Yi Chai

L-infinity event-triggered networked control under time-varying communication delay with communication cost reduction

Abstract A novel L - infinity event-triggered networked control scheme under time-varying communication delay is proposed. The control objective is to maintain L - infinity input to state stability while minimizing triggering and communication overhead under uncertainties. The event triggering is asynchronous for satisfying each subsystem performance requirement individually, and the event triggering strategy is adaptive to the variable communication delay to reduce the communication cost without degrade of system performance (input to state stability). The relations between the communication delay and the event triggering conditions are investigated and related bounds are derived analytically. An example of electrical power system frequency control task is used to illustrate the effectiveness of the proposed scheme in terms of achieving identical control performance with lower communication overhead.

BLDC motor speed control system fault diagnosis based on LRGF neural network and adaptive lifting scheme

Brushless DC (BLDC) machines are found increasing use in applications that demand high and rugged performance. In some critical circumstance, such as aerospace, the motor must be highly reliable. In this context, a novel model-based fault diagnosis system is developed for brushless DC motor speed control system. Under the consideration of the complexity of characterizing the dynamic of BLDC motor control system with analytic expression, a LRGF neural network (LRGFNN) with pole assignment technique is carried out for modeling the system. During the diagnosis process, fault signal of the motor is isolated with LRGFNN online. Meanwhile, adaptive lifting scheme and adaptive threshold method are presented for detecting the faults from the isolated fault signal under the existence of mechanical error and electrical error. The effectiveness of the diagnosis system is demonstrated in the simulation of electrical and mechanical fault in the motor. The detection of the incipient fault is also given.

Energy Function-Based Model Predictive Control With UPFCs for Relieving Power System Dynamic Current Violation

This paper suggests a novel dynamic protection/control scheme with unified power flow controller (UPFC) to prevent transmission system overload. A transient energy function (TEF)-based model predictive control (MPC) methodology is developed to reduce control cost and enhance power grid resilience. First, a TEF and an associated cost function of UPFCs are constructed based on ac dynamic model with transmission over-current tripping model. Then the derived control constraint for MPC exploits the non-positive time derivative of the TEF with the accommodated admittance, which are optimally regulated by UPFC inputs, to mitigate dynamic current violation based on sensitivity analysis. With the proposed control method, the system can be driven to a stable state without current violation even if part of the UPFCs fails. Finally, the simulation results of 14 bus test case and 118 bus test case demonstrate the effectiveness of our proposed method, as well as the advantage over conventional MPC method.