An illustrative example is given to show the effectiveness of this approach.

In this paper, the fuzzy model predictive control for nonlinear system represented by a discrete-time Takagi-Sugeno model with norm-bounded disturbance is studied. An output feedback algorithm is proposed by parameterizing the infinite horizon control moves and estimated states into one free control move, one free estimated state followed by a dynamic output feedback law. Since the introduced free control move and free estimated state are decision variables, which bring more degrees of freedom for the optimization, larger feasibility region and better control performance can be achieved. By properly designing the constraints in optimization problem, the recursive feasibility is guaranteed, and the convergence of the closed-loop system to the neighborhood of the equilibrium is guaranteed. A numerical example is given to illustrate the effectiveness of the proposed algorithm.