A nonlinear adaptive state feedback input-output linearizing control is designed for a fifth-order model of an induction motor which includes both electrical and mechanical dynamics under the assumptions of linear magnetic circuits. The control algorithm contains a nonlinear identification scheme which asymptotically tracks the true values of the load torque and rotor resistance which are assumed to be constant but unknown. Once those parameters are identified, the two control goals of regulating rotor speed and rotor flux amplitude are decoupled, so that power efficiency can be improved without affecting speed regulation. Full state measurements are required. >

Adaptive input-output linearizing control of induction motors

A controller is proposed for the robust backstepping control of a class of general nonlinear systems using neural networks (NNs). A tuning scheme is proposed which can guarantee the boundedness of tracking error and weight updates. Compared with adaptive backstepping control schemes, we do not require the unknown parameters to be linear parametrizable. No regression matrices are needed, so no preliminary dynamical analysis is needed. One salient feature of our NN approach is that there is no need for the off-line learning phase. Three nonlinear systems, including a one-link robot, an induction motor, and a rigid-link flexible-joint robot, were used to demonstrate the effectiveness of the proposed scheme.

Robust backstepping control of nonlinear systems using neural networks

The torque pulsations in switched reluctance motors (SRMs) are relatively higher compared to sinusoidal machines due to the doubly salient structure of the motor. The magnetization pattern of the individual phases together with the T-i-/spl theta/ characteristics of the motor dictate the amount of torque ripple during operation. Both machine design and electronic control approaches have been used to minimize the torque ripple in SRMs. This paper presents an extensive review of the origin of torque ripple and the approaches adopted over the past decade to minimize the torque ripple. A hybrid torque-ripple-minimizing controller that incorporates the attractive features of some of the techniques developed in the past decade is presented along with simulation and experimental results.

Minimization of torque ripple in SRM drives

Higher torque ripple is one of the few drawbacks of switched reluctance motor (SRM) drives which otherwise possess excellent characteristics for applications in many commercial drives. This paper begins with an extensive review of torque ripple reduction methods that appear in the literature and then presents a new strategy of PWM current control for smooth operation of the drive. This method includes a current control strategy during commutation when torque ripple minimization is of utmost importance.

Torque ripple minimization in switched reluctance motor drives by PWM current control

In this article we review the recent advances in iterative learning control (ILC) for nonlinear dynamic systems. In the research field of ILC, two categories of system nonlinearities are considered, namely, the global Lipschitz continuous (GLC) functions and local Lipschitz continuous (LLC) functions. ILC for GLC systems is widely studied and analysed using contraction mapping approach, and the focus of recent exploration moves to application problems, though a number of theoretical issues remain open. ILC for LLC systems is currently a hot area and the recent research focuses on ILC design and analysis by means of Lyapunov approach. The objectives of this article are to introduce recent development and advances in nonlinear ILC schemes, highlight their effectiveness and limitations, as well as discuss the directions for further exploration of nonlinear ILC.

A survey on iterative learning control for nonlinear systems

Motivated by technological advances in power electronics and signal processing, and by the interest in using direct drives for robot manipulators, we investigate the control problem of high-performance drives for switched reluctance motors (SRM's). SRM's are quite simple, low cost, and reliable motors as compared to the widely used dc motors. However, the SRM presents a coupled nonlinear multivariable control structure which calls for complex nonlinear control design in order to achieve high dynamic performances. We first develop a detailed nonlinear model which matches experimental data and establish an electronic commutation strategy. Then, on the basis of recent nonlinear control techniques, we design a state feedback control algorithm which compensates for all the nonlinearities and decouples the effect of stator phase currents in the torque production. The position dependent logic of the electronic commutator assigns control authority to one phase, which controls the motion, while the remaining phase currents are forced to decay to zero. Simulations for a direct drive, single link manipulator with the SRM are reported, which show the control performance of the algorithm we propose in nominal conditions and test its robustness versus the most critical parameter uncertainties of payload mass and stator resistance.

Feedback linearizing control of switched reluctance motors

We address the control design problem of trajectory tracking for multiple link direct-drive robots using switched reluctance motors (SRMs) as joint actuators. The nonlinear model is composed of both a mechanical and an electrical subsystem accounting for link dynamics and actuator dynamics, respectively. The control inputs for the robot are the voltages applied to the SRMs and the joint torques are the coupling between the two subsystems. We design a state feedback control algorithm which compensates for the nonlinearities and decouples the link motions. Simulations for a two link robot are reported which indicate the performance of our control algorithm when the payload mass is uncertain.

S. Peresada

Papers

Adaptive input-output linearizing control of induction motors

Feedback linearizing control of switched reluctance motors

A feedback linearizing control for direct-drive robots with switched reluctance motors