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"Control engineering computing"
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Get going with Amazon Echo and Alexa : in easy steps
\"The days of only being able to search for items on computers using text searches are long gone: voice search is rapidly becoming one of the most popular ways to find content on computing devices and the Web. One of the leaders in this area is the Amazon Echo, a high-quality speaker which uses Alexa ... to perform a range of tasks from playing music and making calls to smartphones, to answering questions and even controlling compatible devices in the home, such as turning on the heating ... [This book] leads you through the process of setting up the Amazon Echo, connecting it to your home wifi network and then controlling much of its functionality, so that you can start making the most of your digital personal assistant\"--ONIX annotation.
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Robust adaptive H-infinity based controller for islanded microgrid supplying non-linear and unbalanced loads
This study introduces a proposed control method for microgrids (MGs) in islanded (off-grid) mode. The proposed control method is developed by modifying the droop control method using H-infinity controller. In this control method, the droop control loop, current and voltage control loops are adjusted to respond to system load variation. The proposed method is an adaptive control one as it regulates the system voltage and frequency to their nominal values after system load variations. Also, it is a repetitive control method as it depends on the internal model principle that provides good performance for voltage and current error tracking. To prove the applicability and effectiveness of the proposed method, it is applied to a test system using MATLAB/Simulink under three different loading conditions. The results are compared with those of droop control and they prove the effectiveness of the proposed method in adjusting MGs under the off-grid mode of operation. Also, a system stability analysis is performed based on root locus and system step response. Robustness analysis is performed to prove the ability of the proposed controller to restore the system performance after the fault clearance.
Journal Article
Reinforcement learning for control of flexibility providers in a residential microgrid
The smart grid paradigm and the development of smart meters have led to the availability of large volumes of data. This data is expected to assist in power system planning/operation and the transition from passive to active electricity users. With recent advances in machine learning, this data can be used to learn system dynamics. This study explores two model-free reinforcement learning (RL) techniques – policy iteration (PI) and fitted Q-iteration (FQI) for scheduling the operation of flexibility providers – battery and heat pump in a residential microgrid. The proposed algorithms are data-driven and can be easily generalised to fit the control of any flexibility provider without requiring expert knowledge to build a detailed model of the flexibility provider and/or microgrid. The algorithms are tested in multi-agent collaborative and single-agent stochastic microgrid settings – with the uncertainty due to lack of knowledge on future electricity consumption patterns and photovoltaic production. Simulation results show that PI outperforms FQI with a 7.2% increase in photovoltaic self-consumption in the multi-agent setting and a 3.7% increase in the single-agent setting. Both RL algorithms perform better than a rule-based controller, and compete with a model-based optimal controller, and are thus, a valuable alternative to model- and rule-based controllers.
Journal Article
PotNet: Pothole detection for autonomous vehicle system using convolutional neural network
Advancement in vision‐based techniques has enabled the autonomous vehicle system (AVS) to understand the driving scene in depth. The capability of autonomous vehicle system to understand the scene, and detecting the specific object depends on the strong feature representation of such objects. However, pothole objects are difficult to identify due to their non‐uniform structure in challenging, and dynamic road environments. Existing approaches have shown limited performance for the precise detection of potholes. The study on the detection of potholes, and intelligent driving behaviour of autonomous vehicle system is little explored in existing articles. Hence, here, an improved prototype model, which is not only truly capable of detecting the potholes but also shows its intelligent driving behaviour when any pothole is detected, is proposed. The prototype is developed using a convolutional neural network with a vision camera to explore, and validates the potential, and autonomy of its driving behaviour in the prepared road environment. The experimental analysis of the proposed model on various performance measures have obtained accuracy, sensitivity, and F‐measure of 99.02%, 99.03%, and 98.33%, respectively, which are comparable with the available state‐of‐art techniques.
Journal Article
Modified fast discrete‐time PID formulas for obtaining double precision accuracy
Proportional integral derivative (PID) controllers are widely used across various industries. This paper presents a new modified PID controller based on integer origin raw data, which are equivalent to classic PID controller based on floating‐point actual values. These new formulas presented in new PID controller provide a mathematical approach to the ‘Classic PID Formula’, ‘Subtractor Formula’ and ‘Scaling Formula’, which form the basis of classic PID controller. The approach integrates these three formulas and separates them into integer and real value by applying the properties of associativity and commutativity. This method uses origin raw data as input to perform integer‐based computation and performs floating‐point operations once. This resulted in faster computation time and energy savings, while showing accuracy comparable to the existing double precision formulas. This paper presents a new modified proportional‐integral‐derivative (PID) controller based on integer origin raw data, which are equivalent to classic PID controller based on floating‐point actual values. This method uses origin raw data as input to perform integer‐based computation and performs floating‐point operations once. This resulted in faster computation time and energy savings, while showing accuracy comparable to the existing double precision formulas.
Journal Article
Optimal Performance Analysis for Networked System with Quantitative Control Input Over Feedback Channel
Networked control systems (NCS) have become an emerging and important research area given the rapidly developing network communication technology. Numerous studies in this area concentrate on the stability of control systems, while little has been done on its performance analysis, especially the index of an optimal control system. We fill this gap here by analysing the optimal tracking performance for an NCS that has quantitative control inputs over feedback channels. We design a dual‐degree‐of‐freedom controller along with an optimal control system, by applying the Youla parameterization method based on the time domain and coprime decompositions. The proposed methodology is assessed by numerical simulations. The system's tracking performance is found to be deteriorated by the unstable poles and non‐minimum phase zeros of the controlled object, as well as by quantization signal errors. These findings are beneficial for analysing and designing practical control systems. This paper focuses on the performance analysis of networked control systems, specifically addressing the optimal tracking performance with quantitative control inputs over feedback channels. A dual‐degree‐of‐freedom controller is designed using the Youla parameterization method, and the system's performance is assessed through numerical simulations. The findings highlight the impact of unstable poles, non‐minimum phase zeros, and quantization errors on tracking performance, offering insights for practical control system design.
Journal Article
Development of adaptive perturb and observe-fuzzy control maximum power point tracking for photovoltaic boost dc–dc converter
This study presents an adaptive perturb and observe (P&O)-fuzzy control maximum power point tracking (MPPT) for photovoltaic (PV) boost dc–dc converter. P&O is known as a very simple MPPT algorithm and used widely. Fuzzy logic is also simple to be developed and provides fast response. The proposed technique combines both of their advantages. It should improve MPPT performance especially with existing of noise. For evaluation and comparison analysis, conventional P&O and fuzzy logic control algorithms have been developed too. All the algorithms were simulated in MATLAB-Simulink, respectively, together with PV module of Kyocera KD210GH-2PU connected to PV boost dc–dc converter. For hardware implementation, the proposed adaptive P&O-fuzzy control MPPT was programmed in TMS320F28335 digital signal processing board. The other two conventional MPPT methods were also programmed for comparison purpose. Performance assessment covers overshoot, time response, maximum power ratio, oscillation and stability as described further in this study. From the results and analysis, the adaptive P&O-fuzzy control MPPT shows the best performance with fast time response, less overshoot and more stable operation. It has high maximum power ratio as compared to the other two conventional MPPT algorithms especially with existing of noise in the system at low irradiance.
Journal Article
Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers
This study elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system’s adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT. This research applies various optimization algorithms to determine the best parameters for controllers in solar energy systems. By conducting 45 simulations using a mix of algorithms and controllers under different environmental and load conditions, the study seeks to identify an optimal system configuration. The system is modelled using MATLAB/Simulink.
Journal Article
A review on manipulation skill acquisition through teleoperation‐based learning from demonstration
Manipulation skill learning and generalisation have gained increasing attention due to the wide applications of robot manipulators and the spurt of robot learning techniques. Especially, the learning from demonstration method has been exploited widely and successfully in the robotic community, and it is regarded as a promising direction to realise the manipulation skill learning and generalisation. In addition to the learning techniques, the immersive teleoperation enables the human to operate a remote robot with an intuitive interface and achieve the telepresence. Thus, it is a promising way to transfer manipulation skills from humans to robots by combining the learning methods and teleoperation, and adapting the learned skills to different tasks in new situations. This review, therefore, aims to provide an overview of immersive teleoperation for skill learning and generalisation to deal with complex manipulation tasks. To this end, the key technologies, for example, manipulation skill learning, multimodal interfacing for teleoperation and telerobotic control, are introduced. Then, an overview is given in terms of the most important applications of immersive teleoperation platform for robot skill learning. Finally, this survey discusses the remaining open challenges and promising research topics.
Journal Article
Intelligent control of a single‐link flexible manipulator using sliding modes and artificial neural networks
This letter presents a new intelligent control scheme for the accurate trajectory tracking of flexible link manipulators. The proposed approach is mainly based on a sliding mode controller for underactuated systems with an embedded artificial neural network to deal with modelling inaccuracies. The adopted neural network only needs a single input and one hidden layer, which drastically reduces the computational complexity of the control law and allows its implementation in low‐power microcontrollers. Online learning, rather than supervised offline training, is chosen to allow the weights of the neural network to be adjusted in real time during the tracking. Therefore, the resulting controller is able to cope with the underactuating issues and to adapt itself by learning from experience, which grants the capacity to deal with plant dynamics properly. The boundedness and convergence properties of the tracking error are proved by evoking Barbalat's lemma in a Lyapunov‐like stability analysis. Experimental results obtained with a small single‐link flexible manipulator show the efficacy of the proposed control scheme, even in the presence of a high level of uncertainty and noisy signals.
Journal Article