Conference Paper

Low Speed Control of PMAC Servo System Based on Reduced-order Observer

Dept. of Electr. Eng. & Autom., Harbin Inst. of Technol.
DOI: 10.1109/IROS.2006.282445 Conference: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006, October 9-15, 2006, Beijing, China
Source: DBLP

ABSTRACT

Incremental encoders are popular for detecting angular position and speed. However, the conventional incremental encoder-based methods for estimating speed are prone to poor performance at low speed where the encoder output rate is correspondingly low, leading to a number of research efforts in improved low-speed detection algorithms. To improve speed control performance, a speed detection method based on reduced-order observer is presented in this paper. The observer can interpolate speed and position which is the integration of speed and compared with real data from the encoder when the encoder pulse is detected between encoder pulses. Furthermore, inertia identification based on recursive extended least square algorithm is presented to reduce sensitivity of the speed estimation. Experimental results show that low speed control performance using the reduced-order order is superior to that of conventional one

0 Followers
 · 
5 Reads
  • [Show abstract] [Hide abstract]
    ABSTRACT: Most servo control systems generally adopt incremental optical encoders for speed detection when considering cost and performance requirements. For a fixed sampling period, this kind of encoder along with the generally used so-called M method, may degrade the response or even cause the system to become unstable in a low-speed operating region because of the resulting speed detection delay. In this article, a reference model improves low-speed responses; parameter identification by recursive least square error algorithm overcomes the problem of parameter variations and an adaptive proportional-integral control strategy based on the parameter identification results further justifies the proposed method. A digital signal processor based permanent magnet synchronous motor drive will be used to carry out the experimental results, which show the effectiveness of the proposed method.
    No preview · Article · Apr 2011 · Electric Power Components and Systems
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper introduces a novel instantaneous speed detection method for permanent magnet synchronous motors (PMSMs). High detection precision is achieved by using a low resolution encoder. First, instantaneous speed and disturbance torque are estimated by a Luenberger observer based on the motion model of PMSM. Then, the estimation errors are corrected by angular position, which is obtained by synchronous sampling method (SSM) or asynchronous sampling method (ASM) according to different speed. Furthermore, in order to reduce the influence of load variation on instantaneous speed observer, load inertia is identified using the estimated values of speed and disturbance torque, and then the parameters of speed observer are adjusted according to load inertia. Compared with traditional speed observer, the method mentioned above eliminates the position detection error and delay time. So detection precision of instantaneous speed is improved largely especially in low speed region.
    No preview · Article · Apr 2011 · Diangong Jishu Xuebao/Transactions of China Electrotechnical Society
  • [Show abstract] [Hide abstract]
    ABSTRACT: The mechanical drivetrain dynamics of electric vehicles can have a detrimental effect on the performance of the vehicle speed controller. It is common for the speed measurement from the motor encoder to be used for the vehicle speed feedback, after taking into account the gear ratio, but it is not valid to assume that motor and vehicle speeds are equal during transient conditions. In this study it is shown how the vehicle driveability can be greatly improved if estimates of vehicle speed and mass are obtained. Estimates of vehicle speed and mass have been realised using a Kalman filter (KF) and a recursive least-squares estimator, and validated with experimental results. The study also shows the importance of finding the most optimal process noise matrix Q for the KF, this has been carried out using a genetic algorithm, with the estimation accuracy then compared with varying vehicle mass.
    No preview · Article · Sep 2013 · IET Electrical Systems in Transportation