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As a key feature of networked control systems (NCSs), the time delays induced by communication medium sharing and data exchange over the system components could largely degrade the NCS performances or may even cause system instability, and thus, it is of critical importance to reduce time delays within NCSs. This paper studies the time-delay reduction problem in distributed NCSs and presents a dual-way data scheduling mechanism for time-delay reductions in delay-bounded NCSs with time-varying delays. We assess the time delays and their influences on the NCSs first with various delay factors being considered and then describe a one-way scheduling mechanism for network-delay reductions in NCSs. Based upon such a method, a dual-way scheduling algorithm is finally proposed for distributed NCSs with different types of transmitted data packets. Experiments are conducted on a remote teaching platform to verify the effectiveness of the proposed dual-way scheduling mechanism. Results demonstrate that, with the stability time-delay bound considered within the scheduling process, the proposed mechanism is effective for NCS time-delay reductions while addressing the stability, control accuracy, and settling time issues efficiently. Such a proposed mechanism could also be implemented together with some other existing control algorithms for time-delay reductions in NCSs. Our work could provide both useful theoretical guidance and application references for stable tracking control of delay-bounded NCSs.

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This paper proposes a practically executable path planning method, namely, Pheromones-RRT(PRRT), for robots with a large joint range in a complex environment. To inter-activate with the real world, the point cloud is utilized as the scene information, while for sampling, the pheromones approach is designed to describe the pheromone content carried by each sampling point. During the sampling process, random sampling nodes are performed with a probability of ε, or those nodes with the highest pheromone content in the current sampling tree are selected with a probability of 1-ε and sampled in their neighborhood. To avoid the local minimum problem, the concept of pheromone volatile factor (PVF) is proposed, while in the expansion, double trees are also generated by PRRT in both cartesian and configuration spaces to improve the speed of the algorithm. The pheromone accumulation enables PRRT to certain learning abilities, reducing the randomness of the sampling process. Simulation results show that the proposed method can effectively plan an optimal obstacle avoidance path with satisfactory performances as compared with the RRT-Connect.