Qing Hu

Bio: Qing Hu is an academic researcher from Shenyang University of Technology. The author has contributed to research in topics: Fuzzy control system & Motion control. The author has an hindex of 4, co-authored 12 publications receiving 35 citations.

Position Synchronized Control of Dual Linear Motors Servo System Using Fuzzy Logic

Abstract: This paper presents a position synchronized controller for dual linear motors servo system in gantry moving type milling machining centers. To guarantee the requirement for high accuracy in synchronous tracking, the same position feedback controller is used to single linear motor position servo axis independently. The acceleration feedforward control and electrical cross-coupled control algorithms based on fuzzy logic are applied to dual linear motor position servo axes to counteract the influence of mechanical coupling rapidly, and then meet dynamic synchronized performance in machining. The simulation results show that the proposed control methods are effective and yield superior performance.

A novel fuzzy PID velocity control of linear elevator driven by the permanent magnet linear synchronous motor

Abstract: A correction factor fuzzy-PID control method is adopted in order to solve the vibration of elevator cab because of the edge effect in the linear elevators driven by the permanent magnet linear synchronous motor (PMLSM). The fuzzy control has the characteristics of being not completely dependent on the mathematics model, but the fuzzy control rules are obtained by experiences of authors or knowledge of experts. The correction factor fuzzy-PID can adjust the fuzzy rules, and so it has adaptability. This method can reduce the vibration influence of edge effect on elevator cab. The simulations show that system has good tracking ability and inhibit the fluctuation of the thrust resulting from edge effect. So the elevator cab will become more comfortable and suitable for people.

Position Control of Linear Servo System Using Intelligent Feedback Controller

Abstract: This paper presents a new position tracking control strategy that meets the position tracking performance and the closed loop robustness to external disturbance and model parameters variations without parameter identification. In order to achieve the desired input-output tracking and disturbance rejection performance independently, a two-degree-of-freedom (2DOF) internal model control (IMC) is introduced in controller structure. Furthermore, based on fuzzy logic, the parameter of the feedback controller is adjusted on-line to improve robustness. The simulation results on a direct-drive permanent magnet linear synchronous motor (PMLSM) show that proposed method is effective on improving system robustness.

Fuzzy sliding mode control of maglev guiding system based on feedback linearization

Abstract: The magnetic levitation device which has non-contact characteristics, is used to replace the traditional guiding equipment, it would be applied in linear elevator to maintain a constant air gap of linear motor, and to eliminate mechanical vibration. In order to solve nonlinear problems of maglev device, feedback linearization control and fuzzy sliding mode control method is adopted to control the air gap of magnetic levitation device. Simulation results show that the control strategy has strong robustness.

Adaptive Fuzzy Sliding Mode Controller for Linear Elevator Using Permanent Magnet Linear Synchronous Motor

Abstract: In order to improve the tracking and robust performances of a linear elevator driving by permanent magnet linear synchronous motor (PMLSM), a stable adaptive fuzzy sliding mode controller (AFSMC) for a linear motor speed loop control is presented, The fuzzy system is applied to approximate the unknown nonlinear function of plant, and then the estimation value of nonlinear function is used to design the control variable. Analysis of simulations reveals that proposed method is robust in the presence of uncertainties and bounded external disturbances.

Adaptive Control

Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

A particle swarm optimization approach for optimum design of PID controller in linear elevator

Abstract: With the rapid development of permanent magnet linear synchronous motor (PMLSM) application in elevators, the design of PID controllers of these systems are very important. According to the ability of Particle Swarm Optimization (PSO) in solving nonlinear problems, in this paper a PSO algorithm has been used in the controller design of PMLSM. The PMLSM model has been developed in MATLAB/Simulink and the PSO algorithm has been applied to PID controller design. The algorithm has been tested on a case study and the results have been provided with a brief discussion at the end.

Endpoint perfect tracking control of robots — A robust non inversion-based approach

Abstract: This paper presents a simple and robust non inversion-based perfect tracking control (RNIBPTC) strategy for robot manipulators. The proposed approach is capable to eliminate the environmental problems arising from classic feedforward control design and so guarantees an appropriate level of robustness of control system to uncertainties including external disturbances, unmodeled dynamics, friction force and variation of payload. Extensive simulation results performed using a two degree-of-freedom actuated elbow robot prove the effectiveness of the proposed approach. The results are also compared to those obtained from Internal Model Control approach. Using free model of system in control law design is a considerable point in the field of robot manipulator control.

Pareto optimum control of a 2-DOF inverted pendulum using approximate feedback linearization and sliding mode control

Abstract: In this paper, a cart-type inverted pendulum is controlled using combining of two methods of approximate feedback linearization and sliding mode control. Both position of the cart and angular posit...

Fuzzy control with fuzzy basis function neural network in magnetic bearing system

Abstract: The paper propose the fuzzy control with recurrent fuzzy basis function neural network (RFBFNN) height control of magnetic bearing system. The magnetic bearing is a very unstable nonlinear system. Fuzzy control need not accurate mathematical model to have good output response for nonlinear systems. However, the fuzzy control cannot guarantee the stability and convergence. Neural networks also need not a precise mathematical model to describe the nonlinear systems. Neural network training is time consuming and unsuitable in real-time control. This paper uses the fuzzy control with the fuzzy basis function neural network to control the magnetic bearing system. The fuzzy basis function neural network use the gradient method for the system error and error derivative such that the output response has good response.