Lech M. Grzesiak

Particle Swarm Optimization of the Multioscillatory LQR for a Three-Phase Four-Wire Voltage-Source Inverter With an

This paper presents evolutionary optimization of the linear quadratic regulator (LQR) for a voltage-source inverter with an LC output filter. The procedure involves particle-swarm-based search for the best weighting factors in the quadratic cost function. It is common practice that the weights in the cost function are set using the guess-and-check method. However, it becomes quite challenging, and usually very time-consuming, if there are many auxiliary states added to the system. In order to immunize the system against unbalanced and nonlinear loads, oscillatory terms are incorporated into the control scheme, and this significantly increases the number of weights to be guessed. All controller gains are determined altogether in one LQR procedure call, and the originality reported here refers to evolutionary tuning of the weighting matrix. There is only one penalty factor to be set by the designer during the controller synthesis procedure. This coefficient enables shaping the dynamics of the closed-loop system by penalizing the dynamics of control signals instead of selecting individual weighting factors for augmented state vector components. Simulational tuning and experimental verification (the physical converter at the level of 21 kVA) are included.

LC

In computerized numerical control (CNC) systems, the communication bus between the controller and axis servo drives must offer high bandwidth, noise immunity, and time determinism. More and more CNC systems use real-time Ethernet protocols such as Ethernet Powerlink (EPL). Many modern controllers are closed costly hardware-based solutions. In this paper, the implementation of EPL communication bus in a PC-based CNC system is presented. The CNC system includes a PC, a software CNC controller running under Linux Real-Time Application Interface real-time operating system and servo drives communicating via EPL. The EPL stack was implemented as a real-time kernel module. Due to software-only implementation, this system is a cost-effective solution for a broad range of applications in machine control. All software systems are based on GNU General Public License or Berkeley Software Distribution licenses. Necessary modifications to the EPL stack, Linux configurations, computer basic input/output system, and motherboard configurations were presented. Experimental results of EPL communication cycle jitter on three different PCs were presented. The results confirm good performance of the presented system.

Output Filter

This paper presents constrained state feedback speed control of a permanent-magnet synchronous motor (PMSM). Based on classical control theory, nonlinear state-space model of PMSM is developed. A simple linearization procedure is employed to design a linear state feedback controller (SFC). Digital redesign of a SFC is carried out to achieve discrete form suitable for implementation in a DSP. Model predictive approach is used to a posteriori constraint introduction into control system. It overcomes limitations of motion control system with nonconstrained SFC resulting in low dynamic properties. The novel concept utilizes machine voltage equation model to calculate the boundary values of control signals which provide permissible values of the future state variables. Secondary control objectives such as zero d-axis current are included. Simulation and experimental results are presented to validate the proposed constrained state feedback control algorithm in comparison to nonconstrained state feedback control and cascade control structure, respectively.

Application of Ethernet Powerlink for Communication in a Linux RTAI Open CNC system

Paper presents a novel DC link balancing scheme for a generic n-level back-to-back system with multi-level diode clamped topologies. The proposed algorithm is generalization of the control strategy formerly introduced with the five-level back-to-back system and it relays on measurement of adjacent capacitor voltages which provide information about the potential variation in consecutive nodes of the n-level DC link network. Subsequently the adequate control signals are used to modify the multi-carrier PWM modulators on the rectifier side as well as on the inverter side respectively. In result, the voltages across all the capacitors in DC link network are effectively maintained balanced. The new balancing scheme has been demonstrated in simulations with 5-level, 7-level and 9-level single phase back-to-back systems and it can be extended to any n-level back-to-back system.

Constrained State Feedback Speed Control of PMSM Based on Model Predictive Approach

The paper investigates further improvements of an adaptive ANN (Artificial Neural Network)-based speed controller employed in a DTC-SVM (Direct Torque Controlled - Space Vector Modulated) drive. An on-line trained ANN serves as a speed controller and does not need a process model to predict future performance. In comparison to the previously published solution, auto-adjusting ability has been added to the controller. The recurrent feedback inside the neural controller has been also introduced. Adaptive behaviour manifests in robustness to moment of inertia variation greater than 10 times. This feature is achieved by the learning algorithm running during system operation. Mentioned variable update period refers to one of the parameters connected with learning algorithm, namely frequency of calling backpropagation procedure (weights update procedure). Proposed control algorithm has been tested in simulation and verified experimentally. The behaviour of the drive has been compared to the one with previously proposed ANN-based speed controller with fixed settings of training algorithm.

Novel DC link balancing scheme in generic n-level back-to-back converter system

Purpose – The purpose of this paper is to discuss the design and verification of a non‐classical structure of servo‐drive controller with the state feedback and a load torque feedforward compensation.Design/methodology/approach – First a well known nonlinear mathematical model of a PMSM is transformed into a linear form by introducing new variables. The state space new model presented in rotated orthogonal reference frame is decoupled by means of equation in d and q axis. To achieve correct dynamic performance of the servo‐drive system the state feedback with an internal input model and load torque feedforward compensation is proposed. The observed load torque has been used as an input signal for the feedforward compensator. The design of the control system and simulation analysis were performed in Matlab/Simulink. The proposed control algorithm was implemented in a DSP controller (TMS320F2812). The experiments were carried out by using a 0.6 kW PMSM drive system.Findings – It is shown that the proposed c...

On-line Trained Neural Speed Controller with Variable Weight Update Period for Direct-Torque-Controlled AC Drive

Abstract Two approaches to speed estimation of an induction motor in the drive system, utilizing only easy measurable electrical signals, are presented, discussed and compared. One is based on phenomenological model of the motor and least squares solution of an overdetermined set of linear equations. Another utilizes nonlinear system modeling via neural network. These two models are complementarily treated in the paper. The phenomenological model is simple and easily interpretable, but it is very sensitive to parameter changes. The neural model requires that input variables are preprocessed. We demonstrate that the input variables selection, sampling time and neural architecture are interrelated and that time instances of sinusoidal signals such as stator voltages and currents, without preprocessing, are not convenient for speed estimation. It is proved that for the most commonly used tapped delay neural architecture, the speed estimation cannot be improved above certain level of accuracy through sampling time selection, or enlarging number of delays of the input signals. Preprocessing of the input variables may change the situation. The information obtained from phenomenological model is used to select and preprocess input variables for the neural model. Simulation examples demonstrating both approaches and very good efficiency and robustness of the neural model are included.

PMSM servo‐drive control system with a state feedback and a load torque feedforward compensation

In this paper new discrete non-stationary linear-quadratic speed regulator for PMSM fed by 3-level NPC sinusoidal inverter is presented. Controlled sinus wave inverter with an output LC filter was used in order to reduce electromagnetic torque ripple of the motor. The novelty of the proposed control system consists in one state feedback controller using in order to control the angular velocity of the motor as well as to provide true sine wave of the motor voltages. It was found that some gains of the designed discrete controller are non-stationary and depends on the angular velocity of the motor. Use of non-stationary controller causes, that linearization and decoupling process of the motor with LC filter is not needed. Simulation test results of the designed control system are compared with results obtained for PMSM fed by classical 2-level inverter. Proposed control system can be used in industrial applications where torque ripple minimization is needed instead high dynamic performance.

Induction motor speed estimation: neural versus phenomenological model approach

This paper is devoted to the field of artificial intelligence for drive control. In previous works, we presented possible advantages from using an artificial neural network (ANN) for speed control in a DTC-SVM (direct torque controlled-space vector modulated) drive. Learning of the neural controller was set on-line. Starting from a random configuration of the speed controller, the network adapts its weights according to an error criterion. Although the use of such specialized controller allows potential adaptive and robust control skills, tuning of an ANN for online learning control is a long iterative procedure. Indeed, optimization of the neural controller induces determination of ten parameters acting critically on the control dynamics. However, using optimization algorithms, one can reduce efforts to reveal this set of parameters. Several optimization algorithms are based on description of biological evolutions. We call such algorithms evolutionary algorithms (EA). Genetic algorithm (GA) is a EA inspired by genetic processes leading human race toward optimal individuals capable of controlling their environment. This paper presents GA for optimization of ANN-based speed controller for induction motor drive.

PMSM fed by 3-level NPC sinusoidal inverter with discrete state feedback controller

This paper describes a discrete linear quadratic speed regulator (LQR) for permanent magnet synchronous motor design. Linearization and decoupling processes of a motor model were discussed. An internal input model was introduced to control algorithm in order to eliminate the steady state speed error caused by step reference speed as well as load torque variations. Simulation tests of the drive system with the PMSM, were carried out in the Matlab-Simulink environment. The designed discrete control algorithm was implemented in a digital signal processor (TMS320F2812) and tested in a setup system. Simulation and experiment test results confirm properly operation of the designed drive system.