• 전기학회논문지
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• 제56권1호
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• pp.71-80
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• 2007

A control approach for the sensorless speed control of interior permanent magnet synchronous motor(IPMSM) based on adaptive integral the binary is proposed. With a main loop regulator and an auxiliary loop regulator, the binary observer has a property of the chattering alleviation in the constant boundary layer. However, the width of the constant boundary limits the steady state estimation accuracy and robustness. In order to improve the steady state performance of the binary observer, the binary observer is formed by adding extra integral augmented switching the hyperplane equation. By mean of integral characteristics, the rotor speed can be finely estimated and utilized for a sensorless speed controller for IPMSM. The proposed adaptive integral binary observer applies an adaptive scheme, because the parameters of the dynamic equations such as the machine inertia or the viscosity friction coefficient is not well known and these values can be easily changed generally during normal operation. Therefore, the observer can overcome the problem caused by using the dynamic equations, and the rotor speed estimation is constructed by using the Lyapunov function. The experimental results of the proposed algorithm are presented to demonstrate the effectiveness of the approach.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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• pp.162-169
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• 1999

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital Signal Processors. Digital signal processors DSPs. are micro-processors that are particularly developed for variables. Digital version of most advanced control algorithms can be defined as sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of their prices. These features make DSPs a biable computatinal tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for implementation of real-time control of robot system by the simulation and experiment.