A Single Neuron Self-Adaptive PID Controller of Brushless DC Motor

Wang, Maoli; Cheng, Guanghe; Kong, Xiangxin

doi:10.1109/icmtma.2011.70

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Shown in Fig. 1, y(t) is the given value from system, y(t) is the actual output value from system, point and actual output values constitute control deviation (t), e(t) = r(t)-y(t); e(t)as a PID controller input, u(t) as regulator controlled object output and input [6]. In the closed-loop motor control, in general, we utilize the PI regulator.…”

Section: Fig1 Pid Algorithmmentioning

confidence: 99%

Autonomous Underground Azimuth Measuring Drive System Based on MEMS Gyroscope

Liu

et al. 2014

AMR

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MEMS inertial sensors using underground azimuth measurement, with small size, high accuracy, low cost and good stability, the control and drive system is very important. This paper presents the autonomous underground azimuth measurement drive system based on MEMS gyroscope. Using MEMS gyroscope as a measuring element, driven by a brushless DC motor, using a combination of photoelectric encoder, according to the underground azimuth measurement principle, this paper proposed a autonomous measurement method, discussing the design principles of the program, the components of the system, and the communication problems between the gyroscope and other part. Overcoming the high cost of traditional logging tool and the complex control problem, experimental results verify that the system is able to achieve underground azimuth measurement purposes. Error in 1.5°or less, with outstanding engineering applications.

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Section: Fig1 Pid Algorithmmentioning

confidence: 99%

Autonomous Underground Azimuth Measuring Drive System Based on MEMS Gyroscope

Liu

et al. 2014

AMR

View full text Add to dashboard Cite

show abstract

“…In [3], aiming at the transmission delay degradation effect in NCSs, a method was used to configure the control signal by aggregating the weighted PID outputs of all zones to facilitate a gradual bump-less control transfer between two adjacent control zone, which is based on sectionalizing the error signal of the step response into three functional zones and fuzzy membership factors. Single neuron self-adaptive PID control was presented in [4] for nonlinear brushless DC motor system, by which it is observed that the static and dynamic performance of single neuron self-adaptive PID controller is superior to the normal PID controller. In [5], the authors combined smith predictor with neural adaptive PID control to effectively restrain the impact of network delays with simple structure and easy for actualization.…”

Section: Introductionmentioning

confidence: 99%