Donald S. Reay

Switched reluctance motor control via fuzzy adaptive systems

This article presents the application of fuzzy adaptive systems to the problem of torque ripple reduction in a switched reluctance motor. Conventional methods for torque linearization and decoupling are reviewed briefly, as is the previous application, by the authors, of neural network based techniques. A solution based on the use of fuzzy adaptive systems is then described. Experimental measurements of the static torque production characteristics of a 4 kW, four-phase switched reluctance motor form the basis of simulation studies of this novel approach. The simulation results demonstrate the capability of fuzzy adaptive systems to learn nonlinear current profiles that minimize torque ripple. The use of fuzzy systems in this application has potential advantages where the incorporation of a priori information, expressed linguistically, is concerned. Experimental results illustrate the effectiveness of the approach. >

Online Modeling for Switched Reluctance Motors Using B-Spline Neural Networks

A novel online-modeling scheme for the switched reluctance motor (SRM) using a B-spline neural network (BSNN) is proposed in this paper. A 2-D BSNN is designed to learn the nonlinear-flux-linkage characteristic of an SRM online and in real-time. Torque, incremental inductance, and back-emf estimates can be derived from the BSNN after training. The scheme does not require a priori knowledge of the SRM electromagnetic characteristics. Simulation and experimental results show that the scheme has a good estimation performance and robustness at low to medium motor speed.

Torque Ripple Reduction in Switched Reluctance Motor Drives Using B-Spline Neural Networks

A switched reluctance motor torque ripple reduction scheme using a B-spline neural network (BSNN) is presented in this paper. Closed-loop torque control can be implemented using an on-line torque estimator. Due to the local weights updating algorithm of the BSNN, the appropriate phase current profile for torque ripple reduction can be obtained on-line in real time. It has good dynamic performance with respect to changes in torque demand. The scheme does not required high-bandwidth current controllers. Simulation and experimental results demonstrate the validity of the scheme.

Application of associative memory neural networks to the control of a switched reluctance motor

The application of an associative memory neural network to the problem of torque ripple minimisation in a switched reluctance motor is presented. Conventional techniques for torque linearisation and decoupling are reviewed, after which the application of neural techniques to the problem is described. An instrumented test rig based around a 4 kW IGBT converter and a four phase switched reluctance motor has been constructed. Results obtained experimentally and by simulation demonstrate the effectiveness of the approach. The neural network has been implemented using both digital signal processor and field programmable gate array technologies.<<ETX>>

Sensorless position detection using neural networks for the control of switched reluctance motors

For high performance position or torque control, or for many of the different possible approaches to torque ripple and acoustic noise reduction in a switched reluctance motor (SRM), position feedback is essential. However, optical position encoders add to the complexity and cost of SRMs, compromising some of their main advantages. The paper describes a novel method of sensorless position detection requiring no special converter or sensor circuitry, and which does not rely on accurate prior knowledge of the magnetic characteristics of the motor. The approach described is novel in two respects. Firstly, it does not rely on accurate prior knowledge of phase winding inductance but merely makes the assumption that it varies substantially as sin(N/sub r//spl theta/), where N/sub r/ is the number of rotor poles and /spl theta/ is rotor angle. Secondly, the approach learns from previous good estimates of position and, once it has done so, makes use of this knowledge where performance of the basic estimation algorithm degrades (principally at low speeds of rotation). The technique has been investigated in simulation and a hardware implementation is under development.

Adapting CMAC neural networks with constrained LMS algorithm for efficient torque ripple reduction in switched reluctance motors

This paper presents a novel approach to learning control in switched reluctance motors (SRM) for torque ripple reduction using a cerebellar model articulation controller (CMAC) neural network. The approach modifies the conventional LMS adaptive algorithm using a variable learning rate function over the rotor angle of the motor under control. The criteria and method for the development of current profiles suitable for use over a wide range of motor speeds are described. In particular, current profiles can be designed to possess desirable characteristics by selection of learning rate function with appropriate switching angles during the training of the network. The approach allows the generation of optimal current profiles in terms of minimizing torque ripple and copper loss as the motor operates at low speeds, and of minimizing torque ripple, copper loss and rate of change of current as the motor runs at high speeds. Experimental measurement of the torque production characteristics of a 4 kW, four-phase switched reluctance motor forms the basis of simulation studies of this approach. Substantial simulation results are reported and the performance of learned current profiles analyzed. These demonstrate that developing CMAC-based adaptive controllers following this approach affords lower torque ripple with high power efficiency, while offering rapid learning convergence in system adaptation.

CMAC and B-spline neural networks applied to switched reluctance motor torque estimation and control

This paper describes the application of cerebellar model articulation controller (CMAC) and B-spline neural networks to switched reluctance motor (SRM) torque estimation and control. Non-linear adaptive systems such as neural networks are well suited to learning the highly non-linear electromagnetic characteristics of the SRM for the purposes of linearisation and simplification of their control and a number of researchers have investigated their use in this context. CMAC and B-spline neural networks are particularly suited to this application area due to their potential for low-cost, high-speed implementation including the capability for real-time, on-line adaptation. CMAC and B-spline neural networks have successfully been applied both to torque ripple minimisation and to torque estimation in simulation and, implemented using FPGA technology, experimentally. This paper describes those applications with particular emphasis on the suitability of the CMAC and B-spline neural networks and gives details of their FPGA implementation.

High performance current control for switched reluctance motors with on-line modeling

This paper presents an improved current controller for a switched reluctance motor (SRM) drive. In the proposed method, a B-spline neural network was used to model the SRM and estimate back EMF and incremental inductance on-line in real-time. The on-line modeling scheme does not require a priori knowledge of SRMs electromagnetic characteristics. Based on the on-line estimated parameters, current control with an adjustable PI controller and a back EMF decoupling technique has been implemented. The performance of the current controller has been demonstrated in simulation and experimentally using a four-phase 8/6 550 W SRM.

On-line torque estimation in a switched reluctance motor for torque ripple minimisation

This paper considers torque ripple minimisation control for switched reluctance motors (SRMs) and presents a novel on-line approach to the estimation of instantaneous torque. An adaptive B-spline neural network is used to learn the non-linear flux linkage and torque characteristics of an SRM. The training of the B-spline neural network is accomplished on-line in real-time, and the system does not require a priori knowledge of the SRMs electromagnetic characteristics. The potential of the torque estimation method is demonstrated in simulation and experimentally using a 550 W 8/6 four-phase SRM operating in saturation, and it has been applied successfully to torque ripple minimisation.

Comments on "A new approach to adaptive fuzzy control: the controller output error method"

For original paper see ibid., vol 27, p. 686-91, Aug. 1997. In the above paper, a novel algorithm for adaptively updating the parameters of a fuzzy controller was proposed. The purpose of this letter is to point out that this algorithm, and its use, are well known. The authors of the above paper acknowledge the previous use of similar concepts, however this letter draws attention to a particularly clear description of the algorithm.