A predictive control and scheduling co-design approach is proposed to deal with the controller and scheduler design for a set of networked control systems which are connected to a shared communication network. In the proposed approach, a predictive controller is applied to generate the control predictions for each system using delayed sensing data and previous control information, and a time delay compensator is designed at the actuator side to actively compensate for the network-induced delay in the forward channel when the control action is taken. Two different scheduling algorithms, the existing static rate monotonic (RM) scheduling algorithm and a new dynamic scheduling algorithm called dynamic feedback scheduling (DFS), are considered to schedule the transmissions of the control signals generated by the predictive controller, which are packed and transmitted to the actuator in one packet simultaneously. Both the scheduling algorithms are designed with the guarantee of the stability of all the systems, which is achieved by ensuring that the time delay of the systems do not exceed the upper bound under which the systems are stable. It is also pointed out that the RM algorithm is a special case of the proposed DFS algorithm, in the sense that the former can work only in a private network environment, whereas the latter extends its application to such networks where other components occupying the network. Simulations for both the RM and the DFS algorithms, illustrate the validity of the proposed approach.

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Data packet disorder often occurs in networked control systems (NCSs), which, however, has not been taken into account in most literature to date. In this brief, the cause and effect of data packet disorder are analyzed, and an active compensation scheme is proposed to compensate for it. The proposed scheme is flexible to admit all the existing control approaches to be used and also derives a novel closed-loop system model of NCSs, which enables more reasonable and effective theoretical analysis of NCSs. The effectiveness of the proposed active compensation scheme is illustrated by a numerical example.

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A predictive control-based approach is proposed to networked control systems. In this approach, an improved predictive controller is designed using delayed sensing data and a compensation scheme is proposed to overcome the negative effects of the network-induced delays and data packet dropouts in both the forward and backward channels. The proposed approach is easy to be implemented in practice compared with previous results in that only delayed data of the control inputs are used to derive the forward control predictions. The stability of the closed-loop system is obtained by modelling the system as a time delay system with structural uncertainties. Simulations show that the proposed approach is superior to the previous results in the situation where only delayed data are used.

Most video surveillance systems use both RGB and infrared cameras, making it a vital technique to re‐identify a person cross the RGB and infrared modalities. This task can be challenging due to both the cross‐modality variations caused by heterogeneous images in RGB and infrared, and the intra‐modality variations caused by the heterogeneous human poses, camera position, light brightness etc. To meet these challenges, a novel feature learning framework, hard pentaplet and identity loss network (HPILN), is proposed. In the framework existing single‐modality re‐identification models are modified to fit for the cross‐modality scenario, following which specifically designed hard pentaplet loss and identity loss are used to increase the accuracy of the modified cross‐modality re‐identification models. Based on the benchmark of the SYSU‐MM01 dataset, extensive experiments have been conducted, showing that the authors’ method outperforms all existing ones in terms of cumulative match characteristic curve and mean average precision.