The use of communication networks in control loops has gained increasing attention in recent years due to its advantages and flexible applications. The network quality-of-service (QoS) in those so-called networked control systems always fluctuates due to changes of the traffic load and available network resources. This paper presents an intelligent scheduling controller design approach for a class of NCSs to handle network QoS variations. The sampling period and control parameters in the controller are simultaneously scheduled to compensate for the network QoS variations. The estimation of distribution algorithm is used to optimize the sampling period and control parameters for better performance. Compared with existing networked control methods, the controller has better ability to compensate for the network QoS variations and to balance network loads. Simulation results show that the plant setting time with the intelligent scheduling controller is reduced by about 64.0% for the medium network load and 49.1% for high network load and demonstrate the effectiveness of the proposed approaches.

This paper deals with a flexible macro-micro manipulator system, which includes a long flexible manipulator and a relatively short rigid manipulator attached to the tip of the macro manipulator. A flexible macro manipulator possesses the advantages of wide operating range, high speed, and low energy consumption, but the disadvantage of a low tracking precision. The macro-micro manipulator system improves tracking performance by compensating for the endpoint tracking error while maintaining the advantages of the flexible macro manipulator. A trajectory planning scheme was built utilizing the task space division method. The division point is chosen to optimize the error compensation and energy consumption for the whole system. Then movements of the macro-micro manipulator can be determined using separate inverse kinematic models. Simulation results for a planar 4-DOF macro-micro manipulator system are presented to show the effectiveness of the control system.