Showing papers by "Kang-Zhi Liu published in 2013"

Optimal quantization feedback control with variable discrete quantizer

Abstract: Networked control systems (NCSs) have been receiving much attention in order to improve control performance in the field of in remote robot operation, surgery and some operations. In the NCSs, it is important to quantize necessary signals for control over a limited network channel for the sake of prevention from transmitting a large amount of data. This paper addresses a quantized feedback control system with a variable discrete quantizer. In the system, both input and a parameter of the quantizer are optimized online with the help of model predictive control (MPC). In our approach, constraints on input/output, the parameter of the quantizer and other physical and/or logical constraints can be explicitly taken into account while guaranteeing optimality. The optimization problem is reduced to a mixed integer quadratic programming. Experimental results are demonstrated to verify the effectiveness of the proposed method.

Adaptation of stator and rotor resistances for the vector control of induction motors: A method based on reduced order current observer

Abstract: This paper deals with the parameter adaptation problem of induction motors. It is well understood that the resistances of the stator and the rotor vary significantly in continuous operation. So, to prevent control performance degradation due to parameter error of the induction motor, online parameter adaptation is required. This paper proposes a new method, which is composed of two parts: a stator current observer and a parameter updating law. An outstanding feature of this method is that the rotor flux observer is not used. Instead, the flux reference value is used in the stator current observer which greatly simplifies the implementation. Simulations show that this method works satisfactorily.

Hysteresis compensation control for reluctance actuator force using neural network

Abstract: Reluctance actuator has a unique property of small volume, low current and can produce great force. So it is very suitable for high-precision and high acceleration control applications such as the next-generation semiconductor lithography equipment. However, the hysteresis characteristics of reluctance actuator cannot be ignored in high-precision control. One of the major challenges of reluctance actuators is the predictability of the force, which has a nonlinear relationship with respect to the current and position and is directly related to the final accuracy in the nanometer range. Therefore, it is necessary to study the control method for the reluctance force. This paper proposes two hysteresis compensation control configurations for the reluctance force using the multilayer neural network (MNN). The multilayer neural network is used as a learning machine of nonlinearity. The advantage and disadvantage of each method as well as their application conditions are investigated extensively through simulations. The simulations are conducted on the E/I Core reluctance actuator model and the results show that the proposed methods are effective in overcoming the hysteresis and promising in high-precision and high acceleration control applications.

Optimal control for networked control systems with stochastic data dropout

Abstract: Data dropout in networked control systems (NCSs) is unavoidable. The desired performance may not be achieved if the data dropout is not considered. Therefore, a controller in the NCS needs to be designed which takes the data dropout into account. In this paper, the data dropout is assumed to follow a stochastic process. For the NCS, a model predictive control (MPC) method is proposed for the design of an optimal input sequence. The effectiveness of the proposed method is verified through simulations and experiments.