DSP Board Research Articles

Adaptive control of magnetic levitation system based on fuzzy inversion

A novel adaptive tracking controller for magnetic levitation systems (MLS) is developed. The controller is based on a special adaptive control scheme incorporating fuzzy model of electromagnetic acceleration enabling fast and accurate fuzzy inversion. The controller ensures accurate tracking of any smooth desired position signal, despite unknown MLS parameters. The closed-loop system stability, in the sense of uniform ultimate boundedness (UUB) of error trajectories, is proved using Lyapunov approach. The closed-loop system performance is investigated during numerical experiments. Finally the proposed controller is verified by successful implementation on a DSP board controlling a typical magnetic levitation ball system. Performed tests and experiments demonstrate that the proposed control technique is robust against discretization and unknown MLS model parameters, provides high tracking accuracy, is easily implementable, and simple to tune.

Design and Implementation of 5G Wireless Receiver Algorithms on the DSP Board

Design and Implementation of 5G Wireless Receiver Algorithms on the DSP Board

Energy management of electric vehicle using a new strategy based on slap swarm optimization and differential flatness control

Optimal energy management of electric vehicles using slap swarm optimization and differential flatness control has been proposed. A battery–supercapacitor power system is adopted. Each source is connected in parallel to the DC-bus using DC–DC bidirectional converters and supplies a synchronous reluctance motor (SynRM) based drive. The proposed EMS fundamental forces lie in using a combination of complementary proprieties of two approaches, a Slap Swarm optimization Algorithm and Differential Flatness (DF). With a fast optimization mechanism, the Slap Swarm optimization algorithm allows adapting in real-time conditions the DF gains to optimize the system performances. On its side, DF uses predefined trajectories respecting the physical proprieties of the system, which is a powerful tool to guarantee the dynamic constraints of the sources when ensuring desired robust control proprieties. To check the feasibility and performance of the suggested EMS, comprehensive processor-in-the-loop co-simulations of the electric vehicle were carried out using the C2000 launchxl-f28379d DSP board. The main goal of the proposed EMS is to guarantee the DC-bus stabilization, reducing the DC-bus voltage ripples (Δv = 5 V) and the voltage overshoots 15 V (3.2%), respect the source dynamics, and satisfy the SynRM motor power demand. Furthermore, the algorithm minimizes induced harmonics by the drive (10.49%), reducing the battery current ripple by 17.15A, thereby enhancing the battery lifecycle.

Fuzzy Logic Controller Based Perturb and Observe Maximum Power Point Tracking

This paper presents a fuzzy logic controller (FLC) based technique to design an adaptive perturb and observe (P&O) maximum power point tracking (MPPT) controller. The objective is to improve the efficiency of a standalone solar energy system consisting of a photovoltaic (PV) panel and a DC/DC buck converter connected to a variable load. The proposed FLC based technique is implemented on a TMS320F28335 DSP board and compared to the conventional P&O technique. The performance of the proposed system is validated through outdoor experiments with a 150-Watt PV system

Active noise control performance in the high-frequency range

Noise control for vehicle mainly uses a reduction method through sound absorbing and insulating materials but has disadvantages of increasing cost and weight. It is known that the noise reduction performance of Active Noise Control (ANC) technology that can be considered as an alternative is limited to the low frequency range. However, there could be a case when the use of active noise cancellation is required at higher frequency range. Therefore, the purpose of this paper is to find out how much high-frequency noise can be controlled by applying active noise cancellation and the performance of ANC according to the equipment used. For this end, an ANC experimental environment was installed at the passenger's seat interior vehicle. By using Filtered-x Least Mean Square (FxLMS) algorithm based on adaptive control, ANC was conducted to reduce the high frequency noise in the 2kHz~4kHz and its noise reduction performance was compared according to the following variables: Filter length, step size, sampling frequency, etc. As a result of selecting the audio DSP board as the control system and applying appropriate filter length, step size, and sampling frequency, noise reduction of up to 40 dB(A) was obtained at the position of both ears of the occupant.

Robust PLL-Based Grid Synchronization and Frequency Monitoring

Nowadays, the penetration of inverter-based energy resources is continuously increasing in low-voltage distribution grids. Their applications cover traditional renewable energy production and energy storage but also new applications such as charging points for electric vehicles, heat pumps, electrolyzers, etc. The power ratings range from a couple of kW to hundreds of kW. Utilities have, in the last few years, reported more challenges regarding power quality in distribution grids, e.g., high harmonic content, high unbalances, large voltage and frequency excursions, etc. Phase-Lock-Loop (PLL) algorithms are typically used for grid synchronization and decoupled control of power converters connected to the grid. Most of the research within PLLs is mainly focusing on grid voltage angle estimation while the byproducts of the algorithms, e.g., frequency and voltage magnitude, are often overlooked. However, both frequency and voltage magnitude estimations are crucial for grid code compliance. Practical considerations for implementation on microcontroller boards of these algorithms are also missing in most of the cases. The present paper proposes a modified PLL algorithm based on a Synchronous Reference Frame that is suitable for both grid synchronization and frequency monitoring, i.e., the estimation of RMS phase voltages and frequencies in highly distorted distribution grids. It also provides the tuning methodology and practical considerations for implementation on commercial DSP boards. The performance of the proposed approach is assessed through simulation studies and laboratory tests under a wide range of operational conditions, showing that the proposed PLL can estimate the grid frequency, for all considered grid events, with an accuracy of less than ±5 mHz, which is a significant improvement on the current state-of-the-art solutions, having an accuracy of at least ±20 mHz or more.

A Standard Two Stage On-Board Charger With Single Controlled PWM and Minimum Switch Count

A novel two-stage standard battery charger having minimum switch count using a single controlled PWM signal is presented in this paper for Electric Vehicle (EV) charging application. The first stage is responsible for AC to DC conversion with power factor correction (PFC) operation that consists of two switches and two diodes. The second stage is a DC-DC converter having a half bridge LLC resonant configuration along with an isolation transformer. The complete charger consists of only four switches and four diodes, which is minimum among the reported literatures. Moreover, unlike a conventional control strategy that requires multiple separate controlled PWM signals for different switches, the new control scheme manages both the PFC operation as well as constant current–constant voltage (CC-CV) optimal battery charging with a single controlled PWM signal. The detailed operation and its modeling are presented in this paper. A 470 W scaled down laboratory prototype is built to validate the proof of concept. The proposed charger is tested to charge a battery set of 24 V, 30 Ah using CC-CV logic with a maximum efficiency of 97.6%. The prototype is also tested with resistive load at its rated power and dynamics results are captured. A TMS320F28335 DSP board from Texas Instrument is used for implementing the control technique.

The Design and Processor-In-The-Loop Implementation of a Super-Twisting Control Algorithm Based on a Luenberger Observer for a Seamless Transition between Grid-Connected and Stand-Alone Modes in Microgrids

The abrupt transfer from grid-connected (GC) to stand-alone (SA) operation modes is one of the major issues that may threaten the stability of a distributed generation (DG) system. Furthermore, if the islanding mode happens, it is vital to take into consideration the load voltages or load current waveforms as soon as feasible. This paper develops an advanced control technique based on a super-twisting sliding mode controller (ST-SMC) for a three-phase inverter operating in both the GC and SA modes. This control scheme is proposed to ensure a smooth transition from the GC to SA mode and enhance the load voltage waveforms under the islanding mode. In addition, to minimize the operational costs of the system and the complexity of the studied model, a digital Luenberger observer (DLO) with a proper design is adopted for estimating the inverter-side current. The control scheme of the whole system switches between a current control mode during the GC mode and a voltage control mode during the SA mode. The super-twisting control algorithm is applied to the outer voltage control loop involved in the cascaded voltage/current control scheme in the SA mode. Simulation tests of a three-phase inverter are performed for the purpose of assessing the suggested control performance by using the PowerSim (PSIM) software and comparing it with a classical PI controller. Furthermore, a processor-in-the-loop (PIL) implementation in a DSP board TMS32F28335 while debugging is conducted using code composer studio 6.2.0. The obtained results show efficient control properties, such as a smooth transition among the microgrid (MG) operating modes, as well as effectiveness and robustness during both the GC and SA operation modes.

케이블 임피던스 측정 및 검증 방법

This paper present detail description on determining cable parameters of an electrical power cable. A method known as short-open method is employed in this work where the series parameters of a two wire cable are determined by shorting the end of the cable while the shunt parameters are extracted by opening the end terminal of the cable. Network analyzer and LCR meter are utilized in carrying out the short-open method. The measured parameters from both network analyzer and the LCR meter are compared and verified by finding the propagation delay of the cable through an experiment. The propagation delay value from the experiment is compared to a propagation delay value computed from the primary parameters to check for accuracy. The experimental setup constituted of a DSP board, 30kHz buck converter, 150V DC power supply and a 16m power cable. The 16m cable was selected because it was much easier to manage in the laboratory. The cable parameters were extracted at 30kHz frequency which is the same as the buck converter switching frequency. At 30kHz a propagation delay of 84ns was obtained for LCR meter while network analyzer had 83ns. Propagation delay from using the buck converter gave a propagation delay of 84ns which is the same as computed propagation delay value from LCR meter.

Extreme Multistability in a Hopfield Neural Network Based on Two Biological Neuronal Systems

Based on different biological neuronal systems, various memristive neuron and neuron network models are generated. In this brief, a three-neurons-based Hopfield neural network with two biological neural mechanisms is investigated, with one of three neurons exposed to electromagnetic radiation and one of the synaptic weights replaced by a memristor. The effects of electromagnetic radiation and memristive synaptic weight on neural networks are investigated. It is found that coexisting behaviors exist in the neural network by adjusting the memristive synaptic weight. Moreover, infinitely many coexisting behaviors are observed by changing the initial electromagnetic radiation. That is, both of them are critical to the system and have the potential to complicate it. Furthermore, circuits of the neural network are implemented in Pspice and DSP board, and the experimental results are in agreement with numerical ones.

PIL Implementation of Feedback Linearisation-SVM Control of 3-Phase Multifunctional Grid-Tied Solar PV Integrated UPQC

In this paper, an implementation of a feedback linearisation-based on SVM control of a multifunctional unified power quality conditioner (UPQC) combined with a photovoltaic generator (PVG) using STM32F429I-DISC DSP board has been presented. Further, The PVG-UPQC is acting as a universal conditioner for power quality enhancement and renewable energy integration simultaneously with harmonics reduction in both grid voltage and current. Furthermore, the system can also compensate reactive power in presence of nonlinear loads. The presented system is made up of a shunt active power filter which acts as a current harmonics compensator in addition to a series filter that compensates voltage harmonics and fluctuations such as voltage sag/swell. In order to enhancing the PVG-UPQC performances, a nonlinear feedback linearisation control technique combined with SVM controller is applied. The performance of the suggested control scheme is validated by extensive PIL co-simulation technique. The obtained results prove the high performances and reliability of the presented system in the case of nonlinear load variations as well as eliminating voltage and current harmonics. These results accomplish the superiority and effectiveness of those obtained by a compared to a conventional PI controller.

Real-Time Reduction of Rotor Position Estimation Error Based on the Stator Flux Estimation-Combined Method for Sensorless Control of PMSMs Drives

This paper presents the implantation and the performance improvement of real-time reduction of rotor position estimation error based on the stator flux estimation-combined method for sensorless control of PMSMs drives. Wherein, the proposed method is designed based on a simple algorithm for accurate estimation and robust control of quasi-exact stator flux and position/speed of PMSMs. A large-speed range of applications in sensorless control strategies is achieved by using the mathematical model of PMSM motor. The latter presents the main idea of the estimators’ structures design. They are generally composed of voltage and current models (VM and CM). However, the dynamic performances of these methods are influenced in the low and high-speed ranges, and they are sensitive to parameters variations. The estimators’ structures are dominated by the current model at the low-speed range, and by the voltage model at the high-speed range. The technique proposed in this study is the combination of the two preceding models; this provides performances’ improvement in the PMSM dynamics for a large speed variation with compensating estimation errors for the two separate methods (VM and CM). The main objective of this combined method is the drawback reduction for the separate use of VM and CM estimators. The algorithm principle is based on exploiting and combining the two estimators while measuring the stator voltages and the currents, in addition to estimating the rotor position and the speed. Furthermore, the performances of the proposed method have been tested and validated through real-time experiments by using dSPACE ds 1104 DSP board.

Prototype design for bidirectional control of stepper motor using features of brain signals and soft computing tools

The brain-computer interface (BCI) plays a significant role in supporting specially-abled people to control devices with the brain signals or electroencephalogram (EEG). The proper functioning of BCI systems requires classification algorithms to distinguish between different tasks based on the features extracted from EEG. Motivated by the requirement of designing a reliable BCI with reduced memory and computational cost, this work proposes an EEG controlled stepper motor drive-based aiding device. The drive system is executed in real-time based on the processing and classification of EEG. The scheme initiates with the extraction of discriminatory features from raw time-domain EEG signals using higher-order statistics (HOS) and phase locking value (PLV).Further, with extracted feature vector, the present study contemplates the operation of backpropagation neural network (BPNN), k-Nearest neighbours (k-NN), and support vector machine (SVM). Signal classification by SVM in conjugation with nonlinear principal component analysis (NLPCA) is implemented to reduce the feature vector dimension. Average classification accuracy of 82.70% and 80.46% is achieved using NLPCA with SVM for PLV and HOS features. The classifier output is utilized to spin the stepper motor clockwise and anticlockwise as needed. The control of stepper motor uses the classifier output and hence the brain signals are implemented on a laboratory-developed digital test bench comprising of TI TMS320F28379D launchpad DSP board and MITSUMI stepper motor. The validation of the proposed scheme for different signals reflects its effectiveness in combining the software and hardware aspects required for realizing an actual BCI system for real-time settings.

Approximation of Permanent Magnet Motor Flux Distribution by Partially Informed Neural Networks

New results in the area of neural network modeling applied in electric drive automation are presented. Reliable models of permanent magnet motor flux as a function of current and rotor position are particularly useful in control synthesis—allowing one to minimize the losses, analyze motor performance (torque ripples etc.) and to identify motor parameters—and may be used in the control loop to compensate flux and torque variations. The effectiveness of extreme learning machine (ELM) neural networks used for approximation of permanent magnet motor flux distribution is evaluated. Two original network modifications, using preliminary information about the modeled relationship, are introduced. It is demonstrated that the proposed networks preserve all appealing features of a standard ELM (such as the universal approximation property and extremely short learning time), but also decrease the number of parameters and deal with numerical problems typical for ELMs. It is demonstrated that the proposed modified ELMs are suitable for modeling motor flux versus position and current, especially for interior permanent magnet motors. The modeling methodology is presented. It is shown that the proposed approach produces more accurate models and provides greater robustness against learning data noise. The execution times obtained experimentally from well-known DSP boards are short enough to enable application of derived models in modern algorithms of electric drive control.

High Gain Non Isolated DC Converter Employed in Single-Phase Grid-Tied Solar Photovoltaic Supply System

This article presents a high gain non isolated dc converter (HGNIDC) employed in single-phase grid-tied solar photovoltaic supply system (SPSS). In the proposed power optimized architecture based SPSS, each photovoltaic (PV) module is connected through HGNIDC to the common dc bus. The HGNIDC boosts up the low PV voltage as well as facilitates to harvest the maximum solar energy. For maximum power harvesting, fuzzy logic control (FLC) is used. The FLC enables high conversion efficiency at different weather conditions, fast dynamic response, reduced PV voltage oscillation, and does not require system specifications. For delivering the power to the ac grid from the common dc bus, an H-bridge voltage source converter (VSC) is used. The synchronization of VSC with the utility grid is carried out using an enhanced third-order generalized integrator (ETOGI). The ETOGI is able to extract fundamental grid voltage even under grid polluted conditions such as voltage sag, swell, higher dc-offsets, and harmonics. The performance of the proposed control approaches is validated using MATLAB/Simulink platform. The real time implementation is obtained using DSP board TMS320F28379D on the developed laboratory prototype.

Harmonic Detection for Shunt Active Power Filter Using ADALINE Neural Network

This paper presents an efficient harmonic detection for real-time generation of the reference current fed to a shunt active power filter using the ADALINE neural network. This proposed method is a single layer with 101 nodes generating the coefficients referred to as weights of the reference current model. It effectively overcomes the drawback of the current technology, which is instantaneous power theory (PQ). The proposed method was implemented on the TMS320F28335 DSP board and tested against MATLAB with Simulink as a hardware-in-loop (HIL) structure. This method gives a good performance by producing a precise reference current in a short period with uncomplicated calculation. It also efficiently can eliminate individual harmonic current. The achieved percentage of total harmonic distortion (%THD) in the current is reduced following the IEEE standard, while the power factor can be maintained to unity.

Neural Network Observers and Sensorless Robust Optimal Control for Partially Unknown PMSM With Disturbances and Saturating Voltages

This article proposes neural network (NN) based observer schemes and a sensorless robust optimal control scheme for partially unknown permanent magnet synchronous motors with disturbances and saturating voltages. First, an NN-observer scheme is designed to estimate back-electromotive force (EMF), for which the mathematical model in rotary or stationary reference frames is relaxed. The NN weight tuning law is designed via Lyapunov theory to guarantee that EMF is ultimately uniformly bounded. Second, to compensate the inexact extraction of the estimated back-EMF at any speed conditions, disturbances, and NN approximation errors, another NN-observer scheme is designed to estimate the tracking errors of rotor position and speed, for which low-pass filters and/or phase-locked loops are not needed. Third, a sensorless saturated robust optimal control scheme dealing with general disturbances and saturating voltages is designed. The scheme includes the augmented feedforward controller to transform the speed and current tracking problem into an optimal control problem. Finally, the feedback control law and worst disturbance law are obtained without estimating unknown internal dynamics. The effectiveness of the proposed schemes is tested through simulations and comparative experiments on a load drive application with a DSP board TMS320F28379D.

A fractional-order chaotic system with hidden attractor and self-excited attractor and its DSP implementation

• A new 3 dimensional fractional order chaotic system with hidden attractor and self excited attractor is proposed. • The dynamical behaviors of FMACS self excited attractors and hidden attractors are explored separately with order q , and some comparisons are made by using LEs and bifurcation model. • The coexistence phenomenon of attractors in FMACS with hidden attractors is studied. It is interesting that attractors with different shapes appear in different orders. • FMACS is implemented on the DSP board. • A non linear term is replaced by e x , FMACS has a multi state transition and eventually quickly degenerates into a periodic state. The definition of fractional calculus is introduced into a 3D multi-attribute chaotic system in this paper. The fractional multi-attribute chaotic system (FMACS) numerical solution is obtained based on the Adomian decomposition method (ADM). The balance points and dynamical behaviors of self-excited and hidden attractors in FMACS are compared and analyzed through the Lyapunov spectrum, bifurcation model, and complexity. It is worth noting that some hidden coexistence attractors with different shapes are affected by the order. Besides, a novel chaotic system without equilibrium points is constructed, in which the nonlinear function term in FMACS is replaced with a rare nonlinear function e x . Meanwhile, its degradation phenomenon and state transition phenomenon are analyzed in detail. Finally, the digital circuit of the system is realized on the DSP board. The research result shows that FMACS has richer dynamical behaviors and higher complexity. This research provides a theoretical basis and guidance for the application of fractional chaotic systems.

Smartphone-Based Real-Time Digital Signal Processing, Third Edition

Real-time or applied digital signal processing courses are offered as follow-ups to conventional or theory-oriented digital signal processing courses in many engineering programs for the purpose of teaching students the technical know-how for putting signal processing algorithms or theory into practical use. These courses normally involve access to a teaching laboratory that is equipped with hardware boards, in particular DSP boards, together with their supporting software. A number of textbooks have been written discussing how to achieve real-time implementation on these hardware boards. This book discusses how to use smartphones as hardware boards for real-time implementation of signal processing algorithms, thus providing an alternative to the hardware boards that are used in signal processing laboratory courses. The fact that mobile devices, in particular smartphones, have become powerful processing platforms led to the development of this book to enable students to use their own smartphones to run signal processing algorithms in real-time considering that these days nearly all students possess smartphones. Changing the hardware platforms that are currently used in applied or real-time signal processing courses to smartphones creates a truly flexible laboratory experience or environment for students. In addition, it relieves the cost burden associated with using dedicated signal processing boards noting that the software development tools for smartphones are free of charge and are well-maintained by smartphone manufacturers. This book is written in such a way that it can be used as a textbook for real-time or applied digital signal processing courses offered at many universities. Ten lab experiments that are commonly encountered in such courses are covered in the book. It is written primarily for those who are already familiar with signal processing concepts and are interested in their real-time and practical aspects. Similar to existing real-time courses, knowledge of C programming is assumed. This book can also be used as a self-study guide for those who wish to become familiar with signal processing app development on either Android or iOS smartphones/tablets. A zipped file of the codes discussed in the book can be acquired from this third-party website: http://sites.fastspring.com/bookcodes/product/SignalProcessingBookcodesThirdEdition

Assessment of maximum power point trackers performance using direct and indirect control methods

The direct and indirect control methods are commonly used for maximum power point tracker (MPPT) of photovoltaic (PV) systems. Due to the absence of the literature that assess their performance in a comprehensive way, this work attempts to analyze the effectiveness of both control methods for the perturb and observe (P&O) MPPT algorithm. The MATLAB/Simulink simulation is verified experimentally using a PV array simulator and the dSPACE DS1104 DSP board driving a buck-boost converter. The results show that the indirect method exhibits lower steady-state oscillation and is less sensitive to rapid irradiance change and load variations. It increases the steady-state efficiency by 1.6%. Furthermore, under irradiance and load step changes, the transient efficiency increases by 29% and 30%, respectively. Based on these findings, it is envisaged that the indirect method is more suitable to be used as the controller in conjunction with the MPPT algorithm.