Design and Implementation of Fuzzy Logic Controller for Online Computer Controlled Steering System for Navigation of a Teleoperated Agricultural Vehicle

This paper describes design, modeling, simulation, control, and implementation of teleoperated agricultural vehicle using intelligent technique. This vehicle can be used for ploughing, sowing, and soil moisture sensing. Online computer controlled steering system for a vehicle utilizing two independent drive wheels can be used to avoid obstacles and to improve the ability to resist external side forces. To control the steer angles of the nondriven wheels, the mathematical relationships between the drive wheel speeds and the steer angles of the nondriven wheels are used. A fuzzy logic controller is designed to change the drive wheel speeds and to achieve the desired steer angles. Online control of the agricultural vehicle is achieved from a remote place by means of Web Publishing Tool in LabVIEW. IR sensors in the vehicle are used to detect and to avoid the obstacles around. The developed steering angle control algorithm and fuzzy logic controller have been implemented in an agricultural vehicle which depicts that the vehicle performs its operation efficiently and reduces the manpower and becomes advantageous

Measurements, Models, Systems and Design

531 s.

Real Time Digital Speed Control System for DC Servo Motor Using LabVIEW 8.5 Package

This paper describes the design and implementation of a personal computerbased closed loop DC motor speed control system Using LabVIEW 8.5 Packagefor data manipulation and interface control. Tuning the parameters of the PIDcontroller is done using trial and error method by conducting simulation on thesystem model using Matlab package. This method is used to find the best systemresponse depending on the tuning parameters of the PID controller. Theseparameters are then implemented in the designed real time digital PID controllersystem based on LabVIEW package.Carrying out the task of tuning the PID controller on the real time model requiresgreat effort and time consuming especially in the early stages. Thus the PIDcontroller tuning is firstly carried out on the simulation model in Matlab which istime saving and gives close parameter approximation for applying in the real timesystem directly. The PID control action in the real time system shows moreoscillation in comparison with the PID simulation control action. Simulation andreal time results for the speed control of the DC motor experiment were found tohave a high degree of agreement in maintaining the desired speed of the motor

Dynamic Simulation and Intelligent Management of Distributed Generation

Significantly large integration of distributed generation (DG) is expected in future power systems. Paving the way are the encouraging government policies, great concerns on environment and optimising economic benefits. This large integration is however raising a lot of concern on stability, security and reliability of power system network which will create many challenges in operating the network due to different characteristics possessed by DG. These concerns and operating challenges need to be investigated to identify the possible problems that may arise and then followed by finding a countermeasure for mitigation. Research work in this thesis addresses most important issues and challenges in operating power system with large DG penetration. The following research areas are performed: • Modeling of distributed generation: New technology embraces by DG has totally different characteristics compared to conventional generation unit based on synchronous machine, thus making it necessary to perform integration studies on various operational aspects to assess the influence of this small scale generation unit on the power system network operation and control. The studies require an accurate and easy to understand dynamic model which closely imitates the dynamics of a real DG unit. In this thesis, development of different dynamic models of micro turbine generation system, fuel cell generation system and a generalized DG dynamic model to be used in power system studies is discussed. The performance of the model developed from the simulation results is found comparable to the reported performance in the literature. • DG fault ride-through capability: Due to different characteristics possessed by DG, power system operator imposed stringent requirements on the DG connected to the grid. During critical time DG has to be able to withstand a temporary voltage dip due to fault occurrence in the grid even when the voltage reaches zero at the point of connection. At the same time, during this fault ride-through DG must also help the grid by injecting reactive power to support power system voltage recovery. Different types of DG technologies are investigated if they are able to fulfil this requirement and if not exploring the necessary modifications which has to be made on the specific DG unit. The investigation results indicate that in fulfilling new grid code requirements, modification inside DG power electronic converter hardware and controller is necessary. iii • Grid support by DG: Massive DG integration in distribution network will interfere with current practices and control scheme inside the network. With appropriate coordination and control, this negative influence on network could be changed to positive. This thesis explores the possibility of participating DGs in voltage regulation and frequency control. Clearly the studies indicate that DG coupled to the grid through power electronic converter has potential to positively contribute to power system voltage regulation and frequency stabilisation. • Influence of DG on power system stability under new grid codes: Power electronic converter utilised in DGs makes them have different characteristics in responding to grid fault and they are also demanded to provide reactive support during this period. This characteristic and new rule is feared to negatively influence the stability of a power system as to where these DG units are connected to. In this thesis the most important types of stability are analysed with different levels of DG penetration and control options. It can be concluded that if DG is properly located and controlled, DG integration will significantly improve the stability of power system network. • Predictive var management inside future distribution network: Future distribution network will contain large number of DG units and the network will be integrated with advance communication facilities. This large number of DG will create technical challenges but the communication link available will provide the opportunity to control DG centrally in real time to optimise the benefits offered by DG integration. In this thesis, a predictive technique in managing DG reactive power to reduce power loss and to control the voltage inside a distribution network is proposed and discussed. The effectiveness of the proposed approach which are developed with two staged intelligent techniques namely, adaptive particle swarm optimisation and artificial neural network, is demonstrated and the results are quite promising

Artificial intelligence applied to speed sensorless induction motor drives

During the last two decades there has been considerable development of sensorless vector controlled induction motor drives for high performance industrial applications. Such control strategies reduce the drive's cost, size and maintenance requirements while increasing the system's reliability and robustness. Parameter sensitivity, high computational effort and instability at low and zero speed can be the main shortcomings of sensorless control. Sensorless drives have been successfully applied for medium and high speed operation, but low and zero speed operation is still a critical problem. Much recent research effort is focused on extending the operating region of sensorless drives near zero stator frequency. Several strategies have been proposed for rotor speed estimation in sensorless induction motor drives based on the machine fundamental excitation model. Among these techniques Model Reference Adaptive Systems (MRAS) schemes are the most common strategies employed due to their relative simplicity and low computational effort. Rotor flux-MRAS is the most popular MRAS strategy and significant attempts have been made to improve the performance of this scheme at low speed. Artificial Intelligence (AI) techniques have attracted much attention in the past few years as powerful tools to solve many control problems. Common AI strategies include neural networks, fuzzy logic and genetic algorithms. The mam purpose of this work is to show that AI can be used to improve the sensorless performance of the well-established MRAS observers in the critical low and zero speed region of operation. This thesis proposes various novel methods based on AI combined with MRAS observers. These methods have been implemented via simulation but also on an experimental drive based around a commercial induction machine. Detailed simulations and experimental tests are carried out to investigate the performance of the proposed schemes when compared to the conventional rotor fluxMRAS. Various schemes are implemented and tested in real time using a 7.5 kW induction machine and a dSP ACE DS 1103 controller board. The results presented for these new schemes show the great improvement in the performance of the MRAS observer in both open loop and sensorless modes of operation at low and zero speed.EThOS - Electronic Theses Online ServiceMinistry of Higher Education, Arab Republic of EgyptGBUnited Kingdo

Bidirectional Neural Interface Circuits with On-Chip Stimulation Artifact Reduction Schemes

Bidirectional neural interfaces are tools designed to “communicate” with the brain via recording and modulation of neuronal activity. The bidirectional interface systems have been adopted for many applications. Neuroscientists employ them to map neuronal circuits through precise stimulation and recording. Medical doctors deploy them as adaptable medical devices which control therapeutic stimulation parameters based on monitoring real-time neural activity. Brain-machine-interface (BMI) researchers use neural interfaces to bypass the nervous system and directly control neuroprosthetics or brain-computer-interface (BCI) spellers. In bidirectional interfaces, the implantable transducers as well as the corresponding electronic circuits and systems face several challenges. A high channel count, low power consumption, and reduced system size are desirable for potential chronic deployment and wider applicability. Moreover, a neural interface designed for robust closed-loop operation requires the mitigation of stimulation artifacts which corrupt the recorded signals. This dissertation introduces several techniques targeting low power consumption, small size, and reduction of stimulation artifacts. These techniques are implemented for extracellular electrophysiological recording and two stimulation modalities: direct current stimulation for closed-loop control of seizure detection/quench and optical stimulation for optogenetic studies. While the two modalities differ in their mechanisms, hardware implementation, and applications, they share many crucial system-level challenges. The first method aims at solving the critical issue of stimulation artifacts saturating the preamplifier in the recording front-end. To prevent saturation, a novel mixed-signal stimulation artifact cancellation circuit is devised to subtract the artifact before amplification and maintain the standard input range of a power-hungry preamplifier. Additional novel techniques have been also implemented to lower the noise and power consumption. A common average referencing (CAR) front-end circuit eliminates the cross-channel common mode noise by averaging and subtracting it in analog domain. A range-adapting SAR ADC saves additional power by eliminating unnecessary conversion cycles when the input signal is small. Measurements of an integrated circuit (IC) prototype demonstrate the attenuation of stimulation artifacts by up to 42 dB and cross-channel noise suppression by up to 39.8 dB. The power consumption per channel is maintained at 330 nW, while the area per channel is only 0.17 mm2. The second system implements a compact headstage for closed-loop optogenetic stimulation and electrophysiological recording. This design targets a miniaturized form factor, high channel count, and high-precision stimulation control suitable for rodent in-vivo optogenetic studies. Monolithically integrated optoelectrodes (which include 12 µLEDs for optical stimulation and 12 electrical recording sites) are combined with an off-the-shelf recording IC and a custom-designed high-precision LED driver. 32 recording and 12 stimulation channels can be individually accessed and controlled on a small headstage with dimensions of 2.16 x 2.38 x 0.35 cm and mass of 1.9 g. A third system prototype improves the optogenetic headstage prototype by furthering system integration and improving power efficiency facilitating wireless operation. The custom application-specific integrated circuit (ASIC) combines recording and stimulation channels with a power management unit, allowing the system to be powered by an ultra-light Li-ion battery. Additionally, the µLED drivers include a high-resolution arbitrary waveform generation mode for shaping of µLED current pulses to preemptively reduce artifacts. A prototype IC occupies 7.66 mm2, consumes 3.04 mW under typical operating conditions, and the optical pulse shaping scheme can attenuate stimulation artifacts by up to 3x with a Gaussian-rise pulse rise time under 1 ms.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147674/1/mendrela_1.pd

Power Electronics and Energy Management for Battery Storage Systems

The deployment of distributed renewable generation and e-mobility systems is creating a demand for improved dynamic performance, flexibility, and resilience in electrical grids. Various energy storages, such as stationary and electric vehicle batteries, together with power electronic interfaces, will play a key role in addressing these requests thanks to their enhanced functionality, fast response times, and configuration flexibility. For the large-scale implementation of this technology, the associated enabling developments are becoming of paramount importance. These include energy management algorithms; optimal sizing and coordinated control strategies of different storage technologies, including e-mobility storage; power electronic converters for interfacing renewables and battery systems, which allow for advanced interactions with the grid; and increase in round-trip efficiencies by means of advanced materials, components, and algorithms. This Special Issue contains the developments that have been published b researchers in the areas of power electronics, energy management and battery storage. A range of potential solutions to the existing barriers is presented, aiming to make the most out of these emerging technologies

Sliding Mode Control

The main objective of this monograph is to present a broad range of well worked out, recent application studies as well as theoretical contributions in the field of sliding mode control system analysis and design. The contributions presented here include new theoretical developments as well as successful applications of variable structure controllers primarily in the field of power electronics, electric drives and motion steering systems. They enrich the current state of the art, and motivate and encourage new ideas and solutions in the sliding mode control area

Risk-Based Machine Learning Approaches for Probabilistic Transient Stability

Power systems are getting more complex than ever and are consequently operating close to their limit of stability. Moreover, with the increasing demand of renewable wind generation, and the requirement to maintain a secure power system, the importance of transient stability cannot be overestimated. Considering its significance in power system security, it is important to propose a different approach for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables (fault type, fault location, fault clearing time, etc.). These approaches typically provide a conservative criterion and can result in expensive expansion plans or conservative operating limits. With the increasing system uncertainties and widespread electricity market deregulation, there is a strong inevitability to incorporate probabilistic transient stability (PTS) analysis. Moreover, the time-domain simulation approach, for transient stability evaluation, involving differential-algebraic equations, can be very computationally intensive, especially for a large-scale system, and for online dynamic security assessment (DSA). The impact of wind penetration on transient stability is critical to investigate, as it does not possess the inherent inertia of synchronous generators. Thus, this research proposes risk-based, machine learning (ML) approaches, for PTS enhancement by replacing circuit breakers, including the impact of wind generation. Artificial Neural Network (ANN) was used for predicting the benefit-cost ratio (BCR) to reduce the computation effort. Moreover, both ANN and support vector machine (SVM) were used and consequently, were compared, for PTS classification, for online DSA. The training of the ANN and SVM was accomplished using suitable system features as inputs, and PTS status indicator as the output. DIgSILENT PowerFactory and MATLAB was utilized for transient stability simulations (for obtaining training data for ML algorithms), and applying ML algorithms, respectively. Results obtained for the IEEE 14-bus test system demonstrated that the proposed ML methods offer a fast approach for PTS prediction with a fairly high accuracy, and thereby, signifying a strong possibility for ML application in probabilistic DSA. Advisor: Sohrab Asgarpoo