This paper proposes a novel robust speed control method for induction motor drives based on a two-layered neural network plant estimator (NNPE) and a two-layered neural network PI controller (NNPIC). The NNPE is used to provide a real-time adaptive estimation of the unknown motor dynamics. The widely used projection algorithm is used as the learning algorithm for these neural networks to automatically adjust the parameters of the NNPIC and to minimize the differences between the motor speed and the speed predicted by the NNPE. The simulation and experimental results demonstrate that the proposed robust control scheme can improve the performance of an induction motor drive and reduce its sensitivity to parameter variations and load disturbances.

Model reference neural network controller for induction motor speed control

The authors develop a fully digital adaptive velocity controller for the permanent-magnet synchronous motor. The critical issues addressed during the development of this controller include discretization and global linearization of the nonlinear motor system, nonlinearities in the inverter, nonminimum phase behavior due to sampling, sampling rate restrictions, robust mechanical state estimation, persistent excitation of the mechanical states, mechanical parameter estimation and validation of these estimates, and unmodeled dynamics. The resulting adaptive controller is implemented in real time using a computer system based on the Motorola 68020 microprocessor. Experimental results demonstrate that the adaptive controller maintains the desired control over velocity in the presence of varying mechanical parameters. >

Real-time adaptive control of the permanent-magnet synchronous motor

This paper describes the design and the implementation of a self-tuning integral-proportional (IP) speed controller for a rolling mill DC motor drive system, based on a 32-bit floating point digital signal processor (DSP)-TMS 320C30. To get a better transient response than conventional proportional-integral (PI) and/or integral-proportional (IP) speed control in the presence of transient disturbance and/or parameter variations, an adaptive self-tuning IP speed control with load torque feedforward compensation was used. The model parameters, related to motor and load inertia and damping coefficient, were estimated online by using recursive extended least squares (RELS) estimation algorithm. On the basis of the estimated model parameters and a pole-placement design, a control signal was calculated. Digital simulation and experimental results showed that the proposed controller possesses excellent adaptation capability under parameter change and a better transient recovery characteristic than a conventional PI/IP controller under load change. >

DSP-based self-tuning IP speed controller with load torque compensation for rolling mill DC drive

This study presents a new self-tuning PI speed controller with load torque observer and feedforward compensation based on neural network identification for an induction motor. A two-layer neural estimator is also used to provide a real-time adaptive estimation of the unknown motor dynamics. The widely used projection algorithm is used as the learning algorithm for this network, to minimize the difference between the motor's actual response and that predicted by the neural estimator. The proposed neural estimator uses this learning to adjust PI speed controller with a load torque observer to generate the control signal online, thereby bringing the motor output to a desired reference trajectory. The theoretical analysis, simulation and experimental results demonstrate the proposed scheme's effectiveness.

Self-tuning control of induction motor drive using neural network identifier

A DSP-based fully digital drive for AC servo application is presented in this paper, in which a high performance DSP is used and all the control loops (speed and current) are implemented with software. Taking advantage of the fast calculation ability of DSP, this paper also designs an on-line self-tuning PI controller, which can detect the mechanical inertia time of this drive and realize the automatic adjustment of speed loop PI controller parameter. The simulation and experiment results have demonstrated that the drive has a good dynamic and robust performance.

A DSP-based fully digital PMSM servo drive using on-line self-tuning PI controller

The problem of designing an adaptive controller for dc drives operating under varying load conditions is addressed. A robust self-tuning controller that can track a constant reference and reject constant load disturbances is developed. The controller is shown to have an adaptive integral-proportional (IP) structure. The proposed controller is implemented in a microcomputer and a dc motor is controlled under varying load and operating conditions. Experimental results show that the controller possesses excellent adaptation capability as well as transient recovery under load changes.

Ramachandran Balasubramanian

Papers

A Microcomputer-Based Self-Tuning IP Controller for DC Machines