In this paper an artificial neural network, which realizes a nonlinear adaptive control algorithm, has been applied in a control system of variable speed generating system. The speed is adjusted automatically as a function of load power demand. The controller employs a single layer neural network to estimate the unknown plant nonlinearities online. Optimization of the controller is difficult because the plant is nonlinear and no stationary. Furthermore, it deals with the situation where the plant becomes uncontrollable without any restrictive assumptions. In contrast to previous work [1] on the same subject, the number of neural networks has been reduced to only one network. The number of the neurons in a network structure as well as choosing certain design parameters was specified a priori. The computer test results have been presented to show performance of proposed neural controller.

In this paper, the issue related to control of the plant with nonconstant parameters is addressed. In order to assure the unchanged response of the system, an adaptive state feedback speed controller for permanent magnet synchronous motor is proposed. The model-reference adaptive system is applied while the Widrow-Hoff rule is used as adjustment mechanism of controller’s coefficients. Necessary modifications related to construction of the cost function and formulas responsible for adjustment of state feedback speed controller’s coefficients are depicted. The impact of adaptation gain, which is the only parameter in proposed adjustment mechanism, on system behaviour is experimentally examined. The discussion about computational resources consumption of the proposed adaptation algorithm and implementation issues is included. The proposed approach is utilized in numerous experimental tests on modern SiC based drive with nonconstant moment of inertia. Comparison between adaptive and nonadaptive control schemes is also shown.

The paper presents a three-phase grid-tied converter operated under unbalanced and distorted grid voltage conditions, using a multi-oscillatory current controller to provide high quality phase currents. The aim of this study is to introduce a systematic design of the current control loop. A distinctive feature of the proposed method is that the designer needs to define the required response and the disturbance characteristic, rather than usually unintuitive coefficients of controllers. Most common approach to tuning a state-feedback controller use linear-quadratic regulator (LQR) technique or pole-placement method. The tuning process for those methods usually comes down to guessing several parameters. For more complex systems including multi-oscillatory terms, control system tuning is unintuitive and cannot be effectively done by trial and error method. This paper proposes particle swarm optimization to find the optimal weights in a cost function for the LQR procedure. Complete settings for optimization procedure and numerical model are presented. Our goal here is to demonstrate an original design workflow. The proposed method has been verified in experimental study at a 10 kW laboratory setup.

This paper presents a state feedback controller (SFC) for position control of PMSM servo-drive. Firstly, a short review of the commonly used swarm-based optimization algorithms for tuning of SFC is presented. Then designing process of current control loop as well as of SFC with feedforward path is depicted. Next, coefficients of controller are tuned by using an artificial bee colony (ABC) optimization algorithm. Three of the most commonly applied tuning methods (i.e. linear-quadratic optimization, pole placement technique and direct selection of coefficients) are used and investigated in terms of positioning performance, disturbance compensation and robustness against plant parameter changes. Simulation analysis is supported by experimental tests conducted on laboratory stand with modern PMSM servo-drive.

In this paper an application of extended Kalman filter (EKF) for estimation and attenuation of periodic disturbance in permanent magnet synchronous motor (PMSM) drive is investigated. Most types of disturbances present into PMSM drive were discussed and described. The mathematical model of the plant is presented. Detailed information about the design process of the disturbance estimator was introduced. A state feedback controller (SFC) with feedforward realizes the regulation and disturbance compensation. The theoretical analysis was supported by experimental tests on the laboratory stand. Both time- and frequency-domain analysis of the estimation results and angular velocity were performed. A significant reduction of velocity ripple has been achieved.