Bartlomiej Ufnalski

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

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.

Output Filter

The paper presents methods in teaching modern drives employed in our Division. The goal is to attract students to often complex issues related to power electronics and drives control. Flexible educational tool in the form of an inverter-fed induction motor drive with internet-based remote control panel is presented. A few scenarios of the experiment are discussed.

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

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.

Methods in Teaching Modern AC Drives: Inverter-fed Motor System with Internet-based Remote Control Panel

Purpose – The purpose of this paper is to provide high-efficiency and high-power hybrid energy source for an urban electric vehicle. A power management strategy based on fuzzy logic has been introduced for battery-ultracapacitor (UC) energy storage. Design/methodology/approach – The paper describes the design and construction of on-board hybrid source. The proposed energy storage system consists of battery, UCs and two DC/DC interleaved converters interfacing both storages. A fuzzy-logic controller (FLC) for the hybrid energy source is developed and discussed. Control structure has been tested using a non-mobile experimental setup. Findings – The hybrid energy storage ensures high-power ability. Flexibility and robustness offered by the FLC give an easy accessible method to provide a power management algorithm extended with additional input information from road infrastructure or other vehicles. In the presented research, it was examined that using information related to the topography of the road in the ...

Genetic Algorithm for Parameters Optimization of ANN-based Speed Controller

The paper presents control system synthesis for a true sine wave four-leg inverter with an LC output filter. Our main goal is to ensure high quality voltage waveform for unbalanced and nonlinear loads. An LQR approach was chosen. The LQR design approach has proven effective in many industrial applications. Although weights in a cost function are usually set by guess and check method, the LQR in proposed system gives satisfactory results as far as some general and very intuitive rules are applied, e.g. the penalty coefficient for an integral of control error is a few orders of magnitude away from penalty coefficients related to plant state variables. In order to immunise system against nonlinear loads oscillatory terms are included in the control scheme. They can be tuned for typical harmonics like 5th, 7th, 11th and 13th. A novelty of the proposed solution lies in the fact that all controller gains (including all integral and oscillatory terms) are determined altogether in one LQR procedure call. Controls are calculated in rotating reference frame. Simulation (continuous and discrete) and experimental results (at the level of 10kW) are included. They establish consistent set of tests that validates effectiveness of the design approach.

Fuzzy logic based power management strategy using topographic data for an electric vehicle with a battery-ultracapacitor energy storage

This paper presents simulation studies and preliminary experimental results related to a hybrid battery-ultracapacitor energy source for an urban electric car. A proposal of energy management strategy based on fuzzy logic is introduced. Control strategy for the hybrid source is designed to achieve high-efficiency and high-power source for the vehicle with a relatively small battery storage. Simulation studies show the possibility to use information from a navigation system to improve performance of the source.

Multi-oscillatory LQR for a three-phase four-wire inverter with L 3n C output filter

In this paper we develop a new approach to combined full-state and disturbance feedforward control system synthesis for a three-phase four-leg true sine wave inverter. Proposed scheme is an extension of the augmented state feedback controller with multi-oscillatory terms. Multi-resonant controllers are well-established solution for this kind of converters if high quality output voltage is needed in the presence of nonlinear loads. They take advantage of repetitiveness of the control process under the steady load conditions (this includes nonlinear and unbalanced loads). No presumption on load type entails introduction of oscillatory terms for all three voltage components with resonant frequencies up to available bandwidth dictated by technically justified switching/sampling frequency. This makes the practical implementation challenging. Our idea is to reduce number of needed oscillatory terms by adding disturbance feedforward. In the discussed system the disturbance signal is easily accessible/measurable and hence this feedback can be very effective because of lack of estimation errors. The control system is designed in dq0 reference frame rotating with the fundamental output frequency. Extensive numerical experiments indicate that the number of oscillatory terms can be reduced significantly and high quality of output voltage can be maintained.

Fuzzy logic control of a hybrid battery-ultracapacitor energy storage for an urban electric vehicle

The paper presents an evolutionary optimization of a novel neurocontroller for the single phase sine-wave inverter. The controller is trained in online mode. The adaptation algorithm takes into account repetitiveness of the process to be controlled. The cost function evaluates performance of the controller over the whole period of the reference signal and the weights are updated only once per period of this signal. A model-free concept is implemented and hence no prior identification of the plant is needed. The controller employs the backpropagation algorithm for updating its weights in order to adapt to changing load conditions. The controller is nonlinear and the tuning procedure involves determining a good set of values for at least three parameters: a number of neurons, an error gain and an output gain. In our recent publication devoted to the development of the control algorithm we relied on guessing and checking at the tuning stage. Here the particle swarm optimizer (PSO) is used to find the optimal set of values for these parameters. The gradientless search algorithm enables a designer to work with any user-defined performance index that reflects desired system behavior. The effectiveness of the proposed approach is illustrated with the help of numerical experiments. The controller tuned by the PSO is capable to maintain a high-quality output voltage waveform in the presence of the periodic disturbance caused by nonlinear loads.

An LQ controller with disturbance feedforward for the 3-phase 4-leg true sine wave inverter

This paper presents a state-feedback current controller with oscillatory terms for three-phase grid-connected PWM converters. Use of the oscillatory terms allows for shaping the sinusoidal input currents of the converter under distorted grid voltage conditions. Linear-quadratic optimization method is used to calculate the current controller gains. In the converter control structure the state current controller and PI DC-link voltage controller are used. Mathematical analysis and simulation results of the proposed control system are presented and discussed. n symulacyjnych. (Uklad sterowania przeksztaltnikiem sieciowym wykorzystuj ˛ acy regulator stanu ze sprz ˛zeniem od pr ˛ adow i sygnalow z czlonow oscylacyjnych)