Zhou Dong-hua

Diagnosis of sensor and actuator faults of a class of hybrid systems based on semi-qualitative method

On the basis of a three tank system, a semi-qualitative approach to diagnosis of sensor and actuator faults of a class of hybrid systems is proposed. Under the framework of hybrid bond graph, some improvements are further carried out. In the qualitative reasoning, an improved temporal causal graph with additional nodes is introduced. This extends the original method to diagnosis of sensor and actuator faults of hybrid systems. Finally, computer simulations are done which demonstrate the effectiveness of the proposed approach.

Event-Triggered Least Squares Fault Estimation with Stochastic Nonlinearities

Abstract In this paper, the event-triggered least squares state and fault estimation problem is investigated for a class of systems with stochastic nonlinearities. An event-triggered scheme is properly proposed whose main idea is to transmit the measurement output to a remote estimator only when a specified event condition is violated and an event is triggered. A filter is designed so as to minimize an upper bound of the filtering error covariance with event-triggered measurement transmissions and additive stochastic nonlinearities. By solving two sets of discrete matrix equations, the desired filter parameters are calculated recursively and thus the method is applicable for online computation. Both the state and fault estimation problems are handled within the same framework using the least squares method. A numerical simulation is exploited to illustrate the effectiveness of the proposed algorithm.

Adaptive control of DC-DC converters based on input equivalent disturbance

DC-DC converter is a class of variable-structure nonlinear time-varying systems, conventional control algorithm is difficult to achieve satisfactory steady and dynamic performance. Taking buck converter as a study object, this paper realizes an adaptive controller for DC-DC converter by online estimating the input equivalent disturbance caused by its inaccurate parameters by use of strong tracking filter, and by correcting control variables of generalized generic model control to compensate effects of time-varying parameters and unknown disturbance. Simulation results testify this adaptive DC-DC converter has a good robustness and control performance while input voltage, reference voltage, load or other parameter changes.

LMI-based robust filtering: A survey

A review of the recent developments of LMI-based robust filtering problems is presented, which include robust filtering problems for time-delay systems, stochastic systems, nonlinear systems, as well as multi-objective robust filtering problems. Robust filtering for special systems and conservatism reduction issues are also discussed. Finally, future research directions are pointed out.

Multiple model estimation represented by Bayesian networks

Multiple Model Estimation (MME) in hybrid systems, as a powerful approach to adaptive estimation, has been widely applied in a great deal of attention due to its unique power to handle problems with both structural and parametric uncertainties. In this paper, multiple well-known methods in MME are represented in the form of Bayesian Networks (BN), which is widely used in artificial intelligence. The discussion implies that MME may be a special case of BN.