Jiang Jing-ping

Nonlinear internal-model control for switched reluctance drives

A nonlinear internal-model control (IMC) based on suitable commutation strategy for switched reluctance motors (SRMs) is proposed. In the commutation strategy, a fixed critical rotor position is defined as the commutation point, which results in reduced computation. Combined the simplicity of the feedback linearization control and the robustness of IMC structure, the proposed drive has excellent dynamic and static performances for the torque and current control. The scheme is analyzed, and some important properties are obtained. Simulations and experiments were carried out on a 7.5 kW four-phase SRM, and the results show that the ripple of the output torque is very low in spite of model-plant mismatches.

Design of the adaptive interacting multiple model algorithm

Adaptive interacting multiple model (AIMM) estimation is a method to improve the interacting multiple model (IMM) estimation. But some new problems appear in the AIMM, such as how to select the structure of the adaptive model set, how to inherit the different datum of the filters based on the old model set. In the paper some instructive conclusions and formulas of the design of the model set M and the model transition probability are presented to solve these problems. A new turn rate estimation method based on a pseudo-observation is given. Several new AIMM algorithms are put forward based on the estimation. Simulation results show the better performance of these new AIMM algorithms.

Robust control methods for PM synchronous motor

Robust controllers which are designed by employing discrete-time reaching law control (RLC) and internal-model control (IMC) are presented for permanent magnet synchronous motor (PMSM). They are to achieve accurate control performance in the presence of uncertainties and plant parameter variation. As the powerful technique to control the nonlinear and uncertain systems, IMC is applied to PMSM current control. The synchronous-frame PI-type IMC controller greatly improves the performances of the current loop and simplifies the design procedure. The RLC-based speed controller is proposed, and the design equations are derived in the discrete time since the discrete time analysis and design is more appropriate for the digital control applications. The robustness of RLC-based speed controller is analyzed, and the bounds of control parameters that ensure the drive stable are obtained. Simulation results verify the proposed control methods for PMSM.

Hierarchical reduction approach of the rough sets theory and its basis on the information theory

From the point of view of practical application, a hierarchical reduction approach of rough sets theory is proposed. In this approach, according to the acquisition mode, cost and real time requirement, the attributes are classified in different parts allocated to several layers. So the knowledge in information systems or decision systems can be presented hierarchically with multiple granularities in multiple layers. The reduction can be applied hierarchically to parts of attributes allocated to each layer. The basis of this approach is proved from information theory. Furthermore, the properties of the approach are discussed, such as practicability, rapidity and dynamic performance. Finally, the hierarchical reduction approach shows its validity in acquiring the control decision of a cement kiln.

Fuzzy linearization for nonlinear systems: a preliminary study

This paper proposes a novel control diagram for nonlinear systems, namely fuzzy linearization. On the basis of fuzzy reasoning, we build a set of fuzzy linear subsystems to linearize the original nonlinear system. Consequently, we design an optimal controller for every linear subsystem using the mature linear control theory. The control effect of each subsystem is composed via fuzzy reasoning to control the nonlinear system. Therefore, the design of any nonlinear systems can be simplified to the control problem of linear time-invariant systems. Compared to the existing methods such as the Taylor expansion and piecewise linearization, the proposed approach exhibits higher precision, better control performances and stronger robustness to system uncertainties.<<ETX>>

The design of a hybrid power filter based on variable structure control

Based on the analysis of a mathematical model with a space vector, this paper presents the algorithm of variable structure control for a kind of hybrid power filter, and the design method of the control parameters. The simulation results show that the control algorithm not only achieves high performances of suppressing the harmonics, but also can be easily implemented.

Modelling of switched reluctance motor based on variable structure fuzzy-neural networks

Switched reluctance motors (SRM) are almost always operated within the saturation region for a very large operation region. This yields very strong nonlinearities, which makes it very difficult to derive a comprehensive mathematical model for the behavior of the machine. This paper presents the variable structure fuzzy-neural networks model of SRM. Based on the Takagi-Sugeno fuzzy-neural networks, a variable structure and step learning arithmetic was presented. Then the fuzzy-simulation results show that this method is more precise and less time-consuming for convergence than BP neural networks model.

Application of evolution strategy in cluster analysis

K-means clustering has two disadvantages, one is easily trapped in local minimum, and the other is difficultly to determine the number of clusters K. To address the problems, this paper proposes 3 new K-means algorithms based on Evolution Strategy. The first individual represents a kind of cluster scheme, and the second represents cluster centers. They can find optimal clustering if K is given. While the third individual adds K on the basis of the first one, it can optimize cluster center and K simultaneously. They all own a simple coding scheme and small population. These algorithms are applied to cluster Fishers iris data set and work very well, especially when a priori knowledge is insufficient.

The optimal planning of an active power line conditioner in a network using evolutionary computation

The impact of voltage harmonics on a power system can be minimized by using an active power line conditioner (APLC) to inject distortion-canceling currents. This paper presents a new method that is based on evolutionary computation to decide where an APLC should be placed for maximum performance. The simulation result verifies the validity of the new method.

Hierarchical reduction approach of rough sets to incomplete systems

This paper proposes a hierarchical reduction approach of rough sets to incomplete information systems. Simulating the recognition laws of a human being, this approach presents knowledge hierarchically with multiple granularities at multiple layers. And the reduction can hierarchically be applied to part of attributes allocated at each layer instead of all attributes at only one layer. The hierarchical reduct, derived by a hierarchical reduction, can solve the problem with coarser granularity at a lower layer, avoiding solving a problem with finer granularity at a deeper layer, where the incompleteness perhaps exists. This approach can make the knowledge reduction simple and fast. Both hierarchical reduction and reduct are applicable to practical problem solving.