1,270 research outputs found

    Fractional Order Fuzzy Control of Hybrid Power System with Renewable Generation Using Chaotic PSO

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    This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants

    On controllability of neuronal networks with constraints on the average of control gains

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    Control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently

    Machine Learning for Unmanned Aerial System (UAS) Networking

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    Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

    Spectrum sensing in cognitive radio:use of cyclo-stationary detector

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    Cognitive radio allows unlicensed users to access licensed frequency bands through dynamic spectrum access so as to reduce spectrum scarcity. This requires intelligent spectrum sensing techniques like co-operative sensing which makes use of information from number of users. This thesis investigates the use of cyclo-stationary detector and its simulation in MATLAB for licensed user detection. Cyclo-stationary detector enables operation under low SNR conditions and thus saves the need for consulting more number of users. Simulation results show that implementing co-operative spectrum sensing help in better performance in terms of detection. The cyclo-stationary detector is used for performance evaluation for Digital Video Broadcast-Terrestrial (DVB-T) signals. Generally, DVB-T is specified in IEEE 802.22 standard (first standard based on cognitive radio) in VHF and UHF TV broadcasting spectrum. The thesis is further extended to find the number of optimal users in a scenario to optimize the detection probability and reduce overhead leading to better utilization of resources. The gradient descent algorithm and the particle swarm optimization (PSO) technique are put to use to find an optimum value of threshold. The performance for both these schemes is evaluated to find out which fares better

    Handling packet dropouts and random delays for unstable delayed processes in NCS by optimal tuning of PIλDμ controllers with evolutionary algorithms

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    This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The issues of stochastically varying network delays and packet dropouts in Networked Control System (NCS) applications have been simultaneously addressed by time domain optimal tuning of fractional order (FO) PID controllers. Different variants of evolutionary algorithms are used for the tuning process and their performances are compared. Also the effectiveness of the fractional order PI(λ)D(μ) controllers over their integer order counterparts is looked into. Two standard test bench plants with time delay and unstable poles which are encountered in process control applications are tuned with the proposed method to establish the validity of the tuning methodology. The proposed tuning methodology is independent of the specific choice of plant and is also applicable for less complicated systems. Thus it is useful in a wide variety of scenarios. The paper also shows the superiority of FOPID controllers over their conventional PID counterparts for NCS applications.This work has been supported by the Board of Research in Nuclear Sciences (BRNS) of the Department of Atomic Energy (DAE), India, sanction no. 2009/36/62-BRNS, dated November 2009

    Proportional resonant control of three-phase grid-connected inverter during abnormal grid conditions

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    PhD ThesisThe development of using grid-connected three-phase inverter has augmented the standing of realizing muted distortion along with high-quality current waveform. The standard three-phase grid-connected inverter is the full-bridge voltage source inverter. This inverter is usually controlled by proportional integral (PI) controller in order to ensure sinusoidal current injection to the grid. Although the PI controller is well established and easy to use under normal grid conditions, it leads to system instability under abnormal grid conditions. When abnormal grid conditions are likely to occur, the control system with PI controller can be configured to include two separate PI controllers for the positive and negative sequence components of the grid current. However, this increases control complexity and total harmonic distortion (THD). More recently, the proportional resonant (PR) controller started to replace PI controller in a different application including grid-connected current control. In this thesis, a comprehensive theoretical and experimental comparison between the PI and PR controllers is presented. The comparison shows that the PR controller offers lower total harmonic distortion (THD) in the current signal spectrum and is simpler to implement as it uses only the positive sequence component of the grid current and consequently only one PR controller is needed. For these reasons, the PR controller is adopted in this thesis. Despite the PR controller offering enhanced functioning under abnormal grid conditions compared to PI controller, a sudden change in the grid voltage could additionally raise the error between the reference signal and the controlled signal which results in causing significant divergence from its ostensible value. In this case, the performance of the conventional PR controller will not keep up with the increase in the error which weakens controller performance. To overcome this problem, a new design concept for controlling the current of the three-phase grid connected inverter during normal and abnormal conditions is presented in this thesis. The proposed technique replaces the static control parameters by adaptive control parameters based on a look-up table. This adaptive PR, controller has been investigated and demonstrated with different normal and abnormal grid conditions. The proposed control technique is capable of providing low THD in the injected current even during the occurrence of abnormal grid conditions compared with PI and PR controllers. It also achieves lower overshoot and settling time as well as smaller steady-state error. Proportional Resonance Control of Three-Phase Grid-Connected Inverter II Additionally, despite the fact that both PI and PR controllers are relatively straightforward to tune, and are sometimes capable of dealing with many time-varying grid conditions. This research also presented an adaptive controller tuned using advanced optimization techniques based on particle swarm optimisation (PSO). PSO is presented to optimize the control parameters of both PI and PR controllers for the three-phase grid-connected inverter. There are many advantages of using PSO, such as no additional hardware being required. Thus, it can be extended to other applications and control methods. In addition, the proposed method is a self-tuning method and can thus be suitable for industrial applications where manual tuning is not recommended for time and cost reasons. Simulation and experimental test were carried out to investigate the performance of the proposed techniques. In the simulation, the system was tested under 100 kW model using Matlab/Simulink environment. In addition, the system was also investigated through a practical implementation of the control system using a Digital Signal Processor (DSP) and grid-connected three-phase inverter. This practical system was demonstrated a 300 W scaled-down prototype. As a result, the comparisons between experimental and simulation results show the behaviour and performance of the control to be accurately evaluated

    Nonlinear Systems

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    Open Mathematics is a challenging notion for theoretical modeling, technical analysis, and numerical simulation in physics and mathematics, as well as in many other fields, as highly correlated nonlinear phenomena, evolving over a large range of time scales and length scales, control the underlying systems and processes in their spatiotemporal evolution. Indeed, available data, be they physical, biological, or financial, and technologically complex systems and stochastic systems, such as mechanical or electronic devices, can be managed from the same conceptual approach, both analytically and through computer simulation, using effective nonlinear dynamics methods. The aim of this Special Issue is to highlight papers that show the dynamics, control, optimization and applications of nonlinear systems. This has recently become an increasingly popular subject, with impressive growth concerning applications in engineering, economics, biology, and medicine, and can be considered a veritable contribution to the literature. Original papers relating to the objective presented above are especially welcome subjects. Potential topics include, but are not limited to: Stability analysis of discrete and continuous dynamical systems; Nonlinear dynamics in biological complex systems; Stability and stabilization of stochastic systems; Mathematical models in statistics and probability; Synchronization of oscillators and chaotic systems; Optimization methods of complex systems; Reliability modeling and system optimization; Computation and control over networked systems
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